Good data, well understood and analysed, can be a game-changer. Knowing how we are doing versus competitors, understanding how sales of a category or competitor move across the year, monitoring customer opinions and behaviour – it’s invaluable.
But data can also be dangerous. It’s often misunderstood and misapplied, with serious consequences. Consider the retailer that thought price wasn’t a problem, forgetting that its satisfaction survey didn’t include shoppers who had left them because of price. Or the many companies over the past 20 years that misread their position in the market by focusing too much on scanning data that excluded the discounters (the most important dynamic in the market). Or the businesses right now that rarely look at Amazon or key parts of foodservice.
There are probably measures being used in your company that are not correctly understood by your decision-makers. So, what can you do to make sure that you manage this risk?
First, give voice to your data experts. You likely have people who fully understand the ins and outs of your measures and analysis techniques. They may already be uncomfortable about how data is being interpreted by leaders. Ask them. But also offer them reassurance, because it can be scary to speak up about a measure or insight that has become a ‘sacred cow’.
Second, expect your decision-makers to have detailed knowledge of the data and analysis methods. It shouldn’t be acceptable to leave it to the data experts. Years ago, many fmcg and retail companies invested heavily in training their people, including by offering excellent graduate programmes. That meant senior leaders with excellent data and analysis knowledge were developed, P&G and Unilever being leading examples.
I suspect there is less resource for training across our industry now, given cost pressures. But by setting the right expectations, and with some baseline training, you can get your leaders where you need them to be.
Third, challenge your data. Don’t lay down before the numbers and let them make the decisions. Look at several data sources to check for consistency. If you find differences, work out why. If you can’t work out why, don’t let it slide.
Fourth, listen to what you hear outside the work bubble by talking to friends or family. If they see a problem with what your company does, but your internal measures remain stubbornly positive, something is probably wrong with the data.
And finally, test the data against your intuition and experience. If there’s a clash, you may be on to an insight that transforms your understanding. Just as likely, the data or analysis is flawed. Keep asking questions until you are satisfied.
Data is good. But it can be bad. Make sure your company understands it, challenges it and keeps it in its proper place.
Jeremy Garlick is partner at Insight Traction
No comments yet