Competing on analytics


There’s a great article in the Financial Times describing the use of data in football. The focus is on the Everton manager David Moyes who is the likely candidate to replace Sir Alex Ferguson at Manchester United. But for this post, the interest is in how Moyes used data to improve performance.

The background into the club highlighted how it is one of the most under-funded football clubs in the Premier league yet consistently out-performs wealthier rivals. Data is used in a range of different ways, but the value has come from when it is personalised to the individual situation.

Rather than have a signature style of game plan like most football managers, Moyes analyses data to determine the specific tactics for each game. If the data isn’t available, the in-house team gathers it from video coverage of football matches. Capturing statistics about the performance of each player and the movement of the ball. Identifying patterns but also putting the statistics within context to help predict behaviours for the next game.

The same approach is applied to talent recruitment. The largest signing at Everton to date has been for Belgian player Marouane Fellaini. Everton needed a specific style of player to replace one who had been sold on. Fellaini was not well known at the time, short of being sent-off early during the 2008 Olympics. From the article:

There were few match stats for Fellaini, because there was then no data available for Belgian league matches. And so Everton watched videos of him to compile their own stats, using key performance indicators that seemed relevant.

The tactics worked and, as a result of his performances since joining Everton, he is expected to be transferred this summer for a far bigger fee than Everton paid. It’s a great article – link at the end of this post to read in full.

Using data to improve performance is not a new science. If you want to read up on the subject, there are plenty of books to choose from. One of my preferred is ‘Competing on Analytics’. (Yep, the title of this post is not the most original.) But I am surprised how little analytics are used to innovate within business, compared to sport. The difference is in the level of personalisation. Within sport, the use of data is highly contextual to the individual scenario to maximise potential within that moment. In business, it tends to be more generalised to look at the ‘big picture’ but often loses value in the process. Much like attempts to capture and re-use knowledge.

General predictions tend to lead to general results (read ‘average’). To compete requires raising performance above average and that means doing something differently to everyone else. Personalisation enables that difference.


  • Everton: how the blues made good – by Simon Kuper, Financial Times, 3 May 2013 (may require subscription to access)
  • Competing on Analytics – by Thomas H. Davenport and Jeanne G. Harris, published 2007 by Harvard Business School Press

Side note: the image is me 🙂 a sports-related post is too good an excuse for the occasional horsey pic


Process matters more than data

Process Flows

Big data is a key digital trend right now. Along with social media, mobile devices and cloud computing, it is seen as a potential disruptor of businesses, if not entire industries.

But data, big or small, is useless if it isn’t applied within a process. And the process is more about people than either information or technology.

Last year, the New York Times covered a story – Armed with Data, Fighting More Than Crime – about how a US city improved performance through applying data analytics. The mayor of the city – Baltimore – applied a method first adopted by the New York City Police Department.

The key principles were:

  • Accurate, timely intelligence
  • Rapid deployment
  • Effective tactics
  • Relentless follow-up and assessment

Baltimore did not put in a big complicated technology system to introduce performance management. They used Microsoft Excel and PowerPoint, and the dashboard cost approximately $20,000 to set-up. Instead, the investment was in people – four analysts and an investigator – whose salaries cost about $350,000 per year.

The original article covers the story in far more detail. But amongst the titbits are the details about how the process was implemented:

Every two weeks, each agency head would come to a CitiStat meeting, facing Enright (the investigator), sometimes the Mayor, and the heads of the departments of finance, labor, legal and information technology.  Before the meeting, a CitiStat analyst went over the latest data from the agency, pulled out the important stuff and put it in graphic form.  The agency director stood at a podium, his senior staff seated behind him, and the information was  projected onto the wall…  When solutions were discussed, there was no need to schedule a meeting with the city’s solicitors or budget director, because they were in the room.   By 5 p.m. that day, the CitiStat analyst had written and circulated a short list of commitments the agency had made or information it needed to provide.  “We expected that when you come back in two weeks you will have answers to these questions,” said Enright.

The meeting applies all four principles. They have accurate and timely intelligence. Everyone who could potentially stop change from happening is in the room, enabling both rapid and effective decisions. And there is relentless follow-up to make sure those decisions lead to action, that in turn becomes data to feed into the next decision.

Within 3 years, overtime was reduced by 30% and absenteeism reduced by half within targeted agencies. Within 6 years, it was estimated that the the program had saved the city nearly half a billion dollars and city services had been significantly improved.

It’s an excellent case study in the effective use of analytics and how the process matters more than the tools or the data. And also highlights how important senior leadership is when difficult decisions need to be made. Two non-performing heads of departments were fired early on in the process, signalling just how seriously the initiative was being taken.

Read the original NY Times article for full details – Armed with data, fighting more than crime

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Watch out for biased data


67% chose the blue pill*

We so often see summarised data encouraging us to buy or do something, that it is easy to forget to be critical. Critical thinking is essential to help avoid making poor decisions due to misleading ‘facts’.


The image above is an advert that appeared on LinkedIn this week encouraging people to vote in a poll. But the question is clearly biased. What if your favourite feature isn’t listed? An accurate response would be ‘Other’ or ‘None of the above’. But those options aren’t available which means you either a) make up an answer or b) don’t respond. This is a common fault with most surveys. Made up answers are more likely if an incentive is given such as free gift for responding to the survey. Otherwise most people would just go ‘Meh’ and not bother voting. But surveys that cover all options can be just as bad. Because people who are interested in the question are still more likely to respond (and respond accurately) than those who are not. Self-selection to participate in surveys will always lead to biased data and unreliable results. It’s the easiest way to make bad predictions.

So next time you see claims such as “84% of CEOs said that <Insert product name> helped increase sales”, check the underlying data for bias (and size! How does the sample size compare to the total population?)

It’s one reason why A|B testing is moving beyond web site design and being used in other industries where the practice can be applied – improving systems by leveraging feedback captured from actual uses rather than what people say they would do. An example is included in the recent presentation: How digital trends are compressing processes.

* The number was made up 🙂 Don’t let it influence a future choice

Featured image is from Flickr: Red pill or Blue pill by Paul L. Dineen

Who gets to own or access the data?

Data Center

Short version: It’s easier to comprehend why Facebook bought Instagram for crazy money if you ignore the social networking and instead focus on the value in automatic location updates via Internet-connected mobile devices. That’s a place where Facebook can build a serious business model.

“All models are wrong, but some are useful.” – George Box, Statistician, circa 1978

“All models are wrong, and increasingly you can succeed without them.” – Peter Norvig, Google, 2008

One of the reasons the technology sector is in the news so much at the moment is the emergence of five connected trends disrupting so many traditional industries: massive online social networks, social media tools, internet-connected mobile devices, cloud computing and ‘big data’ analytics.  The social networks enable us to connect with anyone globally, social media tools have made it easy to share thoughts and opinions with those connections, internet-connected devices enable us to post updates instantly and from any location – no more waiting ’til you get home and login to your computer.  Cloud computing enables all this information to be stored and accessed over the Internet.  And accessing massive amounts of data, updating in real-time, enables new forms of analytics not previously possible.

An early new market is the world of social media analytics – providing feedback in real-time about what people are saying about your organisation or product/service. Sentiment analysis adds emotion – are people using words that are positive or negative, happy or sad, loving or hating.  Mining Internet data such as Tweets and other status updates is far more effective than standing on a street corner trying to conduct a market survey.

But who gets to access all of this data? We share it freely and lose ownership in the process.

In February, the New York Times published an interview ‘Just the facts? Yes, all of them’ with Gil Elbaz. His first company, Applied Semantics, was acquired by Google and formed the basis of Adsense, Google’s business model. Gil’s latest venture – Factual – is focused on acquiring massive data sets, and then selling access to them.  Current storage is running at 500 Terabytes:

FACTUAL sells data to corporations and independent software developers on a sliding scale, based on how much the information is used. Small data feeds for things like prototypes are free; contracts with its biggest customers run into the millions. Sometimes, Factual trades data with other companies, building its resources.

Factual’s plan is to build the world’s chief reference point for thousands of interconnected supercomputing clouds.

And now this month (via Techmeme) Forbes has an article  asking ‘Will Data Monopolies Paralyze the Internet?’. Is it the end of Web 2.0 as blogs and status updates become locked inside password-protected social networks?  They think not because more data lies outside them and if it can be mined, any entrepreneur can do it given sufficient resources.

But not all data is open to mine.  The Forbes article highlights a new area of focus and I disagree with their position (emphasis mine):

Some very promising data hasn’t been collected on a large scale yet and might be less susceptible to monopolization than things like status updates. Lots of people I spoke with at the Where conference last week were excited about new ways to approach ambient data. …[collecting] the little specks of data that we’re constantly releasing–our movements, via smart phone sensors; our thoughts, via Twitter feeds–and turn them into substantial data sets from which useful conclusions can be inferred. The result can be more valuable than what you might call deliberate data because ambient data can be collected consistently and without relying on humans to supply data on a regular basis by, say, checking in at favorite restaurants. It also offers great context because constant measurements make it easier to understand changes in behavior.

The article is right to emphasise the value of automatic updates over manual ones – a phone automatically registering your location versus you manually ‘checking in’ to a location is both easier and more reliable. (Hence why Instagram is potentially more valuable than Foursquare).  But it also highlights just how important mobile devices are in this equation.

Who gets to own or access those updates captured by a mobile device’s sensors?  Simple. The device manufacturer (e.g. Apple), the network operator transmitting the data (e.g. AT&T), and/or the app you granted access to record the data (e.g. Instagram – automatically geo-tagging your photos for you).  Sure, the social network gets a look in if you allow the app to share. But it’s far lower down the chain compared to the app installed on the mobile device.  And top of the queue is the device itself. You can connect those dots for yourself.  Small wonder there are constant rumours that Facebook and Google are building/buying their own mobile devices. Presumably Microsoft too (well they’ll probably buy Nokia)…

In that context, Instagram is valuable to Facebook way beyond its benefits as a social network.  Those location updates originating from Apple and Android devices are a large, accurate and valuable dataset that Facebook now owns.

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