Big data : Raising Irrelevancy & Obfuscation

In a recent meeting the team lead made the following statement:

“We started with a rate of  .037% which is about 19% above the industry category norm of .031%. On re-focusing and re-targeting we achieved a rate of .7%. Then by using our proprietary behavioral scoring algos we achieved…Blah! Blah! Blah!    …add in our optimization matrix and… Blah! Blah! Blah!…this lead to an overall uplift rate of… Blah! Blah! Blah!… and a y-o-y ROI increase of almost 33%.”

The customer, to whom this statement was addressed, remained unmoved, staring at the ceiling.

Oh!” he finally managed to say.

This is a typical case of the phenomenon called “burying the lead.” In journalism when an incompetent reporter doesn’t understand the important point of a story, he buries it beneath irrelevant details. Now in nearly every business, people bury the lead. But mostly, it is done intentionally.

The real story may well have been: For every 100 000 media mentions that we did we got just 5 lousy customer calls. And we don’t even know how many of those were genuine.

The age of “big data” is now the age of “big bullshit” — bullshit on an ever grander scale than ever before. We’ve always had the ability to bullshit with words. Now we have perfected the ability to bullshit with numbers and statistics. We’ve always had bullshit con artists. Now we have created a new breed of elites – scientists and programmers who bullshit.

Marketing, we all know, has this phenomenal capacity in complicating the living shit out of everything, and does so now, more than ever, with Big Data.

Remember, in big data, the deeper the (data) dive, the more calculatedly they’re raking up the shit and burying the lead.

Big Data may mean more information, but it also means more false information. Just like bankers who own a free option — where they make the profits and transfer losses to others – big data scientists  have the ability to pick whatever statistics confirm their beliefs (or show good results) … and then ditch the rest.

I’d rather have Good Data than Big Data, any day.

Many times we hear: reduce risks, cut costs, simplify IT and increase revenue, don’t we? These claims are typical among many of big data’s proponents. But none of the proponents attempt at providing any kind of proof of results. The promise of big data remains nothing more than that — just an empty promise.

So how does one avoid getting caught up in the “big data” hype:

1. Understanding the Problem with Marketing

Analyzing data may produce insights that can help produce improved marketing results but the ability to identify consumer needs and intentions based on their behaviors and actions  is only half the story. The other half is how you respond. Do you follow up by giving appropriate advice or  guidance, and at the right time and in the right way? Most don’t.

Without the ability to reach out to your customers and prospects with just-in-time and just-right-enough messages, marketing will not be of any use.

If you think you can increase revenue by spotting trends ahead of your competitors without reaching out to your customers in a timely and appropriate manner it won’t work.

But you don’t hear the big data proponents talking about this, do you?

2. Understanding the Problem with Customer Integration

Many corporates go through  a “customer data integration” effort integrating data from a number of business lines in an attempt to create a 360-degree view of customers. Many achieve that objective by building a technology infrastructure pulling data from across the organization. But where they fail is providing users easy and timely access to that data. Managers are then forced to  submit data requests to IT for marketing information. These requests often take weeks  to complete. This is not as it should be.

In reality it  will take years for companies to develop and integrate “big data”. The claims of big data ROI bandied around are unattainable in all but a few cases.

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