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Would you like to supersize your data? January 7, 2013

Posted by Jason in Management.
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Is your organization “data-driven”?  Do you scrutinize your company’s analytics to understand sales, customer behavior, product improvements, or innovation opportunities?  Do you stand firmly behind Peter Drucker’s counsel that “you can’t manage what you don’t measure”?

Or are you confused by the term “big data” and wonder just how another database or piece of analytical software could possibly substitute for years of experience?

Either way, you may be right.

Naturally, business is governed by data.  At the simplest, we measure cash, inventory, sales volume, and numbers of customers.  Human intuition and assumptions (and especially memory) are often trumped by basic observations of hard numbers.  You can’t run a business based on feelings.

But on the other end of the spectrum, we have global organizations collecting unimaginable volumes of information, using it to understand the past and predict the future.  We live in a world of immense data gathering and storage capacity.  Whether we are, in fact, any smarter for it is open for debate.

IBM - Big Data Infographic

This HBR article highlights some of the specific ways data is used, and summarizes a few case studies of successful analysis, modeling, and application that led to increased efficiency.  However, the last two pages highlight what I believe are the most important aspects: the leadership challenges associated with using big data.  Another article paints a clearer picture of these challenges and quotes Thomas H. Davenport’s big questions when faced with a decision based on a data set beyond the bounds of human comprehension:

What data do you need?

Where does it come from?

What are the assumptions behind the model?

How is the model different from reality?

We’ve all heard the phrase Garbage In-Garbage Out.  This doesn’t apply just to the data, however.  It also includes the assumptions and structure of the model – the human construct is, by definition, only an approximation.  If we feed good data into a bad model, the results can be just as disastrous.

It’s tempting to buy into a system designed to boost your business, operations, or product development.  In fact, as the above figure shows, at least in the UK, big data focuses on customer-centric outcomes – in other words, things to improve the customer experience, attract buyers, or otherwise interact with the client base.  And for large companies that can improve on the margin over a large pool of customers, the law of averages says that big data may very well be a good enough approximation.

If, on the other hand, you run a small business with personal service to individuals, you may very well find yourself on the wrong side of the database.  As we know, humans are much more irrational than we give ourselves credit for.  While a large sample size can indeed yield beneficial predictions, individuals may behave quite differently than the model founded on untested assumptions.

In my consulting practice, we find quite often that companies have much more data gathering capability than they actually use.  Our challenge is to make the best use of the admittedly imperfect data, challenge preconceived assumptions, and then change a culture while at the same time reducing the dependency on inflexible methods.  Data is, then, merely a tool to achieve a needed change, but can in fact be as much a hindrance in the future once the change has taken hold.

This goes to show that the need for big data will be driven as much by the particular needs of your organization at a particular time as by the size of the business and its customer base.  Invest in big data carefully and in line with your corporate strategy rather than promises of improvements that may only be appropriate for a larger firm.

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