As seen on a plaque at Scienceworks in the House Secrets exhibit.

James Dewar invented the vacuum flask in 1892 to keep laboratory gases cold. Twelve years later, Reinhold Burger manufactured the Thermos to keep our picnic drinks hot.

A nice demonstration of the third of Peter Drucker’s seven sources of innovation.

Innovation based on process need.

Or, put another way, James Dewar scratched an itch; though he did play Edison to Reinhold Burger's Sameul Insull.

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As seen on a plaque at Scienceworks in the House Secrets exhibit.

A thrifty tea merchant from New York named Thomas Sullivan is credited with inventing the first tea bag in 1908. Looking to save money, Sullivan reportedly distributed small samples of tea in silk bags instead of little metal tins. It wasn't until after he saw restaurant and coffee shop owners brewing the entire bag of tea leaves that he realized the potential of his actions.

A nice demonstration of the first, and most valuable, of Peter Drucker’s seven sources of innovation.

The unexpected. The unexpected success, failure or outside event.

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As seen on a plaque at Scienceworks.

The penicillin mold was a pest, not a resource. Backteriologists went to great lengths to protect their bacterial cultures against contamination by it. Then in the 1920s, a London doctor, Alexander Fleming, realized that this "pest" was exactly the bacterial killer bacteriologists had been looking for – and the penicillin mold became a valuable resource.

A nice demonstration of the first, and most valuable, of Peter Drucker's seven sources of innovation.

The unexpected. The unexpected success, failure or outside event.

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I stumbled onto a somewhat interesting post over at HBR, which talks Garry Kasparov's ideas in the business world. This is actually quite a relevant pairing, though an old one in the tradition of human-computer augmentation.

The idea a simple one, which takes far fewer words to express than the article took.

Use information technology to augment users, rather than replace them.

IT is good at lot of tasks, and less good at others. People, too, have their strengths and weaknesses. What's interesting is that computers are weak where people are strong, and vice-versa. Computers excel as appliers of algorithms with huge memories and an attention to detail; people are powerful, creative problem solvers who have trouble thinking of four things at once and like coffee breaks. Why not pair the two, and get the best of both worlds.

Rather than replace the users, why don't we use technology to automate the easy (for technology) 80% of what they do. (This is something I've written about before.) In the chess example, the easy 80% is providing the user with a chess computer for the commoditized solution space search, allowing them to focus on strategy. The performance improvement this approach provides can create an significant competitive advantage. As Garry Kasparov found, even a weak user with a chess computer can be impossible to defeat, by human or computer.

This then provides us with two options:

1. Take the improvement as a saving by reducing head count.
2. Reinvest the improvement by providing our users with more time to focus on the hard 20%.

(I must admit, i much prefer the later.)

If we continue to focus on automating the next easy 80%, we've created a platform and process for continual business optimisation. (Improvements in search efficiency would simply be harvested when appropriate to maintain parity.) Interestingly, this is one of only two sources of a sustainable competitive advantage available to us today.

The competative advantage with this approach rests with the user, in the commonplaces, the strategies, they use to solve problems. By reifying the easy 80% these strategies in software (processes and rules) we are moving some of the competitive advantage into the organisation with it can leveraged by other users. By continually attacking the easy 80% of what the users are doing, we are continually improving our competitive position. We could even sell our IT platform (but not the reified problem solving strategies) to our competitors — commoditzing the platform to realise a cost saving — without endangering our competitive position, as they would need to go through the same improvement and learning process that we did, while we continue to race ahead.

Now that's scary: as long as we keep improving our process, our competitors will never be able to catch us.

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I've already written about why I think private clouds can be a good idea. Similar arguments can be made for SaaS, and then some. A friend and I did the email-ping-pong thing and ended up with a (shortish) list of reasons why to go with a SaaS solution over an traditional on-premises solution.

• OPEX rather than CAPEX cost. The CAPEX gulp is minimised, and the ongoing costs are tied to your own operational cost (head count, etc).
• Faster provisioning. SaaS is can be up to 90% faster to deploy than on-premises solutions. (Weeks/months rather than months/years.)
• No more upgrades. You're always on the latest version, and new features are roll out organically rather than every few years as part of a change management process.
• More focused vendor and community support. As there is only a single version in play, support efforts from the vendor and user community are focused on the version that you're using. This also avoids the problem of getting left behind on a stale and unsupported platform (been there, done that, and have the scars to prove it).
• SaaS provides a platform that scales organically with our organization. You're not required to invest in additional hardware, software, and provisioning processes, letting your business focus on the business.
• Reduced IT involvement. IT resources can focus on specific business problems rather than the care and feeding of the system.

Any more?

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Death, taxes, and now, change, are the eternal verities. As I said in another post:

The pace of change has accelerated to the point that everyone’s challenge, from Pre-Boomers and Baby Boomers through Generation Y to Generation Z, is how to cope with significant change over the next ten years. If we are, as some predict, moving to an innovation economy, then it is the ability to adapt that is most important. Those betting their organisation on a generational change will be sadly disappointed as no generation has a monopoly on coping with change.

While the youngest generation (whichever that is at a particular point in time) might have the advantage of coming unencumbered to the new ways of working, every generation has a unfortunate habit of treating what they learnt in their formative years (~24) as dogma once they hit their late 20s. Social research has shown that most people’s interest in novel ideas or experiences peaks around the mid to late 20s. (Tell me your favourite band and cuisine, and I'll tell you what decade you grew up in.) Or, put another way, 24–28 might have the advantage in a rapidly changing world, but once you grow out the top of that age bracket you’ll find yourself at the disadvantage.

However, as with all gross generalisations, and the exceptions are more interesting than the rule; in this case the commonalities between groups are usually stronger than the differences between them. Research like Forrest’s Groundswell show that its more productive to think in terms of personality types.

I prefer to focus on getting stuff done, and ensuring that each and every stakeholder has the tools and support they need to get their job done. This is not a static thing either, something we do once for each stakeholder, as someone's needs and preferences can change month-by-month, week-by-week, day-by-day or even minute-by-minute.

And this is probably the most important mega-trend we’re seeing emerge at the moment: the drive to continually personalise communication/products/services/tools for each and every individual, rather than trying to divide people into coarse-grained, and increasingly unproductive, demographic groups with predefined needs. If you're managing change, then you're still thinking in terms of a static work/home environment that needs to be transformed (however regularly). If you're managing personalisation, then you're focused on creating a continually optimised environment for all your stakeholders, ensuring that they have the information and tools they need at that moment. Change isn't an enemy that should be managed—its a tool to help you achieve, and sustain, peak performance.

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Google (well, James Hamilton) has weighted in on the question of private clouds. As expected from a large cloud provider, James takes the position that private clouds make no sense. His reasoning is straight forward: private clouds will never have the scale of public clouds, therefore private clouds can never achieve the same price point as their public brethren. Ergo, there's no point in building private clouds.

As I've pointed out before, there's a lot more to cloud than simply reducing costs. The biggest benefit is probably the agility that cloud can bring to your IT estate, leveraging a cloud platform's ability to codify and automate many of the management practices and create a target platform that can work across a range of deployment options, as well as streamlining hardware provisioning. Companies are also increasingly having to deal with the realities of political boundaries, a situation where the best technical solution might not be acceptable due to legal requirements (such as privacy legislation). Developing a private cloud can be a sensible move in this context.

Of course, if you want to compete purely on cost then private cloud will never hit the same price point as public cloud. But this misses the point that for many companies IT flexibility/agility is more important than cost.

Note: I was going to post this as a comment on James' post, but comments appear to be broken.

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Garther have suggested that by 2012, 20% of companies will own no IT assets. At the same time we have Forrester predicting a boom in IT (but not your father's IT). I think both of them are right, and what we're seeing is a breaking of the old covenant between business and the IT services industry (which includes internal IT departments). The old relationship was founded on the development and maintenance of IT assets (networks, applications, desktops ...). The new one will be founded on something different. The new IT industry is going to be a different beast (i.e. no more strategic transformation or infrastructure projects), and we'll need to radically reconfigure our organisations if we want to play a part.

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We're drowning in information, as I've written about before, both in the context of Business Intelligence and Innovation (whatever that is). An interesting blog post by Tim Kastelle over at his Innovation Leadership Network takes a somewhat contrarian view, that we have always had this information overload problem. Quoting Stowe Boyd, he points out:

I suggest we just haven’t experimented enough with ways to render information in more usable ways, and once we start to do so, it will like take 10 years (the 10,000 hour rule again) before anyone demonstrates real mastery of the techniques involved.

The problem is that our current tooling for information processing is not up to the task at hand. Unfortunately Tim, like most of us, is still trying to find the best way to managed the information load pressing down on us.

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There's an interesting post over at Bruce Schnier's blog where he discusses where security did, and didn't, work with the Christmas underwear bomber incident. As is his usual inclination, he points out that the threat wasn't new, security (on the whole) worked, and, of interest to us, the fact the more information would not have helped prevent the threat.

After the fact, it's easy to point to the bits of evidence and claim that someone should have "connected the dots." But before the fact, when there millions of dots – some important but the vast majority unimportant – uncovering plots is a lot harder.

This is a lot like the challenge we've been talking about under the banner of The value of information. How do we make sense of weak, conflicting and volumous signals we see in the environment outside our business, fuse this with strong signals from data inside the business, and create real insight? Granted, sometimes we're aware of the signals (or at least the shape of their outline) we need to go looking for, much like Tesco's decision to integrate weather forecasts and historical till information to predict customer demand. In other circumstances, we're not so sure what we're looking for. The business equivalent of predicting (and responding to) the underwear bomber might be managing exceptions in a complex, global supply chain, countering a competitor's new product launch, or supporting a social case worker dealing with a unexpected crisis in a client's domestic situation.

It's tempting to create counter measures – prescriptive workflows designed to resolve a problem – to each of these scenarios on a case-by-base basis. Or even just throw up our hands and continue with the tribal processes of old. But, as Bruce points out, this doesn't work. The challenge with taking action against specific threats is that the terrorist will simply use a new tactic next time, or you'll be confronted with yet-another situation. Soon you'll have overloaded your knowledge workers with exception scenarios which only address yesterday's problems. You've started an arms race which you cannot win.

Bruce's solution, in the context of security, is to integrate information into an operational decision making framework which wards against generic attacks.

What we need is security that's effective even if we can't guess the next plot: intelligence, investigation and emergency response.

This prompts me to think of two things:

First, we might need to add third dimension to that figure from Inside vs. Outside: Precision, to compliment Inside/Outside and Information Age. (Here, the engineer in me is going to split hairs over the definitions of focus, precise and accurate.) This new dimension captures how precise our need is. The Tesco example from above prefers precise signals, signal which communicates a single message. The exception manager might require imprecise signal, a derivative communicating a generic message aggregated generated by correlating a number of (in)precise signals. (A note of caution though, is to remember the recent impact of derivatives on the global financial markets.)

Second, we might want to rethink about how we conceptualise and use information information in our business. We currently have a very linear view, with information generation and consumption tightly connected to the stages of our value chain. It would be interesting to see how some of the ideas and frameworks behind the value of information could be fused with a decisioning framework like OODA. This would provide a tool to simplify the (potentially too complex) value of information framework, and realize it in operational work practices.

I'm not sure about the first point, but I expect the second will be fertile ground for further investigation.

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I pointed out the other day, that we seem to be at a tipping point for BI. The quest for more seems to be loosing its head of steam, with most decision makers drowning in a sea of massaged and smoothed data. There are some good moves to look beyond our traditional stomping ground of transactional data, but the real challenge is not in considering more data, but to consider the right data.

Most interesting business decisions seem to be a synthesis process. We take a handful of data and fuse them to create an insight. The invention of breath strips is a case in point. We can rarely break our problem down to a single (computed) metric, the world just doesn't work that way.

Most business decisions rest on small number of data points. It's just one of our cognitive limits: our working memory is only large enough to hold (approximately) four things (concepts and/or data points) in our head at once. This is one reason that I think Andrew McAfee's cut-down business case works so well; it works with our human limitations rather than against them.

I was watching an interesting talk the other day — Peter Norvig was providing some gentle suggestions on what features should be beneficial in a language to support scientific computing. Somewhere in the middle of the take he mentioned the Curse of dimensionality, which is something I hadn't thought of for a while. This is the problem caused by the exponential increase in volume associated with each additional dimension of (mathematical) space.

In terms of the problem we're considering, this means that if you are looking for n insights to a problem in a field of data (the n best data points to drive our decision), then finding them becomes exponentially harder for each data set (dimension) we add. More isn't necessarily better. While the addition of new data sets (such as sourcing data from social networks) enables us to create new correlations, we're also forced to search an exponentially larger area to find them. It's the law of diminishing returns.

Our inbuilt cognitive limit only complicates this. When we hit our cognitive limit — when n becomes as large as we can usefully use — any additional correlations can become a burden rather than a benefit. In today's rich and varied information environment, the problem isn't to consider more data, or to find more correlations, its to find the best three or features in the data which will drive our decision in the right direction.

How do we navigate from the outside in? From the decision we need, to the data that will drive it. This is the problem I hope the Value of Information discussion addresses.

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As I've mentioned before, I would like a nice, clear, crisp definition for mash-up. A definition which captures the benefits that mash-ups can bring, rather than detailing a collection of tools, technologies and standards that we happen to find interesting at the time. For me, this is the TQM argument of fusing data and process to eliminate unnecessary decisions—make-work or swivel chair integration—to create a more efficient and effective work environment.

It's Just a Bunch of Stuff That Happens has done a brilliant job of capturing this visually (included below). I like the usability aspect this highlights. A mash-up's focus is cross-application usability—removing the annoyances of dealing with separate information sources. We could simply take these sources and squish them up against the glass, delivering the content into iGoogle or NetVibes gadgets. But what those original push-pins on a map mash-ups did was improve the usability of these information sources by eliminating the decisions required to navigate across them. Just as Apple did with the iPod and iPhone, eliminating or fusing functions to eliminate the (unnecessary) decisions required to navigate the overly complex and confusing interfaces of the mobile phones that came before them.

iGoogle and NetVibes are the Symbian to a mash-up's iPhone.

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This attitude completely misses the point. The art of business is not to take the time to make a perfect decision, but to make a timely decision and make it work. Business opportunities are often only accessible in a narrow time window. If we miss the window then we can't harvest the opportunity, and we might as well have not bothered.

Building the big, scalable perfect solution in the first place might be more efficient from an engineering point of view.  However, if we make the delivery effort so large that we miss the window of opportunity, then we've just killed any chance of helping the business to capitalise on the opportunity. IT has positioned itself as department that says no, which does little to support a productive relationship with the business.

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Definitions are a good thing, as they help keep us all on the same page and make conversations easier. However, what often starts our as a powerful concept—with a clear value proposition—is rapidly diluted as the original definition gets pulled in different directions.

Over time, the foundation of a term's definition moves from the outcome it represents (and the benefits this outcome provides), taking rest on the means which the original outcome was delivered, driven by everyones' desire to define what they are doing in relation to the current hot topic. Next, the people who consider it to be just a means, often start redefining the meaning to make it more inclusive, while continuing to claim the original benefits. We end up selling the new hype as either means or goals or any half-hearted solution in between – and missing the original outcome nearly completely. 

The original mash-ups were simple things. Pulling together data from two or more sources to create a new consolidated view. Think push-pins on a map. Previously I would have had to access these data sources separately—find, select, remember, find, select correlation, click. With the mash-up this multi-step, and multi-decision workflow is reduced to a single look, select, click. Many decisions became one, and I was no longer forced to remember intermediate steps or data. 

It was this elimination of unnecessary decisions that first attracted many of us to the idea of a mash-up. As TQM, LEAN, et al tell us, unnecessary decisions are a source of errors. If we want to deliver high quality at a low cost (i.e. efficient and effective knowledge workers) then we need to eliminate these decisions. This helps us become more productive by spending a greater proportion of our time on the decisions that really matter, rather than on messy busy work. Fewer decisions also means fewer chances for mistakes.

Since those original mash-up solutions, our definition of mash-up evolved. Todays definitions are founded on the tools and techniques used to deliver a modern web-based GUI. These definitions focus on the technology standards, where the data is processed (client vs. server), standards and APIs, and even mention application architectures used. Rarely do they talk about the outcome delivered, or the benefits this brings.

There's little difference, for example, between some mashups and a modern portal. We can debate the differences between aggregating data on the client vs. the server, but does it really matter if it doesn't change the outcome, and the difference is invisible to the user? The same can be said for the use of standards, APIs used, user configuration options, differing solution architectures and so on.

The shift to a feature-function base definition has allowed the product vendors and analysts of seize control of our definition, and apply it to the next generation of products they would like us to buy. This has diluted the term to the point that it seems to cover much of what we've been doing for the last decade, and many of the benefits ascribed to the original mash-ups don't apply to solutions which fit under this new, broader church.

Modern consumer home pages, such as iGoogle and NetVibes for example, do allow us to use desk and screen real estate more effectively–providing a small productivity boost–but they don’t address the root of the problem. Putting two gadgets on a page does little to fuse the data. The user is still required to scan the CRM and order management gadgets separately, fusing the data in their head.  Find, select, remember, find, select correlation, click rather than a single look, select, click.

The gadgets might be visually proximate, but we could do that with two browser windows. Or two green screens side-by-side. The user is still required to look at both, and establish the correlation themselves. The chair might not swivel as much as with old school portlets, but eyeballs still do, and we are still forcing the user to make unnecessary decisions about data correlation. They don't deliver that eliminate unnecessary decisions outcome that first attracted us to mash-ups.

The gold standard we need to measure potential mash-ups against is the melding of data used to eliminate unnecessary decisions. This might something visual, like push-pins on a map or markup on an x-ray. Or it might cover tabular data, where different cells in the table are sourced from different back-end systems. (Single customer view generated at the user interface.) If we fuse the data, building new gadgets which pull data attributes and function into one consistent view, then we eliminate these decisions. We can even extend this to function, allowing the user to trigger a workflow or process that make sense in the view they are presented, but with no knowledge of what or where implements the workflow.

We need a definition for mash-ups is that captures this outcome. Something like:

A mash-up is a user interface, or user interface element, that melds data and function from multiple sources to create one single, seamless view of a topic, eliminating unnecessary decisions and actions.

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[Vizzini has just cut the rope The Dread Pirate Roberts is climbing up] 
Vizzini: HE DIDN'T FALL? INCONCEIVABLE. 
Inigo Montoya: You keep using that word. I do not think it means what you think it means.

The Princess Bride

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Robert Connolly published a brilliant white paper in February 2008 which is a great example of this in action. Showing a a strong sense of realism and common sense, Robert pulls apart the Australian film industry and, using clear and direct language, provides a set of good recommendations on how we might address the challenges the Australian film industry faces.

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