Facts can’t speak for themselves

As a bit of a veteran of consumer insight, I’ve witnessed (and been actively pushing for) a radical transformation over the past two decades in how we gather, store and analyse data to help us better understand consumer experiences, behaviour and actions.

A key driver has been data processing and data, text, and web mining technologies – which continue to advance with artificial intelligence-based engines and cloud computing. I’m in awe of these technologies and the power they provide companies to lead an applied data analytics offense that drives and improves revenue, consumer engagement, consumer retention, and ultimately business growth.

But – and there is a big but – I’ve also seen how the value we can leverage from these amazing technologies can be limited if we focus all our efforts into applying the technology and forget about the enormous effort also needed in working on the data itself.

My experience from having worked in a number of companies over the past few years is that we’ve become better at mining the data and producing a ton of metrics, but we’ve got poorer at deeply analysing what all that data really means. Why? Well, the trouble is that companies have been investing heavily in data applications and some have lost sight of the skills needed to actually analyse the data. Getting from metrics to ‘insights’ is the real challenge.

I have a recent experience to share. Our own customer data, along with tons of market intelligence and social listening data, suggested a big new business opportunity in a new product line. Eagerly, product designers worked up a prototype to test.

When we tested the appeal of the proposition and willingness to purchase through surveys and focus groups, the results were inconclusive. The proposition bombed in markets where other data suggested it would be strong and it was challenging to see a clear pattern of likely adoption across some of the other markets. In this situation, it was only through the team working together to apply a blend of foresight and observations of what was happening in each market, along with our own experiences as a customer, that we could identify a new emerging customer segment for this proposition. If we just took the data at face value, we would have got lost in the weeds.

To that end, being data-driven isn’t the goal — it’s being insights-driven. An insight isn’t something you find – it has to be crafted. It’s taking data points along with your own observations, reflections and qualitative judgement to understand and explain why something is happening the way it is.

After all, a collation of facts doesn’t do anything by themselves. You have to assess them, interpret them, and present them in a clear and meaningful way. You speak for the facts. Then you have to put on your business head, apply business acumen and wrap up that understanding into commercially framed options or recommendations that help your company grow. That’s the tough part.

So, what do you do? You have to re-evaluate the skill sets in your team – you might have kick-ass SQL analysts and data visualisation wizards, but do you have social scientists and market researchers? These folks are experts in inductive and deductive reasoning and the interplay between them, and help you explain things better. Do you have people in your team who bring experiences from other sectors to challenge current assumptions – identify blind spots and bring fresh perspectives? You don’t have to necessarily bring in specific specialisms – you could look to up-skill the people you have.

Its only through building this mixed skill set that you can create the alchemy to turn data into insight and in turn derive value for your company.

Parves Khan is global research and insight director at Pearson PLC