Dangerous Liaisons and Fatal Attraction

Big Data is fantastic.

When I say this, I mean the broad definition of ‘Big Data’ offered by Viktor Mayer-Schönberger and Kenneth Cukier in their 2013 book Big Data: A Revolution that Will Transform How We Live, Work, and Think :

“The ability of society to harness information in novel ways to produce useful insights or goods and services of significant value” and “…things one can do at a large scale that cannot be done at a smaller one, to extract new insights or create new forms of value.”

Over a relatively short period of recent history, big data has been having a hugely positive impact on many aspects of our lives -- and, on the world around us. Yes, there are (quite) a few examples of less desired application and impact of big data, but to me, the good outcomes outweigh the bad ones by several orders of magnitude.

Dangerous Liaisons

The more commonly used, ‘classic’ definition of big data speaks of datasets so large, they require new approaches to collecting, storing, processing, analysing and harnessing the value of data.

In this narrower sense, ‘Big Data’ is a buzzword almost unanimously embraced by the business world. Marketers are among the most devoted worshippers, which is no surprise: the possibilities opened up by big data are plentiful, offering new ways to develop and execute ideas that put a thick layer of dust on many traditional tools of the marketing trade. Let’s innovate! (oops!... another buzzword…)

It seems to me, however, that the global marketing community often takes this popular, narrow definition even further: equating ‘big data’ with petabytes of self-generating, unsolicited data that gets created every second as a by-product of human and machine activity. Posts on social media; credit card transactions; energy consumption spikes; website visits; mobile phones registering with radio towers, Wi-Fi routers and Bluetooth beacons; hashtags; licence plates of cars crossing bridges; smart cards tapped on public transport and in stores… The list goes on and on and on. No one is asking any specific questions -- and yet, all this data is being generated to provide answers. Magic! (well, not exactly…)

Once you jump headfirst and take a swim in the vast pool of (big) data, it’s easy to get attracted (a.k.a. fooled) by its seemingly all-inclusive nature. You quickly lose touch with (and, perspective on) whatever part of the world which remains uncharted by ‘big data’ – thinking the latter is synonymous with the former.

Why bother seeking specific information? Let’s query the dataset, and some interesting patterns will present themselves, for sure. Answers are incomplete, or don’t feel correct? No worries, ‘big data’ is… well… big -- so it must be telling the truth, right? A target audience you know almost nothing about? Minor detail… let’s think of millions of ‘lookalikes’!

Fatal Attraction

Is this the worst that may happen, you may ask? Sadly, the strange process of self-alienation from the real world doesn’t stop there. In extreme cases, the focus shifts chiefly on ‘big data’ generated by one’s own organisation – especially when it has a relatively large user base. On the surface, it appears to be a very attractive concept: by doing business ‘as usual’ and interacting with its customers, an organisation gets free access to large amount of data. Simply (sic!) find a way to link customer-generated data to your business KPIs, perhaps pepper it with a social media listening feed, and Bob’s your uncle!

The danger of focusing too much on ‘big data’ generated by one’s own organisation is that such ‘data pools’ tend to work like sensory deprivation tanks: isolating users from the business reality out there, they offer an illusion that the identified data patterns are a true reflection of what is actually happening in the market.

So, why this fatal attraction? Why the mad run to make most use of ‘big data’, while ignoring other available sources of information vital to the business. Why the sole focus on data that simply gets generated, or -- in extreme cases -- on internal data alone? Some would say: hey!, it’s called Innovation, accept it. Others would say: we live in a Digital Transformation era, that’s why. And, there is also this huge choir signing from another popular hymn sheet: budget pressures…

I beg to differ.

In most cases, it is actually several misconceptions cultivated by the business community that contribute to this kind of thinking. The list below isn’t exhaustive, nor does it universally apply to all ‘big data’ users. Though, in my work, I often come across such ‘archetypal’ thinking.

Misconception #1: data collected from our customers provides understanding of all consumers out there.

This fallacy is based on a (false) assumption that people who become one organisation’s customers are not different from customers of all other organisations, nor from those who are not customers at all. In fact, these distinct groups often are different -- and, it is being different which actually makes people chose one product or service (or, one organisation) over another.

Misconception #2: social media is a rich enough source of data to provide understanding of customers and consumers alike.

This is like saying that for building trusted relationships, stalking people is as good a way as talking to them… ‘Liking’ or ‘following’ a person or a brand doesn’t always mean one actually likes or listens to them. Even if they do, it doesn’t always mean one trusts them, or would be influenced by them when making purchasing decisions. These processes don’t work on auto-pilot.

Misconception #3: internal data which organisations collect every day provides complete understanding of their customers, and drives Customer Centricity.

In most cases, this internal data is 95% behavioural, and says nothing about people’s attitudes, needs, opinions, values, beyond basic socio-demographic profiles, psychographic profiles, what makes them happy in their lives, what do they fear, etc. etc. … Even worse, by definition, organisation’s internal data is collected in the context of their existing products and services. It provides no insight into people’s lives outside that product/service-focused context (guess what?, people do have lives outside of being your customers!). The so called ‘Customer Centricity’ becomes product-/service-driven -- whereas, it should be people-driven.

One can't build an "understanding of consumer behaviour" by analysing consumer behaviour alone. It's called circulus in probando .

Misconception #4: market research is an expensive and time-consuming way of collecting data and building an deep understanding of the market.

It doesn’t have to be. In fact, when you get into a habit of using market research (or, other methods of actively seeking vital market information) to support everyday business decisions, not just those big and strategic ones… when you make bite-sized market research part of your agile, iterative business processes and decision-making… when you start using market research in parallel with ‘big data’ (and other available data sources) to triangulate information… you will have a far greater opportunity to harness the value ofconnected data, and maximise the ROI. (btw, if you get ‘big data’ for free, and make no investment in putting it to work for your organisation, there’s no talking about ‘ROI’ in true sense)

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I still think that Big Data is fantastic. Surprised? I honestly do! And my favourite definition of ‘big data’ comes from Gil Press’ September 2014 Forbes article:

“A new attitude by businesses, non-profits, government agencies, and individuals that combining data from multiple sources could lead to better decisions.”

No matter how ‘big’ the Big Data becomes, marketers mustn’t fall into its trap of tunnel vision. One way to avoid such fatal attraction, and to maximise the value of data for your organisation, is to diligently use connected data from multiple sources to support business decisions – big and small.