Data literacy is having a moment. Understandable given the daily diet we are all being (necessarily) fed of Covid-19 charts and numbers. Getting to grips with the R number and concepts like flattening the curve has undoubtedly put data further into the mainstream than seemed likely before the pandemic.
At a formal level, many organisations have been looking at the data literacy of their employees and deciding it needs to be improved. This is seeing the roll-out of learning and development programmes which typically have the same lessons at their core: explaining what data is, enabling workers to access data for their own purposes, providing them with the tools to analyse and report on that data, and developing their communication skills to use whatever they find when having discussions about what actions to take.
A good expression of the goal for such data literacy programmes can be found at Gymshark, where chief data officer Gemma Hulbert has launched the slogan, “no more ‘I reckon’.” Instead of personal opinion or intuition, her goal is to get colleagues to bring facts in the form of data.
This type of approach is a logical extension of inward investment into data technology and even the fashion for coding that swept management teams in the last few years. If you have created data assets, it makes sense to provision self-service access to them. If you have got business leaders to overcome their fear of using technology to access data, it makes sense to encourage them to explore it further. No surprise then that a swathe of data literacy training courses can now be accessed from the likes of MIT Sloan Business School, Gartner and more.
But take a step back and ask yourself, are these courses actually just building a level of numeracy among employees, ensuring they understand the differences between mean, median and average, for example, or know to check the scale on graphs they are being shown? Do they have any real impact on the way people work?
Consider the type of questions that data literacy training is likely to support, such as, “what is the profitability of this product?” That is an important question for the business to ask at many levels, ranging from product development and marketing through to finance and frontline staff. It has tended to be the preserve of specialist practitioners, including the data office, who have embedded the answer into processes like product recommendations and next best action decision engines.
What the current vogue for self-service data access and data literacy does is push the ability to ask that question out into the organisation. For the 80% of workers who have never been able to run this sort of query for themselves, no doubt it feels fresh.
But make no mistake - this is not a new question the business is asking. Generic data literacy programmes with this limited perspective will not change the business culture one bit. What needs to be aimed for is a genuine upshift in thinking where the question is less about reporting and more about consequences. Less about the profitability of a product and more about what purchasing that product does to the profitability of the customer, for example.
The classic dilemma for most organisations is that it sells products which are not profitable in themselves. If you look at that in isolation, the immediate response will be to cut these loss-making items. Newly-enabled and data literate workers will probably see this for themselves and might stop promoting such products during interactions with customers.
Yet brands in sectors from travel to charity know that single product profitability is seldom the right perspective - they may be gateway products which unlock real customer value. Supermarkets know this well. If they do not have milk and bananas - which are loss leaders - shoppers may abandon their entire baskets.
That is where data culture comes into play. It encourages a dialogue between data owners and business process owners, sharing evidence and building a more rounded perspective. Keep printing the holiday brochure and your customers will book more expensive vacations - axe it on cost grounds and you lose out. Only promote a high-tariff mobile contract because it is the most profitable and the customer will churn after a year, losing you the higher level of profitable that comes in year two and beyond.
DataIQ has been studying this intersection between data, business, evidence-based decision-making and effective data culture ever since we launched ten years ago. The sum of that knowledge is already being played back within our own membership programme, DataIQ Leaders, and later this year will emerge as a fully-worked framework, the DataIQ Way.
We know that great businesses can be built using data and analytics, but only if they become part of the operating culture of the organisation. That means much more than creating your own graphs and being able to explain what the axes represent. It means building the confidence to ask the next question, to look for the white space opportunities revealed in data, to make critical decisions when the data just shows what is, not what might be.
That’s when the dial moves.