Big Data Accuracy Before Insights

Dora the Explorer

A friend’s recent Facebook post really resonated with me.

“Why does United Airlines bother sending me emails telling me I can go this weekend to Moline, IL for a mere $271? or to Saskatoon for $351? Really? Does that sound like a good deal to them? Have I ever shown the least bit of interest in those places ever? With all the data in the world out there, they seem to have no idea of my interests or proclivities. Yet another reason they really bug me.”

After hitting the “like” button, I got to thinking. The marketing conversation in 2012 was dominated by talk of big data, and that trend shows no sign of abating in 2013.

Much of the discussion of big data in the marketing world has been about extracting insights from data. But my friend’s United Airlines example underscores the importance of managing customer data in a way that helps build the customer experience dynamically – and talk of insight generation should be subordinated to that more fundamental goal.

I complained recently about LinkedIn’s serving me ads clearly targeted to women, due undoubtedly to my unisex first name.  Just today, I noticed LinkedIn suggested I join a group called “Bowdoin Club of Asia.” Yes, I graduated from Bowdoin, but I’ve never even set foot in Asia.

LinkedIn certainly isn’t alone. Even the mighty Amazon is not perfect in its knowledge of my preferences. Yes, I bought gifts for my nieces, but do I really have to see Dora the Explorer in my list of suggestions?

Before a company becomes overly concerned with measuring and extracting insights from big data, it needs to be sure it’s implementing it effectively to create a dynamic – and meaningful – marketing experience for customers.

After all, if you drive your customers away with ham-handed marketing appeals, they’ll be gone before you even get the chance to measure their opinions.

About Dana Stanley

Dana is the Editor-in-Chief of Research Access.


  1. Alec Maki says:

    Nice post. Think of big data as a giant spaghetti factory. At the end of the day, you have lots of wet noodles to throw against the wall. Many stick and, inevitably, many don’t.

    Seriously, my problem with “big data” is that data is dumb. Human interpretation and intelligence must be applied to convert data into applied knowledge. Despite all the hype, we’re still at the infancy of knowing how do to that. The result? We get lots of dumb things thrown at us, per your post. This is the beginning of a long work-in-progress.

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