Even in terms of definition, there’s some disagreement regarding the power and potential value of big data for marketers.
Google big data and you’ll find it defined as:
“… extremely large data sets that may be analysed computationally to reveal patterns, trends and associations, especially relating to human behaviour and interactions”
Forbes will tell you it’s:
“…technology (which includes tools and processes) that an organisation requires to handle large amounts of data and storage facilities.”
On the other hand, Webopedia will tell you big data is:
“… a term for data sets that are so large or complex that traditional data processing softwares are inadequate to deal with them, leading to challenges of data capture, storage, analysis, curation, search, sharing, transfer, visualization, querying, updating and privacy.”
So, big data is essentially a lot of data that marketers can analyse using innovative techniques to give marketing insight and power.
Or perhaps it isn’t. Perhaps there’s actually so much data that it cannot be used in a constructive way.
Even if we do accept that it can be utilised within marketing, some argue big data is no more than a sophisticated and complicated way of recreating the past.
Phenomena such as ‘mirroring’, or ‘echoing’, where consumers’ previous behaviour shapes how we market to them in the future, risks stifling innovation and preventing disruptive marketing.
Indeed, we are increasingly beginning to hear of major (and expensive) data-led marketing programmes whose sophisticated in-built metrics reveal – in uncomfortably precise detail – a remarkable lack of effect. There seems to be a growing body of opinion that maybe Big data isn’t quite the claimed panacea bestowing huge insight and power to the marketing professional.
Within the marketing community the debate seems to be moving back to whether big data techniques represent a brave new world or a massive con trick.
The benefits of thinking small
I’d like to suggest a couple of more useful ways of considering big data. Both are pretty obvious, and both have been previously discussed.
But as debate has moved on, they seem in danger of being forgotten.
Think in terms of individuals
You can measure the aggregate behaviour of an unimaginably large crowd, but you must understand the individual movements of each person in the crowd for the data to be of any use.
Measuring collective behaviour makes for accurate history but you need to understand individual motives to shape your future.
Think in terms of clues
The second point is slightly counter-intuitive, and regards how we see the data itself.
It may be intelligent for marketing not to consider big data as ‘facts’.
Instead, we should think of it as clues. Clues about patterns in what people are doing, what they’re thinking and what they’re feeling. And then more clues about influences: what media people are accessing, how they’re receiving and interpreting messages and how they’re changing their minds.
But the thing about clues is that they don’t solve themselves. Clues need to be read, interpreted and understood, and that takes imagination.
And there lies the heart of value of big data and its usefulness as a marketing tool.
Interpretation is everything
It’s unarguably true that big data sources and technologies are hugely powerful in examining markets, in planning how to address audiences with more effective and efficient targeting, and within evaluation and effectiveness assessment regimes. However, while conducted mechanically, with data leading marketing increasingly sophisticated marketing tools lead to increasingly crude and clumsy marketing programmes.
Big data sources and big data techniques will not remove the need for imagination and creativity in marketing. They will raise the bar.
And so, the real challenge to the marketing community may be how to recast the ‘traditional’ marketing skills of imagination and creativity to effectively manage the potential of big data sources and technologies.
A few different models seem to be evolving:
Formalising a series of ‘what if?’ visualisation pauses through the course of marketing development programmes appears to offer potential in terms of ensuring data is continually used with, rather than replacing, insight.
A number of new tech organisations have adopted the route of creating a discrete function of data commentators, analogous to rally car navigators, within marketing teams with the role throughout the process of monitoring implications of data in the context of a clearly defined longer term strategy and unchanging end objective.
Several large scale traditional organisations have, over the past 18 months, responded to opportunities and challenges in this area by establishing a separated Executive Board role of Chief Intelligence Officer.
Any approach adopted will of course reflect the organisation and task in question. However, to succeed, one principle may need to underpin any approach:
Big data can only record the past: Draw inferences, don’t take dictation
Big data measures the aggregate — we must understand the individual: Think small
Big data won’t provide a ready-made answer or do your thinking for you: Look for clues
Above all, big data is complex and nuanced. Effective management and direction demands ultimate simplicity and clarity in strategy: keep it simple.