orgintelligence

http://mix.chimpfeedr.com/d1a50-orgintelligence orgintelligence 2018-01-15T22:25:00+00:00 http://www.chimpfeedr.com/ tag:blogger.com,1999:blog-1254315679163990153.post-5913341951429325263 Carillion Struck By Lightning Richard Veryard http://www.blogger.com/profile/04499123397533975655 noreply@blogger.com 2018-01-15T22:25:00+00:00

A letter to the editor of the Financial Times agrees.

"This situation has been caused, in part, by the unprofessional, fatalistic and blasé attitude to contract risk management of some senior executives in the UK construction industry."

By no means the first company brought low by delusion (I've talked some about Enron on this blog, as well as in my book on organizational intelligence), and probably not the last.

And given that Carillion was the beneficiary of some very large public sector contracts, we could also talk about delusion and poor risk management in government circles. As @econtratacion points out, "the public sector had had information pointing towards Carillion's increasingly dire financial situation for a while".

As it happens, the Home Secretary was at the London Stock Exchange today, talking to female executives about gender diversity at board level. So I thought I'd just check the gender make-up of the Carillion board. According to the Carillion website, there were two female executives and two female non-executive directors in a board of twelve.

In the future, Amber Rudd would like half of all directors to be female. An earlier Government-backed review had recommended that at least a third should be female by 2020.

Lombard: Sir Philip to review why so few female senior executives. He could use a beefed up UK's governance code to propel women to top jobs
— kate burgess ft (@katebur95633594) February 9, 2016

But compared to other large UK companies, the Carillion gender ratio wasn't too bad. "On paper, the directors looked well qualified", writes Kate Burgess in the Financial Times, noting that "the board ticked all the boxes in terms of good governance". But now even the Institute of Directors has expressed belated concerns about the effective governance at Carillion, and Burgess says the board fell into what she calls "a series of textbook traps".

So what kind of traps were these? The board paid large dividends to the shareholders and awarded large bonuses to themselves and other top executives, despite the fact that key performance targets were not met, and there was a massive hole in the pension fund. In other words, they looked after themselves first and the shareholders second, and to hell with pensioners and other stakeholders. Meanwhile, Larry Elliott notes that the directors of the company took steps to shield themselves from financial risk. These are not textbook traps, they are not errors of judgement, they are moral failings.

Of course we shouldn't rely solely on the moral integrity of company executives. If there is no regulation or regulator able to prevent a board behaving in this way, this points to a fundamental weakness in the financial system as a whole. As @RSAMatthew writes,

"There are many culprits in this tale. Lazy or ideologically blinkered ministers, incompetent public sector commissioners, cynical private sector providers signing 'suicide bids' on the assumption that they can renegotiate when things go wrong and, as always, a financial sector willing to arbitrage any profit regardless of consequences or ethics."

There is a strong case that diversity mitigates against groupthink - but as I've argued in my earlier posts, this needs to be real diversity not just symbolic or imaginary diversity (ticking boxes). And even if having more women or ethnic minorities on the board might possibly reduce errors of judgement, women as well as men can have moral failings. It's as if we imagined that Ivanka Trump was going to be a wise and restraining influence on her father, simply because of her gender.

As it happens, the remuneration director at Carillion was a woman. We may never know whether she was coerced or misled by her fellow directors or whether she participated enthusiastically in the gravy. But we cannot say that having a woman in that position is automatically going to be better than having a man. Women on boards may be a necessary step, but it is not a sufficient one.

Martin Bentham, Amber Rudd: 'It makes no sense to have more men than women in the boardroom' (Evening Standard, 15 January 2018)

Mark Bull, A lesson on risk from Carillion’s collapse (FT Letters to the Editor, 16 January 2018)

Kate Burgess, Carillion’s board: misguided or incompetent? (FT, 17 January 2018) HT @AidanWard3

Larry Elliott, Four lessons the Carillion crisis can teach business, government and us (Guardian, 17 January 2018)

Vanessa Fuhrmans, Companies With Diverse Executive Teams Posted Bigger Profit Margins, Study Shows (WSJ, 18 January 2018)

Simon Goodley, Carillion's 'highly inappropriate' pay packets criticised (Guardian, 15 January 2018)

Nils Pratley, Blame the deluded board members for Carillion's collapse (Guardian, 15 January 2018)

Albert Sánchez-Graells, Some thoughts on Carillion's liquidation and systemic risk management in public procurement (15 January 2018)

Rebecca Smith, Women should hold one third of senior executive jobs at FTSE 100 firms by 2020, says Sir Philip Hampton's review (City Am, 6 November 2016)

Matthew Taylor, Is Carillion the end for Public Private Partnerships? (RSA, 16th January 2018)

Related posts

Explaining Enron (January 2010)
The Purpose of Diversity (January 2010)
Organizational Intelligence and Gender (October 2010)
Delusion and Diversity (October 2012)
Intelligence and Governance (February 2013)
More on the Purpose of Diversity (December 2014)

Updated 25 January 2018]]>

A letter to the editor of the Financial Times agrees.

"This situation has been caused, in part, by the unprofessional, fatalistic and blasé attitude to contract risk management of some senior executives in the UK construction industry."

Lombard: Sir Philip to review why so few female senior executives. He could use a beefed up UK's governance code to propel women to top jobs
— kate burgess ft (@katebur95633594) February 9, 2016

"There are many culprits in this tale. Lazy or ideologically blinkered ministers, incompetent public sector commissioners, cynical private sector providers signing 'suicide bids' on the assumption that they can renegotiate when things go wrong and, as always, a financial sector willing to arbitrage any profit regardless of consequences or ethics."

As an example of helping people to use probabilistic output to guide business actions, Ross uses the example of smart recruitment.

But what’s the next step when a recruiter learns from an AI application that a job candidate has a 50% likelihood of being a good fit for a particular opening?

Let's unpack this. The AI application indicates that at this point in the process, given the information we currently have about the candidate, we have a low confidence in predicting the performance of this candidate on the job. Unless we just toss a coin and hope for the best, obviously the next step is to try and obtain more information and insight about the candidate.

But which information is most relevant? An AI application (guided by expert recruiters) should be able to identify the most efficient path to reaching the desired level of confidence. What are the main reasons for our uncertainty about this candidate, and what extra information would make the most difference?

Simplistic decision support assumes you only have one shot at making a decision. The expert system makes a prognostication, and then the human accepts or overrules its advice.

But in the real world, decision-making is often a more extended process. So the recruiter should be able to ask the AI application some follow-up questions. What if we bring the candidate in for another interview? What if we run some aptitude tests? How much difference would each of these options make to our confidence level?

When recruiting people for a given job, it is not just that the recruiters don't know enough about the candidate, they also may not have much detail about the requirements of the job. Exactly what challenges will the successful candidate face, and how will they interact with the rest of the team? So instead of shortlisting the candidates that score most highly on a given set of measures, it may be more helpful to shortlist candidates with a range of different strengths and weaknesses, as this will allow interviewers to creatively imagine how each will perform. So there are a lot more probabilistic calculations we could get the algorithms to perform, if we can feed enough historical data into the machine learning hopper.

Ross sees the true value of machine learning applications to be augmenting intelligence - helping people accomplish something. This means an effective collaboration between one or more people and one or more algorithms. Or what I call organizational intelligence.

Postscript (18 December 2017)

In his comment on Twitter, @AidanWard3 extends the analysis to multiple stakeholders.

Framed as risk reduction there are several different types of risk for different stakeholders in your scenario. An extended analysis still works. https://t.co/OvU03vH2eY
— Aidan Ward (@AidanWard3) December 14, 2017

This broader view brings some of the ethical issues into focus, including asymmetric information and algorithmic transparency.

Jeanne Ross, The Fundamental Flaw in AI Implementation (Sloan Management Review, 14 July 2017)]]>

As an example of helping people to use probabilistic output to guide business actions, Ross uses the example of smart recruitment.

But what’s the next step when a recruiter learns from an AI application that a job candidate has a 50% likelihood of being a good fit for a particular opening?

Postscript (18 December 2017)

In his comment on Twitter, @AidanWard3 extends the analysis to multiple stakeholders.

Framed as risk reduction there are several different types of risk for different stakeholders in your scenario. An extended analysis still works. https://t.co/OvU03vH2eY
— Aidan Ward (@AidanWard3) December 14, 2017

This broader view brings some of the ethical issues into focus, including asymmetric information and algorithmic transparency.

Jeanne Ross, The Fundamental Flaw in AI Implementation (Sloan Management Review, 14 July 2017)]]> tag:blogger.com,1999:blog-1254315679163990153.post-1508360380216991050 Creative Tension in the White House Richard Veryard http://www.blogger.com/profile/04499123397533975655 noreply@blogger.com 2017-04-09T12:30:00+00:00

"Roosevelt devised an administrative structure that would baffle any conventional student of public administration." (p53)

. @tonyjoyce Roosevelt set up "constructive rivalry ... structuring work so that clashes would be certain". Wilensky on #orgintelligence pic.twitter.com/MczcrYlypI
— Richard Veryard (@richardveryard) April 8, 2017

A horrible management technique designed to keep your subordinates so busy fighting with each other they can't challenge you for leadership https://t.co/WSOiHagBOx
— Jon H Ayre (@EnterprisingA) April 8, 2017

In contrast with FDR's approach, Wilensky notes some episodes where White House intelligence systems were not fit for purpose, including Korea (Truman) and the Bay of Pigs (Kennedy).

What about President Trump's approach? @tonyjoyce suggests that Trump is failing FDR's first construct - checking and balancing official intelligence vs unorthodox sources. However, Reuters (via the Guardian) quotes Republican strategist Charlie Black, who believes Trump’s White House reflects his traditional approach to running his business. “He’s always had a spokes-to-the-wheel management style,” said Black. “He wants people with differing views among the spokes.“

"Roosevelt devised an administrative structure that would baffle any conventional student of public administration." (p53)

. @tonyjoyce Roosevelt set up "constructive rivalry ... structuring work so that clashes would be certain". Wilensky on #orgintelligence pic.twitter.com/MczcrYlypI
— Richard Veryard (@richardveryard) April 8, 2017

A horrible management technique designed to keep your subordinates so busy fighting with each other they can't challenge you for leadership https://t.co/WSOiHagBOx
— Jon H Ayre (@EnterprisingA) April 8, 2017

Sources

Reuters, Kushner and Bannon agree to 'bury the hatchet' after White House peace talks (Guardian, 9 April 2017)

Related posts

Delusion and Diversity (October 2010)
The Art of the New Deal - Trump and Intelligence (February 2017)
Another Update on Deconfliction (April 2017)]]> tag:blogger.com,1999:blog-1254315679163990153.post-5068797603560462728 Linear Thought Richard Veryard http://www.blogger.com/profile/04499123397533975655 noreply@blogger.com 2017-04-02T11:05:00+00:00

According to the Guardian, US and UK intelligence officers were also anxious about Flynn's capacity for "linear thought".

I guess most people will interpret this concern as "insufficient capacity". When I searched for "linear thinking" on the internet, I found a number of pages that contrasted linear thinking with various forms of supposedly bad thinking, such as "fragmented thinking". I also found pages that tried to divide people into two camps - the scientific "leftbrain" types who think in straight lines, and the artistic "rightbrain" types who think in circles.

However, systems thinkers might be concerned about someone at that level having too much capacity for linear thought. (As one might be concerned about someone's capacity for gossip or deception.) In a previous post on this blog, I defended Flynn's former boss, Gen. Stanley McChrystal (labelled an "ill-fated iconoclast" by James Kitfield) against the claim that he was not a systems thinker. (This claim was based on a remark McChrystal had made about a subsequently notorious systems dynamics diagram. I argued that McChyrstal's remark could have been made either by someone who doesn't get systems thinking, or at the other extreme by someone who really gets systems thinking.)

The question here is about greater or lesser capacity for various kinds of thinking, because I'm trying to avoid the fallacy (identified by @cybersal) of categorizing people as this or that type of thinker. She rightly insists on seeing systems thinking not as an all-or-nothing affair but "as a lens to be applied in a particular type of situation".

By the way, Flynn himself has appeared on this blog before. In January 2010, using the lens of organizational intelligence, I reviewed his report on Fixing Intel. While I was sceptical about some of his recommendations, I can affirm that the report showed considerable capacity for systems (non-linear) thinking. Make of that what you will.

Links

Phillip Carter, What is Michael Flynn's game? (Slate, 31 March 2017)

Luke Harding et al, Michael Flynn: new evidence spy chiefs had concerns about Russian ties (Guardian, 31 March 2017)

James Kitfield, Flynn’s Last Interview: Iconoclast Departs DIA With A Warning (Breaking Defense, 7 August 2014)

Stanley McChrystal, The military case for sharing knowledge (TED2014, March 2014)

Stan McChrystal, Career Curveballs: No Longer A Soldier (22 April 2014)

Greg Miller and Adam Goldman, Head of Pentagon intelligence agency forced out, officials say (Washington Post, 30 April 2014)

Related Blogposts

A Job Description for Systems Thinking (November 2009)
Making Intelligence Relevant (January 2010)
Understanding Complexity (July 2010)]]>

(In FDR's time, of course, it was considered entirely normal for an administration to be staffed by a bunch of white men with similar education. And yet even they managed to achieve some diversity of perspective.)

Early reports of Donald Trump's administration suggest an unconscious echo of the FDR style. Or perhaps a much earlier pattern.

At the center of it all has been a cast of characters jockeying for Trump’s ear, creating a struggle for power that has manifested in a mix of chaos, leaks and uncertainty. The Trump White House already bears more resemblance to the court of a Renaissance king than to most prior administrations as favorites come and go, counselors quarrel over favor and policy decisions are often made by whim or without consultation. (Guardian, 4 Feb 2017)

But it is difficult to see this as "creative tension" resulting in an "accurate and rounded" view.

“Trump thinks he’s invincible,” says Hemmings, who doubts whether his advisors will ever question or criticise him. “Usually leaders choose the people around them to keep them in check, and Trump needs people to temper his hotheadedness and aggression. Instead, he’s picked advisors who worship him.” (Independent, 2 Feb 2017)

Wilensky's book also discusses the dangers of a doctrine of secrecy.

Secrets belong to a small assortment of individuals, and inevitably become hostage to private agendas. As Harold Wilensky wrote “The more secrecy, the smaller the intelligent audience, the less systematic the distribution and indexing of research, the greater the anonymity of authorship, and the more intolerant the attitude toward deviant views.” (Gladwell 2010)

And secrecy seems to a key element of the Trump-Bannon modus operandi.

“These executive orders were very rushed and drafted by a very tight-knit group of individuals who did not run it by the people who have to execute the policy. And because that’s the case, they probably didn’t think of or care about how this would be executed in the real world,” said another congressional source familiar with the situation. “No one was given a heads-up and no one had a chance to weigh in on it.” (Politico 30 Jan 2017)

But perhaps in reaction to the Bannonite doctrine of secrecy, there has been a flood of leaks from inside the administration. Chris Cillizza suggests two possible explanations - either these leaks are intended to influence Trump himself (because he doesn't take anything seriously unless he hears it from his favourite media channels) or conversely they are intended as a kind of whistle-blowing.

Marx thought that history repeated itself. (Alarmingly, Trump's Counselor Steve Bannon adheres to the same view.) So are we into tragedy or farce here?

Rachael Bade, Jake Sherman and Josh Dawsey, Hill staffers secretly worked on Trump's immigration order (Politico, 30 Jan 2017)

Chris Cillizza, The leaks coming out of the Trump White House cast the president as a clueless child (Washington Post, 26 January 2017), The leaks coming out of the Trump White House right now are totally bananas (Washington Post, 2 Feb 2017)

Malcolm Gladwell, Pandora's Briefcase (New Yorker, 10 May 2010)

Rachel Hosie, The deeper reason we should be worried Donald Trump hung up on Australia PM Malcolm Turnbull (Independent, 2 Feb 2017)

Linette Lopez, Steve Bannon's obsession with a dark theory of history should be worrisome (Business Insider, 2 Feb 2017) HT @BryanAppleyard

Carmen Medina, What is your Stupification Point? (6 May 2010)

Joseph Rago, History Repeats as Farce, Then as 2016 (Wall Street Journal, 4 November 2016) paywall

Sabrina Siddiqui and Ben Jacobs, Trump's courtiers bring chaotic and capricious style to White House (Guardian, 4 February 2017)

Related posts

Puzzles and Mysteries (January 2010)
Enemies of Intelligence (May 2010)
Delusion and Diversity (October 2012)]]>

At the center of it all has been a cast of characters jockeying for Trump’s ear, creating a struggle for power that has manifested in a mix of chaos, leaks and uncertainty. The Trump White House already bears more resemblance to the court of a Renaissance king than to most prior administrations as favorites come and go, counselors quarrel over favor and policy decisions are often made by whim or without consultation. (Guardian, 4 Feb 2017)

But it is difficult to see this as "creative tension" resulting in an "accurate and rounded" view.

“Trump thinks he’s invincible,” says Hemmings, who doubts whether his advisors will ever question or criticise him. “Usually leaders choose the people around them to keep them in check, and Trump needs people to temper his hotheadedness and aggression. Instead, he’s picked advisors who worship him.” (Independent, 2 Feb 2017)

Wilensky's book also discusses the dangers of a doctrine of secrecy.

Secrets belong to a small assortment of individuals, and inevitably become hostage to private agendas. As Harold Wilensky wrote “The more secrecy, the smaller the intelligent audience, the less systematic the distribution and indexing of research, the greater the anonymity of authorship, and the more intolerant the attitude toward deviant views.” (Gladwell 2010)

And secrecy seems to a key element of the Trump-Bannon modus operandi.

“These executive orders were very rushed and drafted by a very tight-knit group of individuals who did not run it by the people who have to execute the policy. And because that’s the case, they probably didn’t think of or care about how this would be executed in the real world,” said another congressional source familiar with the situation. “No one was given a heads-up and no one had a chance to weigh in on it.” (Politico 30 Jan 2017)

It seems that whatever the topic of the survey, middle managers and management consultants always expect significant change within the next three years, because this is what justifies their existence.

In this case, the topic is corporate learning, which McKinsey recommends should be done "at the speed of business", whatever that means. (I am not a fan of the "at the speed of" cliche.)

But what kind of change is McKinsey talking about here? The article concentrates on digital delivery of learning material - disseminating existing "best practice" knowledge to a broader base. It doesn't really say anything about organizational learning, let alone a more radical transformation of the nature of learning in organizations. I have long argued that the real disruption is not in replacing classrooms with cheaper and faster equivalents, useful though that might be, but in digital organizational intelligence -- using increasing quantities of data to develop and test new hypotheses about customer behaviour, market opportunities, environmental constraints, and so on -- developing not "best practice" but "next practice".

Richard Benson-Armer, Arne Gast, and Nick van Dam, Learning at the speed of business (McKinsey Quarterly, May 2016). HT @annherrmann

Chris Argyris and Donald Schön, Organizational Learning: A Theory of Action Perspective. Reading, MA, Addison-Wesley, 1978.

]]>

]]> tag:blogger.com,1999:blog-6106782.post-5467304156940555400 Single Point of Failure (Airlines) Richard Veryard http://www.blogger.com/profile/04499123397533975655 noreply@blogger.com 2016-08-08T12:36:00+00:00

In July 2016, Southwest Airlines suffered a major disruption to service, which lasted several days. It blamed the failure on "lingering disruptions following performance issues across multiple technology systems", apparently triggered by a power outage.

Click below for the latest update on our system and operation: https://t.co/bqV1qwahmz
— Southwest Airlines (@SouthwestAir) July 21, 2016

In August 2016 it was Delta's turn.

New statement from Delta - power outage caused IT failure pic.twitter.com/trkQbpym05
— Rory Cellan-Jones (@ruskin147) August 8, 2016

@ruskin147 A power outage *triggered* this issue, but poor planning and no HA *caused* it. Why can Netflix get this right but airlines cant?
— Richard Price (@RichardPrice) August 8, 2016

I am no computer expert but it seems like a whole system crashing (3 separate airlines) points to bad design (single point of failure)? 3/
— Dan DePodwin (@WxDepo) August 8, 2016

Then there were major problems at British Airways (Sept 2016) and United (Oct 2016).

@razankhabour We apologize to our customers for the delay and we appreciate their patience as our IT teams work to resolve this issue.
— British Airways (@British_Airways) September 6, 2016

We're aware of an issue with our system and are working to resolve it. We'll update as we learn more. We apologize for the inconvenience.
— United (@united) October 14, 2016

So every @united flight is grounded because they can’t run a decent IT shop. What year is this??
— Randy Bias (@randybias) October 14, 2016

The concept of "single point of failure" is widely known and understood. And the airline industry is rightly obsessed by safety. They wouldn't fly a plane without backup power for all systems. So what idiot runs a whole company without backup power?

We might speculate what degree of complacency or technical debt can account for this pattern of adverse incidents. I haven't worked with any of these organizations myself. However, my guess is that some people within the organization were aware of the vulnerability, but this awareness didn't somehow didn't penetrate the management hierarchy. (In terms of orgintelligence, a short-sighted board of directors becomes the single point of failure!) I'm also guessing it's not quite as simple and straightforward as the press reports and public statements imply, but that's no excuse. Management is paid (among other things) to manage complexity. (Hopefully with the help of system architects.)

If you are the boss of one of the many airlines not mentioned in this post, you might want to schedule a conversation with a system architect. Just a suggestion.

American Airlines Gradually Restores Service After Yesterday's Power Outage (PR Newswire, 15 August 2003)

British Airways computer outage causes flight delays (Guardian, 6 Sept 2016)

Delta: ‘Large-scale cancellations’ after crippling power outage (CNN Wire, 8 August 2016)

Gatwick Airport Christmas Eve chaos a 'wake-up call' (BBC News, 11 April 2014)

Simon Calder, Dozens of flights worldwide delayed by computer systems meltdown (Independent, 14 October 2016)

Jon Cox, Ask the Captain: Do vital functions on planes have backup power? (USA Today, 6 May 2013)

Jad Mouawad, American Airlines Resumes Flights After a Computer Problem (New York Times, 16 April 2013)

Marni Pyke, Southwest Airlines apologizes for delays as it rebounds from outage (Daily Herald, 20 July 2016)

Alexandra Zaslow, Outdated Technology Likely Culprit in Southwest Airlines Outage (NBC News, Oct 12 2015)

Related post Single Point of Failure (Comms) (September 2016), The Cruel World of Paper (September 2016)

Updated 14 October 2016. ]]>

Click below for the latest update on our system and operation: https://t.co/bqV1qwahmz
— Southwest Airlines (@SouthwestAir) July 21, 2016

In August 2016 it was Delta's turn.

New statement from Delta - power outage caused IT failure pic.twitter.com/trkQbpym05
— Rory Cellan-Jones (@ruskin147) August 8, 2016

@ruskin147 A power outage *triggered* this issue, but poor planning and no HA *caused* it. Why can Netflix get this right but airlines cant?
— Richard Price (@RichardPrice) August 8, 2016

I am no computer expert but it seems like a whole system crashing (3 separate airlines) points to bad design (single point of failure)? 3/
— Dan DePodwin (@WxDepo) August 8, 2016

Then there were major problems at British Airways (Sept 2016) and United (Oct 2016).

@razankhabour We apologize to our customers for the delay and we appreciate their patience as our IT teams work to resolve this issue.
— British Airways (@British_Airways) September 6, 2016

We're aware of an issue with our system and are working to resolve it. We'll update as we learn more. We apologize for the inconvenience.
— United (@united) October 14, 2016

So every @united flight is grounded because they can’t run a decent IT shop. What year is this??
— Randy Bias (@randybias) October 14, 2016

Writing in The Atlantic during the election campaign, @jon_rauch expressed strong opposition to the conventional view of party leadership.

The very term party leaders has become an anachronism. ... There no longer is any such thing as a party leader. There are only individual actors, pursuing their own political interests and ideological missions willy-nilly, like excited gas molecules in an overheated balloon. ...

This is not only a problem of leadership and individual agency, but also a question of the nature of the political party as a viable system with collective agency and intelligence. Rauch continues

The political parties no longer have either intelligible boundaries or enforceable norms.

The relationship between the politician and the party has always been problematic - consider Winston Churchill who changed party allegiance twice before becoming party leader. But the root cause of this problem is unclear.

Political parties are what things look like when you put politicians in charge.
— David Allen Green (@DavidAllenGreen) August 3, 2016

@DavidAllenGreen or they are machines for turning people into politicians
— Sean Owen-Moylan (@SeanOwenMoylan) August 3, 2016

Writing in The Atlantic during the election campaign, @jon_rauch expressed strong opposition to the conventional view of party leadership.

The very term party leaders has become an anachronism. ... There no longer is any such thing as a party leader. There are only individual actors, pursuing their own political interests and ideological missions willy-nilly, like excited gas molecules in an overheated balloon. ...

This is not only a problem of leadership and individual agency, but also a question of the nature of the political party as a viable system with collective agency and intelligence. Rauch continues

The political parties no longer have either intelligible boundaries or enforceable norms.

Political parties are what things look like when you put politicians in charge.
— David Allen Green (@DavidAllenGreen) August 3, 2016

@DavidAllenGreen or they are machines for turning people into politicians
— Sean Owen-Moylan (@SeanOwenMoylan) August 3, 2016

Jonathan Rauch, How American Politics Went Insane (The Atlantic, July 2016)

Wikipedia: Party Leader (retrieved 4 Feb 2017)

Related posts

Political parties and organizational intelligence 1 (May 2012)
Political parties and organizational intelligence 2 (June 2015)]]> tag:blogger.com,1999:blog-1254315679163990153.post-6904123319512460013 Political parties and organizational intelligence 2 Richard Veryard http://www.blogger.com/profile/04499123397533975655 noreply@blogger.com 2015-06-04T21:34:00+00:00

#orgintelligence #politics @rafaelbehr contrasts the behaviour of the Conservative and Labour parties.

Before the 2015 election, the Labour party practised collective denial ("misplaced confidence", "kidded themselves"), believing that "organization could compensate for uninspiring leadership". Following the election, "a danger now is oversteering the other way".

Denial and oscillation are two of the principal symptoms I have identified of Organizational Stupidity (May 2010).

Contrast this with the Conservative willingness to invest in 'blue collar conservatism'. Behr attributes this initiative to George Osborne, one of whose political gifts "is the self-knowledge to identify gaps in his own experience and to plug them with astute appointments". Cameron, he suggests, is much less intellectually curious than Osborne. And yet it is Cameron who carries through Osborne's plan to appoint Robert Halfon in order to recalibrate the Conservative's relationship with the working classes.

What reveals itself here is a form of intelligence and leadership that is collective rather than individual, a form of collaboration and teamwork that has not been strongly evident in the Labour Party recently.

Steve Richards goes further ...

"During Cameron’s leadership the Conservatives have become more alive as a party, impressively animated by ideas and debate. Cameron appears to be an orthodox Tory but likes having daring thinkers around him, even if they do not last that long. ... In recent years Conservative party conferences have been far livelier than Labour ones, which have been deadened by fearful control freakery."

... and insists that "the next Labour leader must not be frightened by internal debate".

One of the essential duties of leadership in any organization must be to boost the collective intelligence of the organization. Not just debate, but debate linked with action.

Patrick Wintour reports that there was plenty of (apparently) healthy argument in Labour's inner circle.

"Meetings were quite discursive, because there were a large number of views in the room. ... [Miliband] enjoyed that. He used the disagreement as a means to get his own way. It is a very interesting case study in power, in that he would not be described typically as a strong leader, but very consensual. The caricature of him is as weak, but internally he had great control."

But that's not enough.

"The team that Miliband had assembled around him consisted of highly intelligent individuals, but the whole was less than the sum of its parts – it was, according to many of those advisers, like a court in which opposing voices cancelled one another out."

Furthermore, an important requirement for organizational intelligence is that it is just not enough to have an inner circle of bright and well-educated 'spads', and to appoint either the cleverest or the most photogenic of them as "leader". Perhaps the Labour inner circle deeply understood the political situation facing the party, but they neglected to communicate (forgot to mention) this insight to others. The vanguard is not the party. Any party that aspires to be a movement rather than a machine must distribute its intelligence to the grass roots, and thence to the population as a whole.

Exercise for the reader: count the ironies in the above paragraph.

Finally, intelligent organizations have a flexible approach to learning from the past. @freedland argues that Miliband was single-minded about the future, and refused to tackle the prevailing narrative about the Labour government's role in the 2008 economic crisis.

"The management gurus and political consultants may tell us always to face forward, never to look over our shoulder, to focus only on the future. But sometimes it cannot be done. In politics as in life, the past lingers."

Sources:

Rafael Behr, The age of machine politics is over. But still it thrives in the Labour party (Guardian 4 June 2015)

Jonathan Freedland, ‘Moving on’: the mantra that traps Labour in the past (Guardian 5 June 2015)

Tim Glencross, Attack of the clones: how spads took over British politics (Guardian 19 April 2015)

Brian Matthews, The Labour Party and the Need for Change: values, education and emotional literacy/intelligence (Forum, Volume 54 Number 1, 2012)

Steve Richards, Labour’s next leader should look to David Cameron, not Tony Blair (Guardian 1 June 2015)

Patrick Wintour, The undoing of Ed Miliband – and how Labour lost the election (Guardian 3 June 2015)

Chris York, The Rise Of The Spad: How Many Ministers Or Shadow Ministers Have Had Proper Jobs? (Huffington Post, 13 November 2013)

Related Posts:

Symptoms of Organizational Stupidity (May 2010)
Political Parties and Organizational Intelligence (May 2012)
Dark Politics (May 2015)

Updated 6 June 2015]]>

"During Cameron’s leadership the Conservatives have become more alive as a party, impressively animated by ideas and debate. Cameron appears to be an orthodox Tory but likes having daring thinkers around him, even if they do not last that long. ... In recent years Conservative party conferences have been far livelier than Labour ones, which have been deadened by fearful control freakery."

"Meetings were quite discursive, because there were a large number of views in the room. ... [Miliband] enjoyed that. He used the disagreement as a means to get his own way. It is a very interesting case study in power, in that he would not be described typically as a strong leader, but very consensual. The caricature of him is as weak, but internally he had great control."

But that's not enough.

"The team that Miliband had assembled around him consisted of highly intelligent individuals, but the whole was less than the sum of its parts – it was, according to many of those advisers, like a court in which opposing voices cancelled one another out."

"The management gurus and political consultants may tell us always to face forward, never to look over our shoulder, to focus only on the future. But sometimes it cannot be done. In politics as in life, the past lingers."

Related Posts:

Symptoms of Organizational Stupidity (May 2010)
Political Parties and Organizational Intelligence (May 2012)
Dark Politics (May 2015)

Updated 6 June 2015]]> tag:blogger.com,1999:blog-7415430.post-2914714708789565831 Misunderstanding CRM and Big Data Richard Veryard https://plus.google.com/107891026040206160712 noreply@blogger.com 2014-11-27T22:53:00+00:00

IBA Group.

Peter cites an Ovum survey showing that Customer Satisfaction is now the number one concern of management, and argues for what Ovum calls Intelligent CRM. (CA announced something under this label back in October 2000. Other products are available.)

Mark says that CRM and Big Data are widely misunderstood, which is certainly true. My own opinion is the first misunderstanding is to think CRM is about managing THE relationship with THE customer, and I completely agree with Clayton Christensen (via Sloan) that this isn't enough. What we really need to focus on is the job the customers are trying to get done when they use your product or service.

Who is good at CRM? Peter cites an example of a professor of marketing who got a personalized service at a certain chain of hotels and has been talking about it ever since. (That's a pretty good coup for the hotel, if we take the story at face value.) Mark cites the video game market, where both the console manufacturers and the large game publishers are able to collect and analyse huge quantities of consumer behaviour.

Is CRM with Big Data merely a new way of taking advantage of customers? Although most people seem oblivious to the privacy and trust risks, the Wall Street Journal this week suggested that the consumer is becoming more savvy and less susceptible to exploitative loyalty schemes and promotions. This might help to explain why Tesco, once a master of the science of retail, now seems to be faltering.

If there is a sustainable business model based on CRM and Big Data, it must surely involve using these technologies to engage intelligently, authentically and ethically with customers, rather than imagining that these technologies can provide a quick fix for stupid organizations to take advantage of compliant customers.

Related Blogs

Customer Orientation (May 2009)

The Science of Retail (April 2012)

Other Articles

Martha Mangelsdorf, Understanding your customer isn't enough (Sloan Review May 2009)

Shelly Banjo and Sara Germano, The End of the Impulse Shopper (Wall Street Journal 25 November 2014)

Intelligent CRM

AI-CRM "An intelligent CRM system with atuo-learning-tunning engine (sic), Aichain offers the most widely used open source business intelligence software in the world." Last updated March 2013

CA rolling out customer relationship management software (ComputerWorld October 2000)

IBA Group "maintains its focus on IT outsourcing that has become a strategy for many organizations seeking to improve their business processes"

]]>

QTWTAIN @lucykellaway asks if those workers who stay for years with the same companies (are) unambitious and mediocre, or does the corporate grind make them so?

Her article addresses the perception that people seemed to get dimmer the higher they went in the organization. If this perception is correct then there are several possible explanations

increased quality of intake
higher turnover of more talented and ambitious people (who may expect to get better opportunities elsewhere)
dulling effect of corporate life

If it is true that organizations systematically lose the best people and/or turn good people into mediocrities, then according to Stafford Beer's POSIWID principle, this is effect reveals the defacto purpose of the organization.

But perhaps the perception that people get dimmer as they get more experienced is wrong. Perhaps they simply display different forms of intelligence that are associated with collective excellence rather than individual brilliance. Clearly it would be natural for organizations to promote those kinds of intelligence that produce good corporate outcomes. However, it is likely that not everyone (especially fresh graduates) would see or appreciate these forms of intelligence.

According to the conventional metaphor, the corporate grind turns people into round pegs. When I was young, I used to think there was some kind of virtue in being a square peg: now I'm not so sure. However, there is undoubtedly a problem for any organization that cannot accommodate a few brilliant square pegs.

increased quality of intake
higher turnover of more talented and ambitious people (who may expect to get better opportunities elsewhere)
dulling effect of corporate life

Lucy Kellaway, Why firms don't want you to be brilliant at your job (BBC Magazine 20 October 2014)

Twitter Update from @lucykellaway Today was my pearl anniversary at FT. Is 30 years' service a triumph or disgrace? (16 Apr 2015). My answer: Obviously a triumph for the FT to keep such a journalistic pearl.]]> tag:blogger.com,1999:blog-1254315679163990153.post-4987095738639194752 Working for the Machine Richard Veryard https://plus.google.com/107891026040206160712 noreply@blogger.com 2014-11-06T00:15:00+00:00

orgintelligence The recent appointment of an algorithm to a Board of Directors raises the spectre of science fiction becoming fact. Although many commentators regarded the appointment as a publicity stunt, there has always been an undercurrent of fear about machine intelligence. Even the BBC (following Betteridge's Law of Headlines) succumbed to the alarmist headline Could a big data-crunching machine be your boss one day?

There are several useful ways that an algorithm might contribute to the collective intelligence of a Board of Directors. One is to provide an automated judgement on some topic, which can be put into the pot together with a number of human judgements. This is what seems to be planned by the company Deep Knowledge Ventures, whose Board of Directors is faced with a series of important investment decisions. Although each decision is unique, there are some basic similarities in the decision process that may be amenable to automation and machine learning.

Another possible contribution is to evaluate other board members. According to the BBC article, IBM Watson could be programmed to analyse the contributions made by each board member for usefulness and accuracy. There are several ways such a feedback loop could enhance the collective intelligence of the Board.

Retrain individuals to improve their contributions in specific contexts.
Identify and eliminate individuals whose contribution is weak.
Identify and eliminate individuals whose contribution is similar to other members. In other words, promote greater diversity.
Enable trial membership of individuals from a wider range of backgrounds, to see whether they can make a valuable contribution.

Organizational Intelligence is about an effective combination of human/social intelligence and machine intelligence. Remember this when people try to develop an either-us-or-them narrative.

#QTWTAIN

Jamie Bartlett, Will Artificial Intelligence put my job at risk? (Spectator 6 June 2014)

Adrian Chen, Can an Algorithm Solve Twitter’s Credibility Problem? (New Yorker 5 May 2014)

John Rentoul, Will Artificial Intelligence put my job at risk? (Independent 6 June 2014)

Richard Veryard, Does Cameron's Dashboard App Improve the OrgIntelligence of Government? (23 January 2013)

Matthew Wall, Could a big data-crunching machine be your boss one day? (BBC News 9 October 2014)

Retrain individuals to improve their contributions in specific contexts.
Identify and eliminate individuals whose contribution is weak.
Identify and eliminate individuals whose contribution is similar to other members. In other words, promote greater diversity.
Enable trial membership of individuals from a wider range of backgrounds, to see whether they can make a valuable contribution.

Organizational Intelligence is about an effective combination of human/social intelligence and machine intelligence. Remember this when people try to develop an either-us-or-them narrative.

Other Sources

Algorithm appointed board director (BBC News 16 May 2014)

Bud Caddell, Your boss might be better as an algorithm (Quartz, 16 November 2017)

Related Posts

Intelligence and Governance (Feb 2013)

Link added 11 December 2017]]> tag:blogger.com,1999:blog-1254315679163990153.post-6416613590453773730 National Decision Model Richard Veryard https://plus.google.com/107891026040206160712 noreply@blogger.com 2014-05-18T14:34:00+00:00

antlerboy asks does anyone know theoretical underpinning of the (rather good) police decision-making model?

The National Decision Model (NDM) is a risk assessment framework, or decision making process, that is used by police forces across the UK. It replaces the former Conflict Management Model. Some sources refer to it as the National Decision-Making Model.

Looking around the Internet, I have found two kinds of description of the model - top-down and bottom-up.

The top-down (abstract) description was published by the Association of Chief Police Officers (ACPO) sometime in 2012, and has been replicated in various items of police training material including a page on the College of Policing website. It is fairly abstract, and provides five different stages that officers can follow when making any type of decision - not just conflict management.

Some early responses from the police force regarded the NDM as an ideal model, only weakly connected to the practical reality of decision-making on the ground. See for example The NDM and decision making – what’s the reality? (Inspector Juliet Bravo, April 2012).

In contrast, the bottom-up (context-specific) description emerges when serving police officers discuss using the NDM. According to Mr Google, this discussion tends to focus on one decision in particular - to Taser or not to Taser.

"For me the Taser is a very important link in the National Decision Making Model . It bridges that gap between the baton and the normal firearm that has an almost certain risk of death when used." (The Peel Blog, July 2012). See also Use of Force - Decision Making (Police Geek, July 2012).

ACPO itself adopts this context-specific perspective in its Questions and Answers on Taser (February 2012, updated July 2013), where it is stated that Taser may be deployed and used as one of a number of tactical options only after application of the National Decision Model (NDM).

Of course, the fact that Taser-related decisions have a high Google ranking doesn't imply that these decisions represent the most active use of the National Decision Model. The most we can infer from this is that these are the decisions that police and others are most interested in discussing.

(Argyris and Schön introduced the distinction between Espoused Theory and Theory-In-Use. Perhaps we need a third category to refer to what people imagine to be the central or canonical examples of the theory. We might call it Theory-in-View or Theory-in-Gaze.)

In a conflict situation, a police officer often has to decide how much force to use. The officer needs to have a range of tools at his disposal and the ability to select the appropriate tool - in policing, this is known as a use-of-force continuum. More generally, it is an application of the principle of Requisite Variety.

In a particular conflict situation, the police can only use the tools they have at their disposal. The decision to use a Taser can only be taken if the police have the Taser and the training to use it properly. In which case the operational decision must follow the NDM.

More strategic decisions operate on a longer timescale - whether to equip police with certain equipment and training, what rules of engagement to apply, and so on. A truly abstract decision-making model would provide guidance for these strategic decisions as well as the operational decisions.

And that's exactly what the top-down description of NDM asserts. "It can be applied to spontaneous incidents or planned operations by an individual or team of people, and to both operational and non-operational situations."

Senior police officers have described the use of the NDM for non-conflict situations. For example, Adrian Lee (Chief Constable of Northants) gave a presentation on the Implications of NDM for Roads Policing (January 2012).

The NDM has also been adapted for use in other organizations. For example, Paul Macfarlane (ex Strathclyde Police) has used the NDM to produced a model aimed at Business Continuity and Risk Management. which he calls Defensive Decision-Making.

How does the NDM relate to other decision-making models? According to Adrian Lee's presentation, the NDM is based on three earlier models:

The Conflict Management Model (CMM). For a discussion from 2011, see Police Oracle.
The SARA model (Scan, Analyze, Respond, Assess) - which appears to be similar to the OODA loop.
Something called the PLANE model. (I tried Googling this, and I just got lots of Lego kits. If anyone has a link, please send.)

There is considerable discussion in the USA about the relevance of the OODA loop to policing, and this again focuses on conflict management situations (the "Active Shooter"). There are two important differences between combat (the canonical use of OODA) and conflict management. Firstly, the preferred outcome is not to kill the offender but to disarm him (either physically or psychologically). This means that you sometimes need to give the offender time to calm down, orienting himself into making the right decision.

And the cop needs to stay calm. George “Doc” Thompson, who taught US police a de-escalation technique known as Verbal Judo, once said "We know that the most deadly weapon we carry is not the .45 or the 9mm, it is in fact the cop’s tongue ... A single sentence fired off at the wrong person at the wrong time can get you fired, it can get you sued, it can get you killed."

So it's not just about having a faster OODA loop than the other guy (although clearly some American cops think this is important). And secondly, there is a lot of talk about situation awareness and anticipation. For example, Dr. Mike Asken, who is a State Police psychologist, has developed a model called AAADA (Anticipating, Alerting, Assessing, Deciding and Acting). There is also a Cognitive OODA model I need to look into.

However, I interpret @antlerboy's request for theoretical underpinning as not just a historical question (what theories of decision-making were the creators of NDM consciously following) but a methodological question (what theories of decision-making would be relevant to NDM and any other decision models). But this post is already long enough, and the sun is shining outside, so I shall return to this topic another day.

Sources

Michael J. Asken, From OODA to AAADA ― A cycle for surviving violent police encounters (Dec 2010)

Erik P. Blasch et al, User Information Fusion Decision-Making Analysis with the C-OODA Model (Jan 2011)

Tom Dart, ‘Verbal judo’: the police tactic that teaches cops to talk before they shoot (Guardian 21 July 2016)

Adrian Lee, Implications of NDM for Roads Policing (January 2012).

Steve Papenfuhs, The OODA loop, reaction time, and decision making (PoliceOne, 23 February 2012)

National Decision Model (ACPO, 2012?)

National Decision Model (College of Policing, 2013)

SARA model (Center for Problem-Oriented Policing)

Related Posts
National Decision Model and Lessons Learned (Feb 2017)

Updated 28 February 2017
]]>

How does the NDM relate to other decision-making models? According to Adrian Lee's presentation, the NDM is based on three earlier models:

The Conflict Management Model (CMM). For a discussion from 2011, see Police Oracle.
The SARA model (Scan, Analyze, Respond, Assess) - which appears to be similar to the OODA loop.
Something called the PLANE model. (I tried Googling this, and I just got lots of Lego kits. If anyone has a link, please send.)

bengoldacre has produced a report for the UK Department for Education, suggesting some lessons that education can learn from medicine, and calling for a coherent “information architecture” that supports evidence based practice. Dr Goldacre notes that in the highest performing education systems, such as Singapore, “it is almost impossible to rise up the career ladder of teaching, without also doing some work on research in education.”

Here are some of his key recommendations. Clearly these recommendations would be relevant to many other corporate environments, especially those where there is strong demand for innovation, performance and value-for-money.

a simple infrastructure that supports evidence-based practice
teachers should be empowered to participate in research
the results of research should be disseminated more efficiently
resources on research should be available to teachers, enabling them to be critical and thoughtful consumers of evidence
barriers between teachers and researchers should be removed
teachers should be driving the research agenda, by identifying questions that need to be answered.

Clearly it is not enough merely to create an information architecture or knowledge infrastructure. The challenge is to make sure they are aligned with an inquiring culture.

to be continued ...

Ben Goldacre, Teachers! What would evidence based practice look like? (Bad Science, March 2013)]]>

a simple infrastructure that supports evidence-based practice
teachers should be empowered to participate in research
the results of research should be disseminated more efficiently
resources on research should be available to teachers, enabling them to be critical and thoughtful consumers of evidence
barriers between teachers and researchers should be removed
teachers should be driving the research agenda, by identifying questions that need to be answered.

Clearly it is not enough merely to create an information architecture or knowledge infrastructure. The challenge is to make sure they are aligned with an inquiring culture.

to be continued ...

Ben Goldacre, Teachers! What would evidence based practice look like? (Bad Science, March 2013)]]> tag:blogger.com,1999:blog-1254315679163990153.post-4913009286183448365 Intelligence and Governance Richard Veryard http://www.blogger.com/profile/04499123397533975655 noreply@blogger.com 2013-02-28T12:31:00+00:00

TheKingsFund asks What Makes a Board Effective? (Feb 2013). She's looking specifically at the role of the Board in the National Health Service, but there is much that can be generalized to other contexts. She asks some key questions for any given board.

Are its members individually effective and do they communicate effectively – for example, do they challenge themselves and others?
Do they use energetic presentations and have insightful conversations?
Do they support their colleagues and have good decision-making skills?

In this post, I want to develop this line of thinking further by exploring what the concept of organizational intelligence implies for boards.

1. Boards need to know what is going on.

Multiple and diverse sources of information - both quantitative and qualitative
Understanding how information is filtered, and a willingness to view unfiltered information as necessary.
Ability to identify areas of concern, and initiate detailed investigation

2. Boards need to make sense of what is going on.

Ability to see things from different perspectives - patient quality, professional excellence, financial accountability, social accountability.
Ability to see the detail as well as the big picture.
Courage to investigate and explore any discrepancies, and not to be satisfied with easy denial.

3. Boards need to ensure that all decisions, policies and procedures are guided by both vision and reality. This includes decisions taken by the board itself, as well as decisions taken at all levels of management.

Decisions and actions are informed by values and priorities, and reinforce these values. (People both inside and outside the organization will infer your true values not from your words but from your actions.)
Decisions and actions are guided by evidence wherever possible. Ongoing decisions and policies are open to revision according to the outcomes they yield.
Decision-making by consent (Robertson)

4. Boards need to encourage learning.

Effective feedback loops are established, monitoring outcomes and revising decisions and policies where necessary.
Courage to experiment. Ability to tolerate temporary reduction in productivity during problem-solving and learning curve. Supporting people and teams when they are out of their comfort zone.
Willingness to learn lessons from anywhere, not just a narrow set of approved exemplars.

5. Boards need to encourage knowledge-sharing

All kinds of experience and expertise may be relevant
Overcoming the "silos" and cultural differences
The collective memory should be strong and coherent enough to support the organization's values, but not so strong as to inhibit change.

6. Boards work as a team, and collaborate with other teams

Effective communication and collaboration within the board - don't expect each board member to do everything.
Effective communication and collaboration with other groups and organizations.
Circle Organization (Robertson)

Note: The six points I've discussed here correspond to the six core capabilities of organizational intelligence, as described in my Organizational Intelligence eBook and my Organizational Intelligence workshop.

See also

Brian Robertson, The Sociocratic Method. A Dutch model of corporate governance harnesses self-organization to provide agility and a voice to all participants (Strategy+Business Aug 2006)

Steve Waddell, Wicked Problems, Governance as Learning Systems (Feb 2013)

Updated 1 March 2013]]>

Are its members individually effective and do they communicate effectively – for example, do they challenge themselves and others?
Do they use energetic presentations and have insightful conversations?
Do they support their colleagues and have good decision-making skills?

In this post, I want to develop this line of thinking further by exploring what the concept of organizational intelligence implies for boards.

1. Boards need to know what is going on.

Multiple and diverse sources of information - both quantitative and qualitative
Understanding how information is filtered, and a willingness to view unfiltered information as necessary.
Ability to identify areas of concern, and initiate detailed investigation

2. Boards need to make sense of what is going on.

Ability to see things from different perspectives - patient quality, professional excellence, financial accountability, social accountability.
Ability to see the detail as well as the big picture.
Courage to investigate and explore any discrepancies, and not to be satisfied with easy denial.

Decisions and actions are informed by values and priorities, and reinforce these values. (People both inside and outside the organization will infer your true values not from your words but from your actions.)
Decisions and actions are guided by evidence wherever possible. Ongoing decisions and policies are open to revision according to the outcomes they yield.
Decision-making by consent (Robertson)

4. Boards need to encourage learning.

Effective feedback loops are established, monitoring outcomes and revising decisions and policies where necessary.
Courage to experiment. Ability to tolerate temporary reduction in productivity during problem-solving and learning curve. Supporting people and teams when they are out of their comfort zone.
Willingness to learn lessons from anywhere, not just a narrow set of approved exemplars.

5. Boards need to encourage knowledge-sharing

All kinds of experience and expertise may be relevant
Overcoming the "silos" and cultural differences
The collective memory should be strong and coherent enough to support the organization's values, but not so strong as to inhibit change.

6. Boards work as a team, and collaborate with other teams

Effective communication and collaboration within the board - don't expect each board member to do everything.
Effective communication and collaboration with other groups and organizations.
Circle Organization (Robertson)

"The most pressing problem of the organization is complexity. Complexity is the primary responsibility of the enterprise architect." (Roger Sessions, Controlling Complexity in Enterprise Architecture pdf June 2007)

"Enterprises are instances of complex adaptive systems having many interacting subcomponents whose interactions yield complex behaviors. Enterprise Architecture is a way of understanding and managing such complexity." (Beryl Bellman Managing Organizational Complexity pdf FEAC Oct 2009)

"The most pressing problem of the organization is complexity. Complexity is the primary responsibility of the enterprise architect." (Roger Sessions, Controlling Complexity in Enterprise Architecture pdf June 2007)

"Enterprises are instances of complex adaptive systems having many interacting subcomponents whose interactions yield complex behaviors. Enterprise Architecture is a way of understanding and managing such complexity." (Beryl Bellman Managing Organizational Complexity pdf FEAC Oct 2009)

Indeed, I'm sure I've said things like this myself. But if complexity is a problem, whose problem is it? I am not seeing a huge rush of businessmen hiring enterprise architects just to deal with The Complexity Problem. Usually they have much more practical problems that they want addressing.

Read more »]]> tag:blogger.com,1999:blog-1254315679163990153.post-4350283004421735651 How Offices Make People Stupid Richard Veryard http://www.blogger.com/profile/04499123397533975655 noreply@blogger.com 2013-02-09T13:10:00+00:00

benhammersley at #RSAwork talks about the future of office work, and identifies some of the ways that organizations make themselves stupid. The irony is that a lot of these mechanisms were supposed to make offices more productive and efficient, and to promote collaboration and creativity. As Ben puts it

"We have optimized being on top of things rather than getting to the bottom of things."

Let's start with open plan offices. As Ben tells the story, these were introduced in an ideological attempt (supposedly originating in North California) to flatten the office hierarchy, to remove barriers between people, and to encourage people and technology to work together in perfect harmony. There are various dysfunctional versions of this Californian Ideology - see my post All Chewed Over By Machines (May 2011).

In practice, various interesting forms of behaviour emerge in open plan offices. Ben notes the widespread practice of more powerful workers grabbing the desks near to the wall, leaving juniors huddled in the middle in a state of permanent anxiety, as if they were antelope anticipating the lion's pounce.

Many offices are designed as semi-open plan, with people huddled in cubicles, but with the constant chance of someone popping a head over the partition.

In some offices, there is a deliberate policy to move people around - sometimes called hot-desking. One of the supposed benefits of this policy is that it encourages workers to constantly develop new relationships with their transient neighbours. For companies whose workers don't spend all their time in the office, this policy also reduces the amount of office space required. However, the uncertainty and anxiety of getting any desk, let alone a decent desk near the wall and away from the more irritating co-workers, might be regarded as a negative factor.

Putting aside the economics and culture and psychological impact of open plan offices, the essential justification is that they promote communication and collaboration. These elements are necessary but not sufficient for productivity and innovation in a knowledge-based organization. Not sufficient because productivity and innovation also depend on concentrated hard work.

Read more »]]>

"We have optimized being on top of things rather than getting to the bottom of things."

JohnQShift explains how to build a culture of learning in your business. He calls this A Matter of Life or Death (Feb 2013)

In the post, John reports one of his clients observing that they had made some progress in their business over the year. By progress, the client meant that

people were beginning to take up more responsibility and initiative without having to wait for the boss to tell them what to do

there was more discussion amongst the staff as to how to manage some of the day-to-day challenges they meet and less referring to the boss for the “answer”

mistakes were being used as entry points to examining business processes and working out how they could be improved

they had a clearer idea of their collective purpose and how important relationship is to achieving that purpose

the leaders were devoting more of their time to ensuring the conditions and structures of the business were optimised so that people could get on with their jobs (and less time micro-managing operational tasks).

Knowledge Management

Traditional knowledge management has failed to address the problem of knowledge worker productivity. Tools that have been developed in KM focused on information management and do not support many of the key knowledge work processes. Knowledge workers have therefore adpated the email client to suit their needs. It has become the most successful knowledge work tool because it combines personal control with personalisability and integrates communication.

Source: Martin Roell, August 2004

Big Data

By 2018, the United States alone could face a shortage of 140,000 to 190,000 people with deep analytical skills as well as 1.5 million managers and analysts with the know-how to use the analysis of big data to make effective decisions.

Source: McKinsey May 2011

Big data can create big value. But like all the big-data predecessors – i.e., databases, data warehousing, data mining, data analytics and business intelligence – you need to know what you’re looking for, why you’re looking for it, what’s it worth to you, and how will you take advantage of it BEFORE you start. Otherwise, big data will just be a big waste of money.

Source: Silver Peak Systems, May 2012

Social Media

Social media is addictive. And if you’re not too careful, it can seriously eat into your productivity.

Source: MediaBistro All-Twitter October 2012

Places are still available on my Organizational Intelligence Workshop (Feb 1st).]]>

Source: Accenture January 2007

Source: McKinsey July 2012

Knowledge Management

Source: Martin Roell, August 2004

Big Data

Source: McKinsey May 2011

Source: Silver Peak Systems, May 2012

Social Media

Social media is addictive. And if you’re not too careful, it can seriously eat into your productivity.

Source: MediaBistro All-Twitter October 2012

Places are still available on my Organizational Intelligence Workshop (Feb 1st).]]> tag:blogger.com,1999:blog-1254315679163990153.post-2103448630331042713 Beyond Personal Knowledge Management Richard Veryard http://www.blogger.com/profile/04499123397533975655 noreply@blogger.com 2013-01-18T10:42:00+00:00

hjarche via @Cybersal says "I'll show the same thing many times and people have various interpretations of it. Sharing knowledge artifacts is not transferring knowledge." In other words, we don't actually share knowledge, what we share are documents and other artifacts that are supposed to contain knowledge.

If I send a document to Harold or Sally, they may or may not be able to extract some knowledge from it. There are many possible causes of knowledge impedance or attenuation, such as obscure language and specialized terminology, poor presentation, low motivation, and information overload. Even if either of them is able to glean some knowledge from perusing my document, what they get out may be quite different from the knowledge I thought I was putting in. Their interpretations depend on many things: their situation, their prior knowledge, beliefs and values, their expectations about what I'm trying to say, and their ability to read between the lines. (Even my closest friends and associates, who I imagine share a lot of my assumptions, read my documents in ways I find surprising, which is why I always greatly value their comments.)

Harold promotes something he calls Personal Knowledge Management, which describes knowledge management as an Input-Process-Output system.

Input (Seek) - we gather knowledge from our environment, including other people
Process (Sense) - we interpret, personalize and use knowledge
Output (Share) - we pass on our knowledge to other people

Harold talks about Network Learning, which seems to be about taking advantage of digital connectivity and embedding the Input (Seek) and Output (Share) into a wide social network. What I don't see in Harold's account of Network Learning is any sense of collective sense-making - knowledge emerging from the collaboration rather than being generated by one person. I'm also troubled by the implication that knowledge is produced by thinking rather than doing, since I think the most useful knowledge is what emerges from practice.

So while there is undoubtedly a great deal of value in Harold's approach, I think it underplays some of the social and practical elements of knowledge management and organizational intelligence.

Harold Jarche's website: Personal Knowledge Management, Network Learning

See also Jose Baldaia, Who tells a story transfers tacit knowledge and creates new (May 2012)

Places are still available on my forthcoming workshops Business Awareness (Jan 28), Business Architecture (Jan 29-31), Organizational Intelligence (Feb 1).]]>

Input (Seek) - we gather knowledge from our environment, including other people
Process (Sense) - we interpret, personalize and use knowledge
Output (Share) - we pass on our knowledge to other people

Harold Jarche's website: Personal Knowledge Management, Network Learning

See also Jose Baldaia, Who tells a story transfers tacit knowledge and creates new (May 2012)

Places are still available on my forthcoming workshops Business Awareness (Jan 28), Business Architecture (Jan 29-31), Organizational Intelligence (Feb 1).]]> tag:blogger.com,1999:blog-7415430.post-9105473791456659017 Business Signal Optimization Richard Veryard http://www.blogger.com/profile/04499123397533975655 noreply@blogger.com 2013-01-17T13:32:00+00:00

DouglasMerrill of @ZestFinance (via @dhinchcliffe) tells us A Practical Approach to Reading Signals in Data (HBR Blogs November 2012)

If we think of data in tabular form, there are two obvious ways of increasing the size of the table - increasing the number of rows (greater volume of cases) or increasing the number of columns (greater volume of signals). This can either involve a greater variety of variables, as Merrill advocates, or a higher frequency of the same variable. I have talked in the past about the impact of increased granularity on Big Data.

As I understand it, Merrill's company sells Big Data solutions to the insurance underwriting industry, and its algorithms use thousands of different indicators to calculate risk.

The first question I always have in regard to such sophisticated decision-support technologies is what the feedback and monitoring loop looks like. If the decision is fully automated, then it would be good to have some mechanism to monitor the accuracy of the algorithm's predictions. Difficulty here is that there is usually no experimental control, so there is no direct way of learning whether the algorithm is being over-cautious. I call this one-sided learning,

Where the decision involves some human intervention, this gives us some further things to think about in evaluating the effectiveness of the decision-support. What are the statistical patterns of human intervention, and how do these relate to the way the decision-support software presents its recommendations?

Suppose that statistical analysis shows that the humans are basing their decisions on a much smaller subset of indicators, and that much of the data being presented to the human decision-makers is being systematically ignored. This could mean either that the software is too complicated (over-engineered) or that the humans are too simple-minded (under-trained). I have asked many CIOs whether they carry out this kind of statistical analysis, but most of them seem to think their responsibility for information management ends when they have provided the users with the requested information or service, therefore how this information or service is used is not their problem.

Meanwhile, the users may well have alternative sources of information, such as social media. One of the challenges Dion Hinchcliffe raises is how these richer sources of information can be integrated with the tabular data on which the traditional decision-support tools are based. I think this is what Dion means by "closing the clue gap".

Dion Hinchcliffe, The enterprise opportunity of Big Data: Closing the "clue gap" (ZDNet August 2011)

Dion Hinchcliffe, How social data is changing the way we do business (ZDNet Nov 2012)

Douglas Merrill, A Practical Approach to Reading Signals in Data (HBR Blogs November 2012)

Places are still available on my forthcoming workshops Business Awareness (Jan 28), Business Architecture (Jan 29-31), Organizational Intelligence (Feb 1).

]]>

Places are still available on my forthcoming workshops Business Awareness (Jan 28), Business Architecture (Jan 29-31), Organizational Intelligence (Feb 1).

]]> tag:blogger.com,1999:blog-7426899.post-7215657703122585186 Intelligent Marketing Richard Veryard http://www.blogger.com/profile/04499123397533975655 noreply@blogger.com 2013-01-16T00:05:00+00:00

John Wanamaker) once said, "Half the money I spend on advertising is wasted; the trouble is I don't know which half."

Matthew Leitch credits David Ogilvy with pioneering an experimental approach to advertising.

"Something that distinguished his approach from most other advertising agencies was his focus on using solid research to help make marketing decisions. He loved direct mail (or 'junk' mail as most people call it) because with direct mail it is possible to measure the response to advertising accurately. He could send slightly different advertisements to different groups of people and see what difference it made. He applied the same idea using reply coupons on page advertisements in newspapers and magazines, and used other forms of testing too. ... He also codified his research discoveries into over 100 rules of thumb to be applied to future advertisements. "

Ogilvy's approach has now become a routine element of the marketing process. I found a page on the IBM website documenting a Marketing Experimentation process to be automated using Websphere Commerce, although maybe it lacks Ogilvy's flair.

Experimentation allows marketing activity to be differentiated for different categories of customer demand, and refined in order to satisfy marketing goals more cost-effectively. One of the critical success factors here is the speed of the feedback loop - the faster the better.

There are various opportunities here for software technology to facilitate this feedback, by deploying advanced marketing communication tools that allow near-real-time measurement and analysis of campaigns. For example, a company called Intelligent Marketing Solutions claims to give you

"the ability to track, trace and manage every click your customer makes be it on a PC, Mobile or Tablet, you will know exactly how a campaign is performing around the clock and have the ability to make changes along the way to react to trends in response."

Other marketing tools can be used to provide independent monitoring of the effects of a marketing campaign, such as Buzz. See my post From Buzz to Actionable Intelligence (May 2010). If today's Buzz can give us a reasonable estimate of future revenues, then the marketing team can use Buzz as a predictive surrogate metric for the actual commercial success of a campaign.

Faster feedback can support single-loop learning - getting better at achieving a given set of marketing goals. Michael Mainelli and Ian Harris use a simplified version of VSM to describe a marketing process with feedforward and double-loop learning. Their system includes monitoring, information sharing, sense-making, planning, and continuous improvement - in other words a form of organizational intelligence.

Dion Hinchcliffe, Advocacy: The New Currency of Marketing (15 January 2013)

Ira Horowitz, A Note on Advertising and Uncertainty. Journal of Industrial Economics, Vol. 18, No. 2 (April 1970), pp. 151-160 http://www.jstor.org/stable/2097504

Michael Mainelli and Ian Harris, Is Your Organisation Viable? – Customer Relationship Management Systems, Conspectus, Prime Marketing Publications Ltd (October 1999) pages 26-27. via Matthew Leitch

David Ogilvy, Ogilvy on Advertising (Pan Books, 1983)

Places are still available on my forthcoming workshops Business Awareness (Jan 28), Business Architecture (Jan 29-31), Organizational Intelligence (Feb 1).]]>

"Something that distinguished his approach from most other advertising agencies was his focus on using solid research to help make marketing decisions. He loved direct mail (or 'junk' mail as most people call it) because with direct mail it is possible to measure the response to advertising accurately. He could send slightly different advertisements to different groups of people and see what difference it made. He applied the same idea using reply coupons on page advertisements in newspapers and magazines, and used other forms of testing too. ... He also codified his research discoveries into over 100 rules of thumb to be applied to future advertisements. "

"the ability to track, trace and manage every click your customer makes be it on a PC, Mobile or Tablet, you will know exactly how a campaign is performing around the clock and have the ability to make changes along the way to react to trends in response."

Places are still available on my forthcoming workshops Business Awareness (Jan 28), Business Architecture (Jan 29-31), Organizational Intelligence (Feb 1).]]> tag:blogger.com,1999:blog-1254315679163990153.post-7938705079176033021 On Readiness Richard Veryard http://www.blogger.com/profile/04499123397533975655 noreply@blogger.com 2013-01-15T09:25:00+00:00

In falconry, the word Yarak describes a trained hawk that is fit and in a proper condition for hunting. According to the Oxford Dictionary, the word entered the English language in the 19th century, perhaps from Persian yārakī 'strength, ability' or from Turkish yaraǧ 'readiness'.

Patrick explained that Yarak involves a balance between two forces - motivation and strength. The falcon has to be hungry enough to want to hunt, and strong enough to hunt effectively. So the falconer has to get the balance right: too little food and the creature cannot hunt, too much food and it can't be bothered.

When I talk to people about building organizational intelligence in their own organizations, I hear two forms of resistance. One is that the organization has so little inherent intelligence at present that the task is daunting; the other is that the bosses wouldn't want it.

When I take examples from glamorous high-tech companies like Microsoft and Google, this can provoke a somewhat fatalist reaction. People say: This kind of intelligence may be all very well for these hi-tech birds of prey, but ordinary companies like us simply don't have the resources or capability to do any of this stuff.

So it's important to see examples from ordinary companies as well as from the glamorous ones. Every company has some intelligence, although it may be patchy, fragmented and inconsistent. So we need to find ways of linking and leveraging this intelligence to create a positive spiral of improvement.

As for the question of motivation, there will still be many organizations where the senior management team, perhaps lacking confidence in its own intelligence, will lack enthusiasm for developing intelligence across the rest of the organization. This may be a generation thing - the younger generation of management may be much more comfortable with new styles of management (such as "Theory Y") as well as with social networking and other technologies.

Does this mean we have to wait for a generation, until the current bosses have shuffled off to the golf course or the Caribbean cruise? Not if the organization can start to develop intelligence in a bottom-up piecemeal fashion. In which case, what matters is the motivation and strength of the people and groups across the organization, and not just the motivation and strength of the bosses. Can we achieve some useful results without top-down support?]]>

The Prime Minister sometimes opposes the measures his own ministers put forward. He often finds out about these policies from the radio or newspapers.

Only 30 per cent of what the government is doing is actually delivering what we are supposed to be doing.

It’s a brilliant system for paper-shuffling people to be in control. The bureaucracy masters the politicians.

I just wanted to make a few comments about collective intelligence and the role of the policy adviser.

Some Prime Ministers and Presidents have had an extraordinary ability to get through large quantities of paperwork and master the critical points. Cameron has many strengths as a leader, but this doesn't seem to be one of them. As a consequence of this, he is effectively leaving journalists to perform a filtering function - thus he pays attention to an issue only when it is drawn to his attention by the media, and of course, this delayed attention may cause some irritation or embarrassment sometimes. Perhaps a more diligent policy adviser should have picked up some of these issues earlier?

In the system we may infer from Hilton's description, journalists are not only performing a filtering function but also a sensemaking function. There is clearly a difference between the way a policy looks in some bundle of government papers and how it looks when it appears in the media. Again, we might have expected a diligent policy adviser to have anticipated how policies would appear to the public.

But it seems that the politicians and their advisers don't control the volume of paperwork they are given to wade through. In his seminar, Hilton dramatically produced a pile of paper one foot high (representing four days committee output), prompting gasps from students. "The idea that a couple of political advisers read through all this and spot things that are bad, things that are contradictory, is just inconceivable", pleads Hilton.

Of course it is, say members of the previous government including Damian McBride, Gordon Brown's former political press secretary. Which is why the previous government had a greater number of political advisers, and a coordination process (known as the "grid") allocating a manageable number of pages to each. McBride acknowledges that the grid sometimes resulted in leaks to journalists, and suggests that Hilton may have downgraded the grid in order to reduce these leaks, but argues that the grid was a key mechanism for effective government and that the problems Hilton complains about are an inevitable consequence of abandoning this mechanism.

It may also be a consequence of regarding the civil service as a malignant force, trying to pull the wool over the politicians' eyes. (This was a great theme in the original "Yes Minister" series, but has turned into a tired joke in the 2013 series.) Edward Pearce stands up for the independence of the civil service, and complains that it is Hilton who is unrepresentative and unelected.

When Hilton talks about "delivering what we are supposed to be doing", this presumably refers to some kind of top-down strategic plan, formulated before the election and presented in the manifesto. But this raises some important questions about the relationship between strategy and execution, and the possibility for strategies to emerge and evolve during execution.

Which in turn raises some questions about government as a learning system. Recent governments (including Blair's New Labour) have had a focus on delivery, which emphasizes single-loop learning - getting better at achieving a fixed set of goals. However, this has to be balanced against double-loop learning - changing the goals to fit changing circumstances.

In an earlier analysis of New Labour and Delivery, two MORI analysts argued that delivery and achievement was at least partially subjective and rhetorical.

"Delivery" is not keeping your promises, it is convincing the public that you have kept your promises.

What matters is not what you promise, but what the public understands by those promises, and what expectations they arouse.

Hilton clearly agrees about the importance of external communication. He encourages his students to think about how policies can be "branded", and suggests that policies often fail not because they weren't very good policies in the first place but because they are poorly presented. That might be true, but it is also a common excuse: politicians genererally find it easier to admit to errors in presentation than to errors in policy.

Which part of this ecosytem has the longest memory? Presumably the civil servants. And which part has the shortest memory? With some honourable exceptions, probably the media. According to one theory of change, when there are several subsystems operating on different timescales, it is the slowest system that controls the whole. And the Purpose Of the System Is What It Does.

Roger Mortimore and Mark Gill, New Labour and Delivery (IPSOS MORI May 2004)

PM’s aide exposes No 10’s lack of control (Sunday Times, 13 January 2013) (subscription)

John Harlow and Eric Kiefer, Shoes off, feet up, the dude lifts lid on No 10 (Sunday Times, 13 January 2013)

Patrick Hennessy, David Cameron finds out about policies from the newspapers, reveals Steve Hilton (The Telegraph 13 January 2013)

Damien McBride, Whither the Grid? (13 January 2013) Why did the Grid Wither? (14 January 2013)

Edward Pearce, The Unelected (LRB 25 January 2013)

James Tapsfield, Prime Minister often finds out about policies from the radio or newspapers, says former advisor Hilton (The Independent 13 January 2013)

Richard Veryard (ed), Fragile Strategy or Fragile Execution (Storify, December 2012)

Nicholas Watt, David Cameron's ex-policy guru Steve Hilton criticised over policy remarks (Guardian, 13 January 2013)

updated 25 January 2013]]>

The Prime Minister sometimes opposes the measures his own ministers put forward. He often finds out about these policies from the radio or newspapers.

Only 30 per cent of what the government is doing is actually delivering what we are supposed to be doing.

It’s a brilliant system for paper-shuffling people to be in control. The bureaucracy masters the politicians.

"Delivery" is not keeping your promises, it is convincing the public that you have kept your promises.

What matters is not what you promise, but what the public understands by those promises, and what expectations they arouse.

Unicom is also planning another Enterprise Architecture Forum in London on March 21st. These generally attract a good audience of senior IT management from blue chip organizations. At the previous Forum in September, we had case studies from finance, oil and higher education. Anyone wishing to present a case study at the next event, please contact me or Unicom.

Those outside the UK may wish to plan a trip to London to coincide with these events.

These events are part-funded by commercial sponsorship and vendor exhibitions. Please contact me or Unicom for a vendor information pack.]]>

#oipsrv One view of innovation is that it is motivated by a series of challenges. Once upon a time, we would have used the word "problems", and called this the "problem-solving" approach to innovation. But the word "problem" is now taboo in business world, and we have to find various euphemisms such as "opportunity" or "challenge". Necessity is the mother of invention.

At a seminar at the British Library yesterday (Open Innovation in Public Services), I heard several ways of managing innovation in these terms.

Challenge Prizes - Offering cash prizes to the first person or team that can solve a well-defined problem. This approach has been used for centuries, although the history of technology is littered with unfortunate inventors who have produced something brilliant only to have the prize taken by a rival, or unfairly denied for various spurious reasons. Furthermore, a poorly designed prize can discourage collaboration and thus inhibit innovation instead of encouraging it. However, as Vicki Purewal explained, prize schemes do not have to follow the winner-takes-all, loser-gets-nothing rule, and are often designed to distribute the rewards more fairly and in stages. See Centre for Challenge Prizes

Hack Days - Bringing volunteers together for a day to build quick and dirty solutions to a broad range of problems. This approach is most commonly seen in the software arena, and the example presented was NHS Hackdays.

Challenge Platform - Creating a social network and/or funding for collective problem-solving. Contrasting examples from Barking and Dagenham, Camden, and York.

I think these are all good and useful initiatives. One of the benefits is that they open up the organization or ecosystem to ideas from a much larger community of people. This can be both more democratic and a lot more cost-effective than hiring one of the large consultancies, which seems to be the default method in some organizations. One way of putting this is that it changes the available scope of Organizational Intelligence.

However, problem-solving may be necessary for innovation, but is not sufficient. These initiatives concentrate on invention, which tends to be the sexy part of innovation. @davidtownson from the Design Council showed two slides that placed invention into a broader context. The first of these slides showed the Design Council's design process, drawn as a Double Diamond. The first diamond is devoted to clarifying the problem or requirement, and the second diamond is devoted to solving a well-defined problem. If the challenge-led approach starts from a well-defined problem, then it is just doing the second diamond.

Source: Design Council

The second of David's slides showed a spiral model of innovation, culminating in Systemic Change. (I can't find a version of this spiral on the Design Council website.) This might suggest extending the Double Diamond into a Triple Diamond, where the third diamond tackled the difficult and unglamorous end of the innovation process - rolling out the solution, integrating it with systems and working practices, and embedding it into the target organization or ecosystem.

This triple diamond faintly echoes the three-phase innovation model proposed (in a somewhat different context) by Abernathy and Utterback, which combined product innovation, process innovation, competitive environment and organizational structure:

Fluid phase (exploratory)
Transitional phase (convergence on solution)
Specific phase (focus on costs and performance)

Within the public sector, there may be broad demand for innovations (individual challenges), but there is also extremely strong demand for innovation as such (focus on costs and performance). So a suitably modified version of the Abernathy and Utterback model would be extremely relevant to the public sector.

Let us return to the question of Open Innovation. In her presentation, Heather Niven contrasted a large tanker with a flotilla of small boats. In the specific area of NHS information systems, Heather's metaphor applies very well to the contrast between the NPfIT - a grossly expensive centralized white elephant - and a large number of small but useful apps developed in the NHS Hackdays Carl Reynolds has organized. The "bottom-up" approach may be more promising than the "top-down" approach, as well as more exciting, but there probably needs to be a stronger element of coordination and integration before we can see this innovation as anything more than a load of well-meaning but marginal efforts by a bunch of extremely clever geeks.

Finally, there was some discussion about the word "innovation", and resistance to this concept within the public sector in particular. Perhaps we need to go back to talking about problem-solving?

Challenge Prizes - Offering cash prizes to the first person or team that can solve a well-defined problem. This approach has been used for centuries, although the history of technology is littered with unfortunate inventors who have produced something brilliant only to have the prize taken by a rival, or unfairly denied for various spurious reasons. Furthermore, a poorly designed prize can discourage collaboration and thus inhibit innovation instead of encouraging it. However, as Vicki Purewal explained, prize schemes do not have to follow the winner-takes-all, loser-gets-nothing rule, and are often designed to distribute the rewards more fairly and in stages. See Centre for Challenge Prizes

Hack Days - Bringing volunteers together for a day to build quick and dirty solutions to a broad range of problems. This approach is most commonly seen in the software arena, and the example presented was NHS Hackdays.

Challenge Platform - Creating a social network and/or funding for collective problem-solving. Contrasting examples from Barking and Dagenham, Camden, and York.

Source: Design Council

Fluid phase (exploratory)
Transitional phase (convergence on solution)
Specific phase (focus on costs and performance)

Finally, there was some discussion about the word "innovation", and resistance to this concept within the public sector in particular. Perhaps we need to go back to talking about problem-solving?

Abernathy, W.J. and Utterback, J.M. Patterns of Innovation in Technology (Technology Review 1978) via Innovation Zen

For @LucyInnovation 's report of the British Library seminar, see Because not all the smart people work for you ...]]> tag:blogger.com,1999:blog-1254315679163990153.post-164055263674077091 On Agility, Culture and Intelligence Richard Veryard http://www.blogger.com/profile/04499123397533975655 noreply@blogger.com 2012-11-13T00:58:00+00:00

Source: Deal and Kennedy

Meanwhile, speed of feedback also affects organizational intelligence. Shorter feedback loops are associated with greater agility and responsiveness, and faster learning, and is a popular meme of the Agile Software movement. Shahzad Bhatti is one of those who emphasizes the link with John Boyd's OODA loop.

"One of key finding he made was that shorter feedback or iteration loop of OODA with low quality was better than longer or tiring cycle of OODA with high quality. Despite the fact that everyone calls his/her organization agile, this feedback loop is real essense of agility."

So that seems to associate Agile with the upper two quadrants of the Deal and Kennedy model, and OODA with the top left quadrant.

So then what are the cultural implications of Agile for the host organization?

Notes and references

Lisa Crispin, Shortening the Feedback Loop (March 2011)
Ilan Kirschenbaum, What does a butterfly say at the end of the day? (May 2012)
Rune Larsen, Know your feedback loop – why and how to optimize it (Oct 2012)
Thomas Sundberg, Why should you use different technical practises when you develop software? (April 2011)

Places are still available on my Organizational Intelligence workshop, November 22nd.]]>

Source: Deal and Kennedy

"One of key finding he made was that shorter feedback or iteration loop of OODA with low quality was better than longer or tiring cycle of OODA with high quality. Despite the fact that everyone calls his/her organization agile, this feedback loop is real essense of agility."

Places are still available on my Organizational Intelligence workshop, November 22nd.]]> tag:blogger.com,1999:blog-6106782.post-8349844673864451645 Co-Production of Data and Knowledge Richard Veryard http://www.blogger.com/profile/04499123397533975655 noreply@blogger.com 2012-11-11T14:32:00+00:00

Here's an analogy for the so-called hierarchy of Data, Information, Knowledge and Wisdom DIKW).

Data = Flour
Information = Bread
Knowledge = A Recipe for Bread-and-Butter Pudding
Wisdom = Only Eating A Small Portion

Note that Information isn't made solely from Data, Knowledge isn't made solely from Information, and Wisdom isn't made solely from Knowledge. See also my post on the Wisdom of the Tomato.

That's enough analogies. Let me now explain what I think is wrong with this so-called hierarchy.

Firstly, the term "hierarchy" seems to imply that there are three similar relationships.

between Data and Information
between Information and Knowledge
and between Knowledge and Wisdom

as well as implying some logical or chronological sequence

Data before Information
Information before Knowledge
Knowledge before Wisdom

and quantitative relationships

Much more data than information
Much more information than knowledge
Tiny amounts of wisdom

But my objection to DIKW is not just that it isn't a valid hierarchy or pyramid, but it isn't even a valid schema. It encourages people to regard Data-Information-Knowledge-Wisdom as a fairly rigid classification scheme, and to enter into debates as to whether something counts as "information" or "knowledge". For example, people often argue that something only counts as "knowledge" if it is in someone's head. I regard these debates as unhelpful and unproductive.

A number of writers attack the hierarchical DIKW schema, and propose alternative ways of configuring the four elements. For example, Dave Snowden says that "knowledge is the means by which we create information out of data". Meanwhile Tom Graves suggests we regard DIKW not as ‘layers’, but as distinct dimensions in a concept-space.

But I don't see how any of these DIKW remixes escapes the most fundamental difficulty of DIKW, which is a naive epistemology that has been discredited since the Enlightenment. You don't simply build knowledge out of data. Knowledge develops through Judgement (Kant), Circular Epistemology and Dialectic (Hegel), Assimilation and Accommodation (Piaget), Conjecture and Refutation (Popper), Proof and Refutation (Lakatos), Languaging and Orientation (Maturana), and/or Mind (Bateson).

What all of these thinkers share is the rejection of the Aristotelian idea of "one-way traffic" from data to knowledge, and an insistence that data must be framed by knowledge. Thus we may validate knowledge by appealing to empirical evidence (data), but we only pick up data in the first place in accordance with our preconceptions and observation practices (knowledge). Among other things, this explains why organizations struggle to accommodate (and respond effectively to) weak signals, and why they persistently fail to "connect the dots".

And if architects and engineers persist in trying to build information systems and knowledge management systems according to the DIKW schema, they will continue to fall short of supporting organizational intelligence properly.

For a longer and more thorough critique, see Ivo Velitchkov, Do We Still Worship The Knowledge Pyramid (May 2017)

Other references

Tom Graves, Rethinking the DIKW Hierarchy (Nov 2012)
Patrick Lambe, From Data With Love (Feb 2010)
Dave Snowden, Sense-making and Path-finding (March 2007)
Gordon Vala-Webb, The DIKW Pyramid Must Die (KM World, Oct 2012) - as reported by V Mary Abraham
DIKW Model (KM4dev Wiki)

Updated 8 December 2012
Link added 26 November 2017]]>

Here's an analogy for the so-called hierarchy of Data, Information, Knowledge and Wisdom DIKW).

Data = Flour
Information = Bread
Knowledge = A Recipe for Bread-and-Butter Pudding
Wisdom = Only Eating A Small Portion

Note that Information isn't made solely from Data, Knowledge isn't made solely from Information, and Wisdom isn't made solely from Knowledge. See also my post on the Wisdom of the Tomato.

That's enough analogies. Let me now explain what I think is wrong with this so-called hierarchy.

Firstly, the term "hierarchy" seems to imply that there are three similar relationships.

between Data and Information
between Information and Knowledge
and between Knowledge and Wisdom

as well as implying some logical or chronological sequence

Data before Information
Information before Knowledge
Knowledge before Wisdom

and quantitative relationships

Much more data than information
Much more information than knowledge
Tiny amounts of wisdom

For a longer and more thorough critique, see Ivo Velitchkov, Do We Still Worship The Knowledge Pyramid (May 2017)

Other references

Tom Graves, Rethinking the DIKW Hierarchy (Nov 2012)
Patrick Lambe, From Data With Love (Feb 2010)
Dave Snowden, Sense-making and Path-finding (March 2007)
Gordon Vala-Webb, The DIKW Pyramid Must Die (KM World, Oct 2012) - as reported by V Mary Abraham
DIKW Model (KM4dev Wiki)

Updated 8 December 2012
Link added 26 November 2017]]> tag:blogger.com,1999:blog-1254315679163990153.post-1993745907836684486 Learning Lessons Learned Richard Veryard http://www.blogger.com/profile/04499123397533975655 noreply@blogger.com 2012-11-06T14:45:00+00:00

#orgintelligence Adapted from my contribution to a Linked-In discussion on "Lessons Learned".

There are several strands of learning from experience, and it may be useful to call these out explicitly.

1. Signals. What signals (that turned out to be important) could we have picked up sooner? What signals (that turned out to be unimportant) did we pay too much attention to?

2. What outcomes were achieved? To what extent did these outcomes match requirements and/or expectations? To what extent did requirements and expectations change during the project? Do we now recognize that some of the original requirements and expectations were inappropriate or unachievable?

3. Sense-making. How do we explain the things that went well and not so well? How much of what happened can be attributed to random variation? Which factors could have been better controlled?

4. Policy-making. What measures could be put in place to improve outcomes in future? How should these measures be communicated and enforced?

5. Learning. How many similar lessons were identified by previous projects and not implemented? How do we explain this? (For example, have voluntary guidelines worked in the past, or is a more formal governance called for?) How shall we check whether future projects learn any of these lessons?]]>

#orgintelligence Adapted from my contribution to a Linked-In discussion on "Lessons Learned".

Simon Jenkins, Guardian 23 October 2012

The popular belief in merger and acquisition to deliver economics of scale overlooks the common reality "that high executives from one of the previously separate corporations would be at loggerheads with executives from the other, that from top to bottom the cultures of melded corporations wouldn’t mesh, that cost savings wouldn’t materialize, that earnings would not be smoothed out, that purchasing corporations wouldn’t know how to make good use of acquired corporations, that different industries require very different mentalities, that size was achieved by destroying highly innovative, often new companies, that companies make acquisitions not because this creates better economic entities but because it creates more power, more prestige and vast compensation for high executives".

Dean Velvel, The Bigger The Company, The More Disastrous The Mistake (September 2008)

"Many companies try to grow via big acquisitions. These deals are seductive, because you get lots of favorable ink and a love buzz from Wall Street. You also buy time to implement your strategy, if you actually have one, because year-to-year financials aren't comparable and outsiders can't analyze your results. WorldCom is a classic case. Chief executive Bernie Ebbers--make that former chief executive Ebbers--wanted his grandly named enterprise to be the nation's biggest telecom firm. He got up to No. 2 by making about five dozen acquisitions. But when the takeover music stopped two years ago after regulators nixed his proposed purchase of Sprint, it became clear the company was a mess. Bye-bye, Bernie. And with WorldCom stock down 95 percent from its high, bye-bye to $100 billion of shareholder wealth."

Allan Sloan, Memo To Ceos: Bigger Isn't Better (Newsweek via Daily Beast, May 2002)

]]>

Simon Jenkins, Guardian 23 October 2012

Dean Velvel, The Bigger The Company, The More Disastrous The Mistake (September 2008)

Allan Sloan, Memo To Ceos: Bigger Isn't Better (Newsweek via Daily Beast, May 2002)

]]> tag:blogger.com,1999:blog-7415430.post-7339893332856808587 Embedding Intelligence into Technical Systems Richard Veryard http://www.blogger.com/profile/04499123397533975655 noreply@blogger.com 2012-10-22T15:58:00+00:00

Smart GridOne area where intelligence is being embedded into technical systems is in electric power infrastructure - the Grid.

The demand for intelligence in this context includes several factors

efficiency (delivering more with less)
reliability and robustness (fault tolerance)
affordable growth
systemic risk (climate change, national security)

Sources

US Department of Energy, Smart Grid (pdf),
Gridwise Alliance
Raymond Kelley, Architecting Intelligence into AMI systems (Penn Energy)
Gene Wolf, Embedded Intelligence (April 2007)]]>

Smart GridOne area where intelligence is being embedded into technical systems is in electric power infrastructure - the Grid.

The demand for intelligence in this context includes several factors

efficiency (delivering more with less)
reliability and robustness (fault tolerance)
affordable growth
systemic risk (climate change, national security)

Sources

US Department of Energy, Smart Grid (pdf),
Gridwise Alliance
Raymond Kelley, Architecting Intelligence into AMI systems (Penn Energy)
Gene Wolf, Embedded Intelligence (April 2007)]]> tag:blogger.com,1999:blog-1254315679163990153.post-5537107361143220820 Delusion and Diversity Richard Veryard http://www.blogger.com/profile/04499123397533975655 noreply@blogger.com 2012-10-09T16:32:00+00:00

VenessaMiemis asks "If most people are self delusional, what's the point of qualitative research?" @CoCreatr retorts "What if we are all self-delusional and need proof by qualitative research to become more accepting of it?"

Of course organizations are self-delusional - some more than others. The interesting challenge here is to distinguish between evidence-based policy (which forms policy based on the evidence) and policy-based evidence (which collects evidence to support a given policy).

"Yes", says @CoCreatr, "organizationally the difference between fundamentalism and curiosity". He points to a presentation by Seth Godin on Curiosity.

Without curiosity, people in organizations tend to use research (both qualitative and quantitative) to try and promote their own delusions and/or undermine the delusions of others. So there is a dual purpose for research.

This is why diversity helps. If there is a broad variety of delusion, then there is at least the possibility of enough evidence emerging to create sufficient cognitive dissonance for cherished delusions to be questioned. Whereas if everyone shares the same delusions (commonly known as "groupthink"), then cognitive dissonance seems much less likely to occur.

By the way, the "diversity" agenda usually focuses on achieving a fair distribution of age, gender, race, class background, etc., as well as avoiding various forms of unfair discrimination. Sometimes this is justified not only on ethical grounds, but also because it is thought to increase the likelihood of cognitive diversity - different attitudes, personality styles and belief systems.

But the correlation between externally visible diversity and cognitive diversity looks more tenuous nowadays than it might have done in the past. If you assume attitude, personality style and belief system depends solely on the colour of your skin and the number of X chromosomes, then you might exaggerate the differences between Hillary Clinton and Barack Obama. (As the popular song goes, I wonder who's Kissinger now? See my post Relationships Based on Self-Interest from January 2009).

Talking of presidents and would-be presidents ... In his 1967 book on Organizational Intelligence, Harold Wilensky praised President Roosevelt for maintaining a state of creative tension in the US administration. Wilensky reckoned that this enabled FDR to get a more accurate and rounded account of what was going on, and gave him some protection against the self-delusion of each department. Roosevelt could still get a degree of cognitive diversity from a bunch of white men with similar education. Think what he could achieve today.

Related Blogs

Relationships built on self-interest (January 2009)
What is the Purpose of Diversity? (January 2010)
Organizational Intelligence and Gender (October 2010)
Delusion and Diversity (October 2010)
More on the Purpose of Diversity (December 2014)

Links added 12 December 2014]]> tag:blogger.com,1999:blog-1254315679163990153.post-6906216860552339715 Gillian Stamp on Effective Decision-Making Richard Veryard http://www.blogger.com/profile/04499123397533975655 noreply@blogger.com 2012-08-29T02:20:00+00:00

#orgintelligence I found an interesting chart on Gillian Stamp's blog.

Effective Decision-Making

The chart illustrates two observations

"Organizational well-being depends on the interplay between challenges and decision-making capabilities."

"Where challenges exceed capabilities, financial and human costs rise; where capabilities exceed challenges, resources are wasted."

Source: The Individual, the Organisation and the Path to Mutual Appreciation (dated 2004)

Stamp sees decision-making primarily as an individual activity, and seeks to understand the conditions for effective decision-making by individuals within organizations. She is particularly concerned about levels of individual stress and anxiety caused by a mismatch betwen individual capability and individual responsibility, and advocates a process she calls Career Path Appreciation to improve the alignment between capability and responsibility over the course of an individual's career.

Similar thinking could be applied to the collective decision-making capability of groups, teams and whole organizations. The collective capabilities for decision-making need to match the scale of the challenges facing the organization, and we might reasonably expect some symptoms of anxiety to manifest themselves in organizations that are under-endowed or over-endowed with organizational intelligence. (For a detailed account of organizational anxiety and its symptoms, see Larry Hirschhorn's Workplace Within.)]]>

#orgintelligence I found an interesting chart on Gillian Stamp's blog.

Effective Decision-Making

The chart illustrates two observations

"Organizational well-being depends on the interplay between challenges and decision-making capabilities."

"Where challenges exceed capabilities, financial and human costs rise; where capabilities exceed challenges, resources are wasted."

#orgintelligence @larryhirschhorn has produced a very detailed analysis of Barclays Bank, Robert Diamond and the LIBOR scandal (July 2012). He asks why Marcus Agius (Barclays Chair) and Bob Diamond (Barclays CEO) were stunned at the Bank of England's demand for Diamond's resignation, and suggests it was because they lacked something he calls a “political imagination”.

There is a lot of interesting material in Larry's blog from the perspective of organizational psychology, and I don't want to reproduce it all here. What I do want to explore is whether what Larry calls "political imagination" is an aspect of what I call organizational intelligence.

Central to Larry's narrative is a cryptic note, written by Bob Diamond after a telephone conversation with Paul Tucker, the Bank of England’s executive director for markets. This note appears to have been interpreted by one of Diamond's subordinates as an coded instruction from the Bank of England to lower its LIBOR submissions. However, Diamond later denied that this was the meaning of the note. As Larry points out, this kind of deniability is all too common in and between organizations.

What is more complicated is the decision by Barclays to include this note in its published account of the LIBOR affair. Why was this note relevant to the LIBOR affair, if it didn't mean what it appeared to mean? Diamond's self-justification and repudiation looks like what Freud called Kettle Logic - "we didn't fix the LIBOR rate ... and anyway you hinted we should fix it ... and anyway it wasn't a hint".

The Bank of England was undoubtedly sensitive to the allegation that it had been complicit in the LIBOR affair, and seems to have reacted angrily to the publication of this note. Diamond and his colleagues may have decided to include the note as a coded message to other banks, but failed to anticipate the reaction of the Bank of England. And as one of the highest paid bankers in London, Diamond may also have failed to appreciate the extent to which the Bank of England disapproved of overpaid London bankers.

According to the Wall Street Journal, there were differences of opinion within Barclays as to whether it was a good idea to include this note in its report, and there were some who worried about the reaction. However, the decision was taken to include it. At the time, this might have seemed like a fairly small detail, but such details can sometimes have very significant consequences.

(Of course, we cannot know for sure that it was this detail that triggered the Bank of England's demand for Diamond's resignation, but it is a highly plausible interpretation of events.)

One of the most common limitations of organizational intelligence is that all decisions are taken within a fixed frame of reference - which I regard as a failure of sensemaking. Larry suggests that Bob Diamond was operating within a frame of reference based on "technical rationality", within which the publication of the controversial note seemed perfectly reasonable, and that he lacked the imagination to move outside this frame of reference. Larry also indicates some of the organizational mechanisms that may have helped to reinforce Diamond's limited worldview, including his experience of being protected by his subordinates.

In that regard, there are some strong parallels with the Murdoch empire and its recent troubles. When Diamond said (speaking to the House of Commons Treasury Committee), "When I read the e-mails from those traders I got physically ill" (BBC News, 4 July 2012), I was convinced I had heard either Rupert or James Murdoch saying much the same thing a few weeks earlier. They are obviously using the same scriptwriter.

Doubtless there will be a stage play at the Royal Court before long, showing us the tragic fall of these doomed heroes.]]>

The question of organizational intelligence is about the power of an organization to think powerfully and coherently, and the power to learn and solve problems quickly. How does a political party become aware of new opportunities and threats in the socioeconomic environment, or new situations that call for a coordinated political response? How does a party develop and evolve stories and narratives, to make sense of new situations? How are policies developed, tested and agreed? How do new ideas (including new problems and new solutions) travel through a political organization, and is this different from the way ideas travel through other kinds of organization? How do different communication mechanisms and technologies (e.g. meetings, internet forums, social networking) affect the development of a coherent political consensus?

@mrianleslie sees the problem in terms of diversity. Having too many clever men around Ed Miliband is making the Labour Party stupider (New Statesman, 25 June, 2014). Clearly that is an important factor, but it is not the only one.

We are more accustomed to looking at these questions in relation to large commercial organizations or government bodies. Enron is a fascinating example, because it was packed with talented people, but the business was incoherent. Microsoft is another fascinating example, because everyone imagines (wrongly) that the decisions are all taken at the top. Perhaps party organizations would like to be like Microsoft, but end up more like Enron. So how can parties get better at thinking?

I am keen to make contact with anyone who would be interested in exploring this question - either from within one of the political parties, or as an outside observer.

Related Post
Political parties and organizational intelligence 2 (June 2015)

Updated 25 June 2014]]>

I am keen to make contact with anyone who would be interested in exploring this question - either from within one of the political parties, or as an outside observer.

Related Post
Political parties and organizational intelligence 2 (June 2015)

Updated 25 June 2014]]> tag:blogger.com,1999:blog-7426899.post-7130671608343743656 Size and Organizational Intelligence Richard Veryard http://www.blogger.com/profile/04499123397533975655 noreply@blogger.com 2012-04-25T21:16:00+00:00

It is a popular idea that large organizations should behave like small organizations: one way to achieve this is to look at the way successful small organizations practise organizational intelligence. Let us start by asking whether it is NECESSARY to sacrifice the good things about small organizations to become a big one? And if not, why does it often seem to happen?

Small startup companies often need to mobilize high levels of organizational intelligence. One reason may be a self-reinforcing narrowness of scope that forces a tight focus on essentials; small companies have very limited resources they can't afford to squander. Therefore the founders and early employees scan the environment keenly and solve problems collectively.

As the company grows in size, it may lose some of this keen intelligence. Customer situations recur, and many of the day-to-day problems have been solved, so the challenge becomes simply efficiently repeating known solutions to known problems. Meanwhile, the immediate and intensive communication and coordination enjoyed by small teams gets attenuated as the organization grows. Workers still spend large amounts of time communicating with their colleagues, but the critical flows of information are more indirect and may suffer interruption, distortion and interference.

Because of these factors, large companies often display a number of pathological characteristics. For example: forced diversification to keep growing; geographic expansion and timezone issues; competitors start to see you as a threat; regulators get interested in you; new people join with their own agendas.

...

Extract from new book on Organizational Intelligence by Richard Veryard. Available at http://leanpub.com/orgintelligence/]]>

Extract from new book on Organizational Intelligence by Richard Veryard. Available at http://leanpub.com/orgintelligence/]]> tag:blogger.com,1999:blog-7415430.post-2395319070772106916 Embedding Intelligence in the Business Process - Reprise Richard Veryard http://www.blogger.com/profile/04499123397533975655 noreply@blogger.com 2012-04-16T22:11:00+00:00

Embedding business intelligence (BI) into the business process.
Embedding Enterprise 2.0 into the business process.
Embedding knowledge (content) into the business process.
Embedding learning into the business process.
Embedding collaboration into the business process -
Embedding intelligence into business capabilities

There are various hardware and software vendors who offer one or more of these. Here is an unscientific selection.

Intel - Embedded@Intel

Oracle - Fusion CRM Applications - Embedded Intelligence

Oracle Fusion CRM Applications - Embedded Intelligence

Textron Defense Systems - "sophisticated embedded intelligence applications that enable warfighters to do more, better and faster, with their current assets". Features of this embedded intelligence apparently include augmented futures, social networking, anthropology, resource optimization, HCI and force multiplication. (Textron website)

Workday - "Embedded Intelligence within business processes allows for real-time contextual insight. Workday enables pre-built or custom worklets to be embedded within selected business processes. These worklets provide relevant, real-time business insight in context at the point of decision." (Press release August 2011)

Machine Analytics, a company in the Boston area, offers a solution methodology called Amitie for embedding intelligence into the business process. Amitie stands for Analyze, Model, IMplement, Test, Interface and Evaluate. The embedding takes place in the final two steps: interfacing the implemented and tested model with the client's business process, and evaluating/monitoring performance of the embedded model within the client's business process environment.

This material is partly based on some posts from November-December 2010.

]]>

Embedding business intelligence (BI) into the business process.
Embedding Enterprise 2.0 into the business process.
Embedding knowledge (content) into the business process.
Embedding learning into the business process.
Embedding collaboration into the business process -
Embedding intelligence into business capabilities

This material is partly based on some posts from November-December 2010.

]]> tag:blogger.com,1999:blog-6106782.post-299936582366367804 From Design Thinking to Creative Intelligence Richard Veryard http://www.blogger.com/profile/04499123397533975655 noreply@blogger.com 2012-04-07T00:08:00+00:00

Field Manual 5-0: The Operations Process (pdf), published in March 2010, where design is described as a kind of sensemaking or orientation process - an intelligent front-end to the real business of decision making and action.

the importance of understanding complex problems more fully before we seek to solve them through our traditional planning processes ... applying design to understand before entering the visualize, describe, direct, lead, and assess cycle.

Thus design is part of the intelligence loop (rather than the other way around). See also @EllenNaylor on Design Thinking for Strategic Competitive Advantage.

In April 2011, Bruce Nussbaum, described as "one of Design Thinking’s biggest advocates" posted a blog entitled Design Thinking Is A Failed Experiment. So What’s Next? His answer: Creative Intelligence, the ability to frame problems in new ways and to make original solutions.

On the one hand, Nussbaum dreams that that his godchild will win admission to a top university on the strength not only of her IQ but also her creative intelligence - in other words, seeing creative intelligence as an attribute of individual genius. On the other hand, he wants to frame creative intelligence not in terms of a psychological approach of development stages but a sociological approach in which creativity emerges from group activity - in other words, seeing creative intelligence as an attribute of a group or organization, not just the individuals within it.

See further commentary by Tom Berno, Cameron D Norman, Erica Schlaikjer.

The draft of my book on Organizational Intelligence is now available on LeanPub http://leanpub.com/orgintelligence. Please support this development by subscribing and commenting. Thanks.]]>

the importance of understanding complex problems more fully before we seek to solve them through our traditional planning processes ... applying design to understand before entering the visualize, describe, direct, lead, and assess cycle.

The draft of my book on Organizational Intelligence is now available on LeanPub http://leanpub.com/orgintelligence. Please support this development by subscribing and commenting. Thanks.]]> tag:blogger.com,1999:blog-6106782.post-3706231275220789481 Intelligent Business Operations Richard Veryard http://www.blogger.com/profile/04499123397533975655 noreply@blogger.com 2012-04-06T10:48:00+00:00

opheretzion reposts a healthcare example from @jimsinur concerning resource allocation in surgeries.

The loop is as follows.

Decision / Planning	Scheduling and resource allocation for the following day, using simulation and optimization tools.
Information Gathering	Real-time tracking of selected "things" (physicians, nurses, equipment; monitor of procedure duration and status) using a range of devices (sensors and cameras).
Sensemaking	Detecting deviations from plan - "things going wrong"
Decision / Planning	Revising the plan in "near real time"

Obviously this is an impressive use of the relevant technologies, and it may well deliver substantial benefits in terms of supply-side cost-effectiveness as well as a safer and better experience for the patient. This is essentially a goal-directed feedback loop.

However, we may note the following limitations of this loop.

1. Decision / Planning is apparently based on a fixed pre-existing normative model of operations - in other words a standard "solution" that should fit most patients' needs. This may be a reasonable assumption for some forms of routine surgery, but doesn't seem to allow for the always-present possibility of surprise when you cut the patient open.

2. Information Gathering is based on a fixed set of things-to-be-monitored. Opher calls this "real-time tracking of everything" - but of course this is a huge exaggeration. Perhaps the most important piece of information that cannot be included in this rapid feedback loop is the patient outcome. We might think that this cannot be determined conclusively until much later, but there may be some predictive metrics (perhaps the size of the incision?) that may be strongly correlated with patient outcomes.

3. Sensemaking is extremely limited - there is no time to understand what is going wrong, or to carry out deeper root-cause analysis and learning. All we can do is to react according to previously established "best practice".

4. Replanning is limited to detecting and quick-fixing deviations from the plan. See my post on Real-Time Events.

Full organizational intelligence needs to integrate this kind of rapid goal-directed feedback loop with a series of deeper analytic sensemaking and learning loops. For example, we might want to monitor how a given surgical procedure fits into a broader care pathway for the patient. Real-time monitoring is then useful not only for near-real-time operational intelligence but also for longer-term innovation.

Jim Sinur Success Snippet (January 2012)

Opher Etzion Medical Use Case (January 2012)

And please see my draft Organizational Intelligence Primer, now available on LeanPub.]]>

opheretzion reposts a healthcare example from @jimsinur concerning resource allocation in surgeries.

The loop is as follows.

Decision / Planning	Scheduling and resource allocation for the following day, using simulation and optimization tools.
Information Gathering	Real-time tracking of selected "things" (physicians, nurses, equipment; monitor of procedure duration and status) using a range of devices (sensors and cameras).
Sensemaking	Detecting deviations from plan - "things going wrong"
Decision / Planning	Revising the plan in "near real time"

4. Replanning is limited to detecting and quick-fixing deviations from the plan. See my post on Real-Time Events.

Jim Sinur Success Snippet (January 2012)

Opher Etzion Medical Use Case (January 2012)

And please see my draft Organizational Intelligence Primer, now available on LeanPub.]]> tag:blogger.com,1999:blog-6106782.post-104655011844883192 Organizational Intelligence eBook Richard Veryard http://www.blogger.com/profile/04499123397533975655 noreply@blogger.com 2012-03-31T14:15:00+00:00

#orgintelligence I have put a draft of my Organizational Intelligence book onto the @LeanPub platform. This is now available in PDF, ePUB and MOBI.

http://leanpub.com/orgintelligence

I'm also hoping to get a draft of my Business Architecture book up soon, but this is going to take a bit longer because of the large number of diagrams.

If you work for a large company and can claim the book on expenses, please consider paying the full price. Otherwise, early readers can get the book for $15, paid via PayPal. LeanPub takes a small cut and I get the rest.

The idea of the LeanPub platform is to provide financial and moral support to authors during the development process. A variable pricing scheme encourages readers to subscribe to a book at an early stage and get full access to the book as it develops. I'm also hoping to get a lot of detailed questions, comments and suggestions.

Many thanks to Tom Graves for introducing me to LeanPub. As he states in his blog on Publishing Tetradian e-Books via LeanPub, there are a few constraints and minor bugs in the platform at present; so if you are thinking of publishing something yourself, please feel free to talk to Tom or myself.]]>

#entarch In his talk to the BCS Enterprise Architecture Group this week, Patrick Hoverstadt suggested that traditional enterprise architecture obeyed Alfred Chandler's principle: Structure Follows Strategy. In other words, first the leadership defines a strategy, and then enterprise architecture helps to create a structure (of sociotechnical systems) to support the strategy.

Chandler's principle was published in 1962, and is generally regarded nowadays as much too simplistic. In 1980, Hall and Saias published a paper asserting the converse principle Strategy Follows Structure! (pdf), and most modern writers now follow Henry Mintzberg in regarding the relationship between strategy and structure as reciprocal.

What are the implications of this for enterprise architecture? Patrick offered us a simple syllogism: if enterprise architects determine structure, and if structure determines strategy, then enterprise architects are (consciously or unconsciously) determining strategy. In particular, the strategies that are available to the enterprise are limited by the information systems that the enterprise uses (a) to understand what is going on both internally and externally, and (b) to anticipate future developments. In many situations, the information isn't readily accessible in the form that managers would need to mobilize a strategic response to the complexity of the demand ecosystem.

Of course it isn't as simple as this. The defacto structure (including its information systems) is hardly ever as directed by enterprise architecture, but is created by countless acts of improvisation by managers and workers just trying to get things done. (The late Claudio Ciborra wrote brilliantly about this.) People somehow get most of the information they need, not thanks to the formal information systems but despite them. Thus the emergent structures are a lot more powerful and rich than the official structures that enterprise architects and others are mandated to produce. Patrick cites the example of a wallpaper factory where productivity was markedly reduced after smoking was banned; a plausible explanation for this was that smoking had provided a pretext for informal communication between groups.

Meanwhile, Minztberg drew our attention to a potential gulf between the official strategy and the defacto emergent strategy. (I have always especially liked his example of the Canadian Film Board, published in HBR July-Aug 1987.)

Nevertheless, Patrick's mission (which I endorse) is to connect enterprise architects with the strategy processes in an enterprise. He is a strong advocate of Stafford Beer's Viable Systems Model (VSM). Using his approach, which he encourages enterprise architects to adopt, VSM provides a unique lens for viewing the structure of enterprise, and for recognizing some common structural errors, which he calls pathological; I encourage enterprise architects to read his book on The Fractal Organization.]]>

Content enhanced to be fit-to-purpose ... content that is organized and structured for customer tasks and needs, not just for the production, packaging and distribution of physical documents. (Mirko Minnich, ex Elsevier)

Self-organizing and transparent content, organizing itself automatically depending on your context, goals, and workflow, and allowing you to see why it's doing what it's doing. (Mark Stefik, Xerox PARC)

Granular at the appropriate level, semantically rich, useful across applications, and meaningful for collaborative interaction. (Gilbane Group)

Has good metadata (not lots), fit for purpose, uses classifications to provide context and aid discoverability (Madi Solomon, Pearson)

And there are several characteristic technologies that are supposed to facilitate smart content, among other things. Some of these technologies are linked to Sir Tim Berners-Lee. Come on Tim!

Semantic technologies to cross-reference and cross-polinate with other kinds of content. (Madi Solomon, Pearson)

As Natasha Fogel pointed out, "smart content is in the eye of the beholder" - in other words, the perceived smartness of content is relative to its context of use.

But in this post, I don't want to talk about the technologies themselves but about the emerging value propositions that may be supported by smart content. Last year, when he was a SVP at scientific and technical publisher Elsevier, Mirko Minnich talked about two key enablers for smart content. Firstly a value-adding process - transmuting content into scientific data, and transmuting scientific data into solutions. And secondly what he calls a product bridge, not only linking content with data but also linking the content business with the data analytics business. The product bridge appears to be a kind of platform, and Mirko was using the term "Smart Content" to refer to the platform itself as well as the content delivered on the platform.

Mirko's strategy at Elsevier represented a strong drive towards asymmetric design - in other words, recognizing that in order to deliver indirect value into a complex ecosystem you have to move away from a traditional product-based business model (in Elsevier's case, selling scientific journals) towards regarding your business as a multi-sided platform.

Mark Stefik (Xerox PARC) puts smart content into an organizational intelligence frame - the intelligence is now located (reified) in the content as well as in the people producing and consuming the content. Instead of the user asking "what content do I need", Mark wants the content to ask "who needs me?" Madi Solomon (Pearson) seems to be suggesting the exact opposite when he mentions the Big Shift from Push to Pull in his recent presentation on Smart Content. We can resolve this apparent contradiction only by understanding the intelligence as the property of the whole system rather than trying to locate it in one place - see my material on organizational intelligence.

Sources

Content enhanced to be fit-to-purpose ... content that is organized and structured for customer tasks and needs, not just for the production, packaging and distribution of physical documents. (Mirko Minnich, ex Elsevier)

Self-organizing and transparent content, organizing itself automatically depending on your context, goals, and workflow, and allowing you to see why it's doing what it's doing. (Mark Stefik, Xerox PARC)

Granular at the appropriate level, semantically rich, useful across applications, and meaningful for collaborative interaction. (Gilbane Group)

Has good metadata (not lots), fit for purpose, uses classifications to provide context and aid discoverability (Madi Solomon, Pearson)

And there are several characteristic technologies that are supposed to facilitate smart content, among other things. Some of these technologies are linked to Sir Tim Berners-Lee. Come on Tim!

Semantic technologies to cross-reference and cross-polinate with other kinds of content. (Madi Solomon, Pearson)

Sources

Seth Grimes, Six definitions of smart content (Information Week, Sept 2010)Several of the quotes above come from this article.

Technology in Publishing (Editors Update, Elsevier, Jan 2011)
Next Generation Clinical Decision Support (Elsevier Press Release, Feb 2011)

Madi Solomon, Making Information Pay (April 2011)]]> tag:blogger.com,1999:blog-6106782.post-4035567522792210122 Five Views of Business Architecture (OMG) Richard Veryard http://www.blogger.com/profile/04499123397533975655 noreply@blogger.com 2011-10-17T01:04:00+00:00

#OMG #BAWG The Object Management Group Business Architecture Working Group has identified five key views of a business.

the Business Strategy view ("What the Business Wants")
the Business Capabilities view ("What the Business Does")
the Value Stream view ("How the Business Does")
the Business Knowledge view ("What the Business Knows", "How the Business Thinks")
the Organizational view ("What the Business Is")

Source: Business Architecture Overview

While working with the CBDI Forum between 2002 and 2009, I developed an approach to business modelling for SOA, which included an emphasis on decoupling the WHAT (what the business does, what the business knows) from the HOW (how the business does). We felt that this decoupling provided the best basis for managing differentiation and integration across complex enterprises, and for achieving appropriate economics of scale and scope.

In my more recent work on Organizational Intelligence, I have sought to further decouple "What the Business Knows" from "How the Business Thinks". Different enterprises operating in the same ecosystem may be able to share a lot of common knowledge and information, but may each arrive at different judgements about What-Is-Going-On.

I shall be presenting the latest version of this schema in my Business Architecture Bootcamp and Organizational Intelligence Workshop, and explore how this schema helps to address a range of practical business problems.

book now Business Architecture Bootcamp (November 22-23, 2011)
book now Workshop: Organizational Intelligence (November 24th, 2011)]]>

#OMG #BAWG The Object Management Group Business Architecture Working Group has identified five key views of a business.

the Business Strategy view ("What the Business Wants")
the Business Capabilities view ("What the Business Does")
the Value Stream view ("How the Business Does")
the Business Knowledge view ("What the Business Knows", "How the Business Thinks")
the Organizational view ("What the Business Is")

Source: Business Architecture Overview

book now Business Architecture Bootcamp (November 22-23, 2011)
book now Workshop: Organizational Intelligence (November 24th, 2011)]]> tag:blogger.com,1999:blog-6106782.post-3374669232907251288 Intelligence Failure at Kodak Richard Veryard http://www.blogger.com/profile/04499123397533975655 noreply@blogger.com 2011-10-16T13:32:00+00:00

mkplantes sees the demise of Kodak as an intelligence failure.

Put yourself in their Kodak leaders’ chairs for a moment and consider the four expectations of a leadership team and, more importantly, consider the speed with which they had to work though all of the expectations:

• Sense what’s going on around you? (“Digital is coming!”)
• Make sense of what you see, hear, and feel (“Film is dying, but we can’t kill it now. It’s too important!”)
• Decide on a course of action (“OMG! Nothing is as big as film is now. Let’s think about this and be careful.”)
• Act on your decisions (“Well, this is a big ship! Hard to change course overnight!”)

Kay Plantes, A sad “Kodak moment” business model failure WTN News 7 October 2011

This is effectively an OODA loop. Dr Plantes identifies a number of possible errors in this loop.

1. Incorrect estimate of the pace of change. "Successful companies often underestimate the speed of industry evolution."

2. Incorrect understanding of the value proposition from the customers' perspective. "People don’t buy film, they use film to capture the pictures they want."

3. Incorrect optimization of the basis of competition - commodity wars.

If it was a strategic error for Kodak to get caught up in a dogfight with Fuji, we should also ask how Fuji is faring? Has Fuji committed the same errors as Kodak, and is it suffering the same fate? Meanwhile, Stuart Henshall compares Kodak with HP: two inventive companies, who "failed time and time again to find a more agile footing". (HP - What's Your strategy? August 2011).

Dr Plantes complains that Kodak was focused on the product rather than the value received by its customers - in other words, a platform strategy. But Kodak has been trying to shift its business model from product to a service-oriented platform for at least five years. In November 2006, an article in BusinessWeek described this transformation, and outlined some of the big challenges then facing Kodak (Mistakes made on the road to innovation, BusinessWeek November 2006). In February 2007, Clayton Christensen and Scott D. Anthony saw the Kodak strategy as an ambitious attempt to implement Christensen's concept of disruptive innovation (Will Kodak's New Strategy Work? Forbes February 2007).

Antonio Perez (who spent much of his career at HP) has been the CEO throughout this period, and has watched the Kodak share price drop from around $25 to less than $1. We may infer that Kodak has failed to overcome the challenges identified by BusinessWeek and Christensen. But why?

What's missing from Dr Plantes' analysis is an appreciation of how these four steps operated as an effective OODA loop, with feedback and learning, rather than merely repetition. In a detailed analysis of Kodak strategy, George Mendes concludes

Kodak is an example of repeat strategic failure – it was unable to grasp the future of digital quickly enough, and even when it did so, it was implemented too slowly under a continuous change strategy and ultimately it did not fit coherently as a core competency.

George Mendes, What went wrong at Eastman Kodak (pdf), TheStrategyTank

There is a great deal on the Internet about Kodak's social media strategy - but it seems to be largely about Kodak marketing communications. Journalist Courtney Boyd Myers (@CBM) invites us to Meet the brilliant and beautiful woman behind Kodak’s social media strategy (September 2011). The woman in question is extremely photogenic and obviously good at self-promotion, but there is nothing strategic in the article. The big strategic error here is to regard social media and content management as a marketing issue, separate from the business model itself. This seems to suggest a lack of joined-up thinking - and ultimately a failure of organizational intelligence.

In 2007, Jacob McNulty thought that that instilling the elements of a learning organization would have strongly contributed to a different story for Kodak’s recent years.

A learning organization is one that learns from its mistakes and successes, spots trends in the market and acts on them by being nimble enough to do so. A culture of learning rewards knowledge sharing which reduces the chances that you’ll be blindsided by something like digital in 2007. Kodak could have presented themselves as a picture company many years ago - whether those pictures are on film or in a file it shouldn’t matter. Part of making that transition would require a company that is ready to learn and develop.

Jacob McNulty, Not a Kodak Moment (2007)

Other sources claim that Kodak is a learning organization. In which case, why has it failed to learn the things that matter?

book now Business Architecture Bootcamp (November 22-23, 2011)
book now Workshop: Organizational Intelligence (November 24th, 2011)]]>

mkplantes sees the demise of Kodak as an intelligence failure.

Put yourself in their Kodak leaders’ chairs for a moment and consider the four expectations of a leadership team and, more importantly, consider the speed with which they had to work though all of the expectations:

• Sense what’s going on around you? (“Digital is coming!”)
• Make sense of what you see, hear, and feel (“Film is dying, but we can’t kill it now. It’s too important!”)
• Decide on a course of action (“OMG! Nothing is as big as film is now. Let’s think about this and be careful.”)
• Act on your decisions (“Well, this is a big ship! Hard to change course overnight!”)

Kay Plantes, A sad “Kodak moment” business model failure WTN News 7 October 2011

Kodak is an example of repeat strategic failure – it was unable to grasp the future of digital quickly enough, and even when it did so, it was implemented too slowly under a continuous change strategy and ultimately it did not fit coherently as a core competency.

George Mendes, What went wrong at Eastman Kodak (pdf), TheStrategyTank

A learning organization is one that learns from its mistakes and successes, spots trends in the market and acts on them by being nimble enough to do so. A culture of learning rewards knowledge sharing which reduces the chances that you’ll be blindsided by something like digital in 2007. Kodak could have presented themselves as a picture company many years ago - whether those pictures are on film or in a file it shouldn’t matter. Part of making that transition would require a company that is ready to learn and develop.

Jacob McNulty, Not a Kodak Moment (2007)

Other sources claim that Kodak is a learning organization. In which case, why has it failed to learn the things that matter?

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book now Workshop: Organizational Intelligence (November 24th, 2011)]]> tag:blogger.com,1999:blog-6106782.post-9195869610798982486 Towards a VPEC-T analysis of Google Richard Veryard http://www.blogger.com/profile/04499123397533975655 noreply@blogger.com 2011-10-10T13:14:00+00:00

#entarch Enterprise architects need to understand values and policies. VPEC-T is an approach that is particularly useful for situations where there are multiple conflicting values and policies, or multiple interpretations of What-Is-Going-On.

In this post, I want to look at Google. Can we infer its values and policies from its observed behaviour (over time).

We may start by asking what events we think Google is paying attention to. Here are some of the events that are available to Google.

1. You search for "XYZ"
2. You skip over the first few items, and click on the third item on the second page.
3. You look at a webpage and then come back to continue your search.
4 You rephrase and clarify your enquiry.

Google is pretty coy on its exact use of these events, but most Google-watchers assume that these events have an influence on its search algorithms and/or its advertising algorithms. In other words, we may presume that Google is generating valuable content from these events.

Google has indulged in a wide range of initiatives over the years, many of which have no obvious line to revenue. But all of them have the potential to generate vast amounts of rich content - much of it related to the observed behaviour of internet users. On this interpretation of Google's strategy, initiatives are dropped, not because they fail to generate revenue but because they fail to generate enough of the desired kind of content. Google is betting its future on building and maintaining this content through powerful positive feedback.

Google's strategy is therefore surprisingly traditional - it involves capturing some territory and defending it against its competitors. Here's an example - Google provides the Android platform to mobile device manufacturers. When Motorola wanted to use Skyhook's voice recognition instead of Google's, Google forced it to fall into line. Daniel Soar argues that this was not because Google executives feared losing revenue but because they feared losing access to an important source of content. As Soar puts it, "Google faced the unfamiliar problem of the negative feedback loop: the fewer people that used its product, the less information it would have and the worse the product would get." (Google has since bought out Motorola Mobility, which presumably resolves some of the trust issues as well.)

Daniel Soar, You can't get away from Google, London Review of Books, Vol 33 No 19, 6 October 2011

Can we understand Google's phenomenal collection and use of data as an example of organizational intelligence? Google is certainly seeking to differentiate each Internet user's experience, as well as integrate across multiple domains (web browsing, email, blogging, voice, video, satnav, and so on). Google already has an army of brilliant engineers as well as an alarmingly large carbon footprint. There is lots of evidence of Google's integrating these resources into one of the most innovative sociotechnical systems on the planet.

(By the way, when I asked Google itself about its carbon footprint, it recommended I look at a recent story in the Guardian (8 September 2011). I can see that Google has been asked this question many times before, because it pops up so quickly as an expected search term. But why should I trust Google's recommendation, and how can I ever discover what newspapers would be recommended to a browser with a different browsing history to mine?)

But a lot of this learning looks suspiciously like first-order learning. So the content gets better, based on better capture of events, but to what extent is there any systematic evolution of policies or questioning of values? There may well be some second-order or third-order learning, but it's not easy to see from the outside. There is also an important question about the relationship between Google's own ability to learn from its accumulated content, and Google's ability or willingness to provide a rich platform for learning by others in its ecosystem - in other words, a broader notion of collective intelligence.

I wonder if there are any lessons for other organizations? Sometimes firms like Amazon, Apple, Facebook and Google (Eric Schmidt's Gang of Four) seem pretty far removed from most other organizations, but their platform strategies and operating patterns will surely become increasingly relevant in other sectors. A traditional retailer may now collect and analyse a much larger quantity of data about its customers' behaviour than ever before, even if this is still several orders of magnitude less than what Google does. A traditional telecoms or media company may now see itself as a platform business in a multisided market. Therefore instead of seeing Eric Schmidt's Gang of Four as impossibly remote and mysterious organizations, populated by unbelievably talented and creative engineers, we should start to think of them as harbingers of the enterprise of the future.

See also my post Google as a Platform (not)

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book now Workshop: Organizational Intelligence (November 24th, 2011)]]> tag:blogger.com,1999:blog-6106782.post-2922855710052354301 Fast data - speed over precision? Richard Veryard http://www.blogger.com/profile/04499123397533975655 noreply@blogger.com 2011-06-22T11:12:00+00:00

bmichelson posts a piece on an HP website called Fast Data: Speed over precision for better decision-making (21 June 2011), summarizing a recent interview in MIT Sloan with Ali Riaz and Sid Probstein of software company Attivio.

Riaz and Probstein are not claiming that better decision-making is just about speed: it also requires a continuous refinement loop, including rethinking one's premises. In other words, we need multiple feedback loops at different speeds, to handle different levels of complexity. As I pointed out in my piece on TIBCO's slogan Two Second Advantage, although simple decisions can be taken quickly, complex decisions need what I call "time for understanding".

One of the architectural challenges of organizational intelligence is to coordinate a complex array of sense-making and decision-making processes operating at different characteristic tempi, and to maintain a proper balance between the very fast (reactive) and the comparatively slow (reflective). Probstein identifies Amazon.com as a successful exemplar.

It is common for technologists to make a fetish of speed, but business effectiveness and organizational improvement need "time for understanding" as well. Riaz and Probstein appear to understand this, and I look forward to seeing how this understanding is supported by their software.]]>