When it comes to finding nuggets of insight hidden in sheets of data, many eyes are always better than one!

Developed jointly by IBM Research and the IBM Cognos software group, IBM Many Eyes is a community that brings together academics, visualization experts, practitioners, and tinkerers like me, to collaborate and share insight through data visualization techniques. Free to use, you can upload your own data set with a simple copy and paste from Excel, or leverage data sets that others in the community have shared – and within minutes be creating and exploring different ways to visually represent and explain trends within the data.

Here’s one I prepared earlier

Armed with my statistics on US Unemployment from the Guardian Data Blog, I was able to very quickly recognize the US states with highest unemployment in December, 2012 (ie. California). Using advanced visualization techniques, I could also cross-reference the number of unemployed with the % change in unemployment since 2009 – showing that whilst California currently has the highest unemployment (largest block), it is in decline (blue). Whereas New York has both high unemployment (large block) and is on the rise (orange).

Using that same data set, I very quickly selected a few different visualizations to show the geographical representation of unemployment across the US….

… as well as states that were above (increasing unemployment) and below (decreasing unemployment) the line.

But don’t take my word for it, head to the new IBM Many Eyes platform and try it for yourself. You’ll even find my US Unemployment dataset uploaded to get you started!

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But it wasn’t until this year when I really understood the true power of social media to collaborate and share within an organization.

IBMers are not new to the concept of internal social media. For decades, techies have been connecting and blogging on internal social platforms on a range of topics and interest areas, collaborating and sharing technical knowledge across teams, specialties and countries. For me, it was a source of troubleshooting, but never part of my day-to-day activities.

That is, until Ginni Rometty – our CEO – joined the revolution.

Suddenly, Executives and Leaders across IBM are sharing valuable knowledge about their strategies and goals for the year, about the hopes and aspirations for the company, and asking for our opinions on how we can take IBM to the next level. With Executives and Leaders, came managers and individual contributors, and now we have the momentum needed to generate active conversation that contains enough breadth and depth to interest any business or technical colleague in the company.

More than just a source of collaboration, our social platform has become a hive of activity, with updates, shares, tags and likes spreading valuable knowledge across the half-a-million employees. Digital information has new life, with valuable, relevant and recent information becoming dramatically easier to find.

Instead of logging support tickets and wasting endless hours on internal and external search engines, employees are posting questions and troubleshooting tips in status updates to efficiently find the answers they need.

Strategic thinkers are posing though-provoking questions to capture the knowledge and experience from teams spread across business groups, specialties and countries, providing them an avenue to progress key business decisions in shorter time frames with more context than ever before.

It is no longer just a platform for technical knowledge sharing, but a core part of the way we communicate and collaborate each and every day.

If you haven’t investigated the impact social business can have within your organization, what are you waiting for? Connect now!

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The world of social business has come a long way since then, and now is the time to stop making rash decisions, collaborate with your network of peers and experts, listen to the collective advice and experience of the entire team/organization, and make more informed, collaborative business decisions.

Organizations have a wealth of knowledge and expertize.  One of the greatest challenges we face is knowing how to capture that knowledge to share more widely across the organization, and train new employees as they enter the workforce.

Take for example a new employee who needs to make an important business decision for the first time.  Even with analytical insight at his/her fingertips, they may not know the right way to interpret that information, and the range of different options they have to choose from.  A senior employee in the same situation has a wealth of expertize and experience to use in addition to the analytical insight – so how do we capture that expertize to help everyone in the team make better decisions?

By combining analytics with social collaboration, of course!

Utilizing this unique integration point between IBM Cognos Business Intelligence and IBM Connections, the new employee can create a collaborative decision activity directly from their reporting dashboard, where they can start to capture dialogue and advice from more experienced members in the team.

Not only does this help them make a better decision today, the process of capturing advice and dialogue around a key decision can then be re-used by others in the team as they learn and make similar decisions in the future.

Collaborative-decision making allows employees to start a dialogue around a specific piece of insight, leverage expertize and experience from across the team, and ultimately make better business decisions.

Are you ready to stop, collaborate and listen?

—

On a more personal note, I never thought I’d quote Vanilla Ice under professional circumstances.  Who knew he had such a forward-looking view of collaborative decision making.

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This week has been a crazy week for me – analyzing trends across our growth markets and busily setting our strategy for 2013. Which unfortunately meant I missed the controversial game between Sloane Stephens and Victoria Azarenka at the Australian Open yesterday. I was curious to know how the public had responded to her so called “medical time out” – but had no idea what time the “incident” had occurred.

Fortunately, IBM and Tennis Australia have been monitoring and analyzing public sentiment towards all of this year’s players throughout the course of the event, so it didn’t take long to find out!

A quick visit to the Fan Center at the Australian Open website gave me access to a wealth of insight into which players have been mentioned most over the course of the event, and how much of the social chatter was positive or negative towards them. Looking up profiles for both Stephens and Azarenka gave me the answer I was looking for.

Over the course of the game, both players had been receiving steady mentions on social media, up until 5:15pm when Azarenka left the court for a suspect medical time out right as Stephens broke serve to stay in the match. Whilst comments about Stephens were 87% positive and peaked at 2,253 around the time she left the court, comments about Azarenka peaked at 3,052 at 5:29pm as people started to respond negatively to her extended time out – almost 41% of comments at the time contained negative sentiment.

It’s no surprise that in the hours following the game, as she’s responded to the media claims of faking the need for medical treatment, she’s continued to receive a mix of both positive and negative sentiment.

Some might argue that any publicity is good publicity, and this increased focus on Azarenka can only serve to increase her public profile even further. Others might simply look at the data captured from social media over the course of the event and conclude that you don’t need fame to be loved. Whilst this event gave Azarenka her peak focus in social media, Stephens has received steady positive sentiment throughout the course of the event, peaking at 13,087 comments, of which 84% was positive.

After all is said and done, we’re here to watch the tennis. So why do we care about sentiment in social media?

Companies today have a wealth of information available to them in social media to understand the community’s sentiment towards marketing campaigns, brands and products – or in this case, athletes. This valuable insight helps them make more informed decisions about where to invest their money - selecting new campaigns, product lines, or sponsorship deals.

Think about it – would you buy a product from the world #1 player who is suspected of cheating to get there? Or a product from the world #2 player who you admire, respect, and love to watch on court?

Would you invest in Azarenka or Stephens?

That’s the answer many companies are trying to understand – and with IBM Social Media Analytics, they can capture and analyze millions of tweets, blogs and forum comments to better understand community sentiment and invest their money to maximize returns.

Keep your eye on the Australian Open Fan Center to see how the community responds to Azarenka in the finals, and other players left in the competition.

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There’s only one thing better than being a Melbournian during the Australian Open, and that’s being a Melbourne-based IBMer.  With a 20-year partnership between Tennis Australia and IBM, I have every excuse to drop tools and hit the courts, all in the name of research

While other sponsors have come and gone over the past few decades, IBM has remained a constant logo uniquely positioned on the game clock.  And whilst the location of the logo hasn’t changed, the way in which IBM technology supports, contributes and enriches the event has.  From the back office and game operations, to match statistics and providing predictive insight in the hands of officials and supporters alike, 2013 is going to be an exciting event for the players, organisers and fans alike.

Leading the fan experience is IBM Slam Tracker – a real-time stats and visualisation platform that leverages IBM‘s predictive analytics technology.  Over eight years of Grand Slam data (over 41 million data points) is analysed to identify patterns in player styles and performance, and key performance indicators – what players need to do to succeed in a match.  During each match, each player’s performance will be measured against their KPIs and updated in real-time on AustralianOpen.com providing you, me and screaming fans around the world with a deeper level of insight as the match unfolds.

Over the next few weeks I’ll be glued to my TV set and posting more information about how IBM technology supports this fantastic event.  In the meantime, here’s a few previous posts on analytics in the world of sport to get you started.

• IBM Cognos Insight calls Time on Wimbledon Tweets
• Analytics in Sport: Making the Invisible, Visible
• Predicting Pro Players

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And he’s been taking a few tips from the local energy industry on how to turn insight into action.

Within Santa’s wider executive team, he has employed a secret group of specialist Elves. These elves manage the day to day monitoring of The List using specialist tools like Santa’s Perfect Santa Sack (SPSS). Using SPSS, the team is able to uncover hidden trends and patterns in a child’s behaviour and predict whether they are likely to end up on the naughty or nice list, directing the toy factories and the Elf team that manages information sent out to children.  This insight is near real time, forward looking, and lets them know how many toys to build and plan for the Big Day.

The innovation doesn’t stop there.  What once was an organisation run on gut feel (and a big one at that), Santa and his team now make key business decisions based on analytical insight.

The factory Elves have put in a sophisticated system for keeping an eye on their toy manufacturing machines. Lots of sensors and monitoring systems feed information back to their central system, aggregating, summarizing and visualizing it in a way Elves can understand. With SPSS, the Elves can now better predict when toy manufacturing assets will break and can take preventative action ahead of time.  The children will get the toys they want!  (And more importantly, Mum and Dad will get a day of peace)

Even the Elves on the front line have been installing clever monitoring devices. Instead of waiting for parents to send Santa an email update on their children, they can see how the kids are behaving every half an hour.  Imagine how great that is going to be for Mum and Dad!

With a comprehensive analytical platform deployed through the back and front office, when Santa’s team sit down to talk shop, everyone knows what is going on in near real time, and everyone is clear on what decisions need to be made to drive greater value. New initiatives like the Child Performing Perfectly (CPP) program can be rolled out to homes and the children can be rewarded for their good behaviour in real time – they don’t have to wait until Christmas day.  And parents can even monitor them on their fancy new phones.

The heart of this transformation in the culture of the North Pole is data translated into insight. The North Pole is being transformed into a dynamic team with a clear understanding of value for all, the toy factories are running perfectly, the optimum volume of toys are going out the door (or being brought from other factories), and the children are getting real time information to help them stay good.

Santa is ahead of the game, and with his trusted advisers can keep an eye on the operation from his office.  When he sees change coming he can get the whole North Pole team on board and aligned because everyone understands the most important thing at the North Pole – making sure that every good little boy and girl gets the toy they want for Christmas.

Whether you’re producing and distributing energy, or toys, decisions based on analytical insight at every point of the distribution network provide the opportunity for cost-effective operations and happy customers.

Merry Christmas to everyone – to learn more about how SPSS is transforming Santa’s operations, or an industry a little closer to home, take your local IBMer out for some egg nog and holiday cheer!

Nigel Broomhall (@sustaiNZ) is the Industry Solutions Executive Utilities, and has worked in the energy industry for more than 10 years. He is a passionate advocate for leveraging technology to assist the industry and has led initiatives in smart grid, electric vehicles and carbon offsetting.

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See my guest post on A Smarter Planet Blog to find out.

And Happy Holidays to all!

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You asked for a way to derive predictive insight without the need to acquire statistical expertise and invest capital.  IBM Analytic Answers with predictive analytics offered as a service, hosted via a cloud subscription to provide predictive insight on your data.

In my previous post, “Business Analytics: Design Outcomes, Not Solutions“, I talked about some of the amazing results that had been achieved using predictive analytics.  Payback in 2 months; ROI of 624%; annual net benefits of $3.8 million – these results are spectacular for the big end of town.  Imagine the impact of achieving this level of value to an SMB!

But let’s face it, in today’s financial turmoil it’s getting harder and harder for SMBs to find the level of investment required to compete with the big end of town.  The upfront capital expense, dependence on small IT teams, need for analytical expertise, and time to value are all key challenges that must be overcome to successfully adopt innovative technology.

Which is where IBM Analytic Answers comes in – a portfolio of cloud-hosted solutions, available on a subscription basis, that deliver actionable predictive information direct to the line of business.  This removes the barriers to adoption, with no up-front investment required, minimal IT involvement, no analytical skills required, all leading to a faster time to value.

Most importantly, IBM Analytic Answers is tailored to each and every client’s business needs with models built from your data, coupled with IBM’s deep analytics expertise, supported by scalable, high performing analytical infrastructure.

The big end of town better watch out – now that IBM is backing the little guy too, it’s game on!

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In Shyam Sankar‘s TED presentation “The rise of human-computer cooperation“, he so rightly points out that “brute computing force alone can’t solve the world’s problems“.  Least of all, the world’s analytical problems.  Which is why Man and Machine must team to drive greater insight.

One of my favorite topics to discuss with clients is predictive analytics.  The value of predictive insight to any business is not only profitable, but highly trans-formative in the way the business thinks and acts.

Even today, confusion still exists over what predictive analytics is and how it is used by the business – with many still fearful of the “black box” that is going to replace their employees and start making key business decisions for them.

This couldn’t be further from the truth.

Predictive analytics is the process of using advanced mathematical algorithms to solve complex business problems, such as identifying hidden trends and patterns in historical information to identify claims that are most likely to be fraudulent, or analyzing customer buying behaviour to identify marketing segments based on their propensity to purchase specific items in the future.

Predictive analytics cannot be applied by either Man or Machine alone.  Humans lack the ability to scale and compute large volumes of data.  Machines lack our ability to think creatively and approach problem solving in a non-linear fashion.  Together, Man and Machine make the perfect team – with Man setting the goals, formulating the hypotheses, identifying the criteria, and iterating the process, whilst Machine performs the computation and executes routine work to scale.

Sankar points out that to make this team operate efficiently and be successful, you have to design the human into the process, ensuring the interface between Man and Machine is void of “friction”.  Predictive analytics is not a question of finding the right algorithm, but rather the right symbiotic relationship between computation and human.

Which is why clients that have turned to IBM SPSS for predictive analytics are so successful.

SPSS Modeler, IBM’s client for designing the workflow required to derive predictive insight, is geared to reduce the “friction” between Man and Machine.  Using a simple, drag-and-drop interface, Man can set the goals of the predictive model, formulate the hypothesis to test, evaluate the results of the model, and employ the Machine to execute the computational work, all without writing any code.   This seamless, configurable process leads to shorter and more efficient model builds which can be further refined and improved in a short space of time – increasing the performance of the predictive model and the Man-Machine team.

When it comes to successfully harnessing the power of predictive analytics: the better the process, the less the friction, the more successful the outcome.

Sankar‘s full presentation is posted below for reference.

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Have you ever found yourself in the situation where you’ve had to make an important business decision, IT have given you a 100-page report (that took months to develop) and yet you can’t find the one answer you need to make the right decision?

That’s the situation my mother found herself in at the McDonald’s drive through – okay, so not exactly an “important business decision”!  Driving up to place her order she was overwhelmed with the four-boards of menu items with extensive lists of burgers, drinks, desserts, options, sauces, coffees, non-burger burgers, kids meals…argh!

I am a technology girl, and this is a technology world.  As such, we expect to only be presented with the information that is relevant to us – both in the boardroom and on the boardwalk.  This is what my mother should have experienced…

On driving into the drive-through lane, the security camera scans her license plate and looks up her customer record.  By the time she gets to the order box, the analytical-driven menus have already analyzed her historical purchases and predicted what she is most likely to purchase today.  She is presented with the top 10 items she orders most regularly, including the McHappy meals she often orders when she has her grand-kids in the car.  The menus also make a recommendation on something she hasn’t tried before, but based on her previous purchase, is most likely to enjoy.  They also offer her a discount on a menu item she is unlikely to purchase at full price, in the hope to increase the wallet share on this particular transaction.  On seeing this personalized menu, she places her order quickly and stress-free and moves to the next window.

Using analytical-driven insight, she is presented with only the information she needs to make the right choice, increasing the probability of a successful outcome.

This mentality should be prevalent in all aspects of decision-making.  To maximize the potential for a successful outcome, decision makers need to be presented with the right information at the right time – something which the IBM Business Analytics portfolio has dedicated its existence to achieving.

It’s through personalization of the right information that we can achieve better business decisions and deliver real McResults.

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