Big Data: The Big Myth


Big Data: The Big Myth



For a number of years, Big Data has been promoted as an opportunity to unlock customer insights and uncover customer behaviours so that organisations can become more customer focused and perhaps even reach the holy grail of “segments of one” marketing. In reality, an unintended consequence of the Big Data pursuit is that many organisations are undermining their own efforts towards becoming truly customer-centric.

Once products are launched, a data ‘faucet’ is opened and data is created and collected: data that didn’t exist until sales had been made and customers had ‘come into existence’. These events spark a shift in focus.

Managers typically feel an understandable sense of reassurance when they shift their attention from the hazy contours of customer needs to the crisp precision of a spreadsheet. And this switch happens organically and usually, with little fanfare. Product sales generate data about products: how many, how profitable, which models, the list goes on. Customers’ purchases generate data about customers themselves: business or consumer, large or small, young or old, direct or via a third-party channel, the options are virtually limitless.

Beyond products, when investments are made in people, facilities, and technology, more data is generated, providing information relating to productivity, returns and value creation.

And if there wasn’t enough data generated from all of these sources, even more data is collected on competitors. And this data tends to lead managers to create benchmarks that generate, you guessed it, even more data.

What’s important to remember is that all this data is just an abstraction of reality, typically collected and organised based on underlying assumptions as to how to categorise the unstructured phenomena of the real world. Too often, managers conveniently set one important single truth aside: the way in which data is organised is constructed by humans.

It is therefore often not surprising to find that data has a way of conforming itself to support whatever point of view the data analyst wants it to support. We consistently see lots of examples in which an organisation’s particular department or division presents carefully constructed ‘supporting data’ that offers a model of reality which is clearly developed through the lens of its particular functional responsibilities, performance metrics and financial incentives. This is just another example (as if there weren’t already enough) of confirmation bias: the tendency to search for, interpret, favour, and recall information in a way which confirms one’s pre-existing beliefs or hypotheses.

But there is a misconception about data that is so prevalent it’s tacitly embedded in many organisations — the idea that only quantitative data is objective. There’s a pervasive belief that there is some set of ideal data that can, together, yield the perfect insights about customers. It’s just a matter of figuring out what the right data is and of course Big Data provides access to a much bigger and broader data set, so it increases the chances that the “right data” can be uncovered.

The focus on Big Data often conveniently ignores the truth about the data source choices, data analysis choices – and a lot of other choices – being made by humans. Typically, at some point, someone decided what data to collect, how to organize it, how to present it, and how to infer meaning from it — and it embeds all kinds of false rigour into the process. The real truth is that data ends up having the same agenda as the person who created it, wittingly or unwittingly.

So the next time you’re on a quest to uncover insights about your customers, what they value and why they behave the way they do, it will pay to ask yourself one simple question: What mechanisms do you have in place to ensure that the data you’re presenting to support your innovation and investment decisions reveals what you need to see, rather than what is comforting to believe?

If you’d like some help with ensuring that your quest for customer insights is based on the right data, then please feel free to get in touch:

Related Posts

April 22, 2024

Innovation vs. Parenthood. Episode #3 – Will the arguing ever stop? Or should it?

March 4, 2024

Innovation vs. Parenthood. Episode #2 – ‘Why’?!

January 30, 2024

Innovation vs. Parenthood. Episode #1 – Is anyone in charge?

October 13, 2023

Strategy and the People Trap (and how not to fall in it)