Data: Does your firm speak business’s new lingo?


If your firm − and that means everybody in it − does not “speak data” then it is certainly headed for the scrapheap sometime soon. In the first of two articles, I look at why data literacy is so important, what the current state of play is, and how to start the journey towards becoming a truly data-driven enterprise.

Everybody knows that we live in an increasingly digital world, and every business, large or small, is busy working out what digital transformation means. What most people fail to realise is that the digital world, and particularly the digital business world, is all about data. And that means firms need to ensure their people understand what data is and how it can be used.

Not just specialists or executives, but everybody. Another little-recognised fact is that becoming data-driven means that decision-making is being driven down from managers and executives to the frontline staff who are actually dealing with customers and business partners.

Everybody in the company needs to “speak data”, at least to some extent. Businesses need to think of themselves as in the middle of an episode of Star Trek. They have just landed on a new planet − Planet Data − and they cannot leave. To survive, and then to prosper, they need to understand the language the natives speak or risk disaster.

With the advent of the fourth industrial revolution, and the ongoing digitalisation of business processes, we are generating more and more data.

Everybody in the company needs to “speak data”, at least to some extent.

IDC estimates that by 2025, we will be generating 10 times more data than we are now − a gobsmacking 163 zettabytes. By then, nearly 20% of that data will be critical to our daily lives, and 10% “hypercritical”. More than a quarter of it will be real-time in nature, and the bulk (95%) will come from the internet of things.

Why is this important? Because all of this data − assuming it is properly classified and stored − is literally a treasure trove. Thanks to the huge computing resources in the cloud, it can be analysed using artificial intelligence and machine learning to generate insights that support better, evidence-based decision-making, develop better products, deliver better service, or generally just read the market better.

MIT Sloan defines data literacy as the ability to read, work with, analyse and argue with data. Just like any new language, in fact, one needs to know at least something about how the grammar works, what the vocabulary is, etc, before one can use it.

Everybody is battling

Learning a new language is not particularly easy and requires some degree of application. Research clearly shows that companies and their employees are battling to get to grips with this new language.

Research conducted on behalf of Qlik by Censuswide surveyed 7 377 business decision-makers from companies across Europe, Asia and the United States; it paints a somewhat bleak picture:

  • Only 24% of business decision-makers are fully confident in their ability to read, work with, analyse and argue with data.
  • Only 32% of the C-Suite is viewed as data literate.
  • 94% of respondents using data in their current role agree that data helps them to do their jobs better, and 82% also believe greater data literacy would give them more workplace credibility.
  • 78% of business decision-makers said they would be willing to invest more time and energy into improving their data skills.

So, while it’s recognised that data literacy is important, well under half of decision-makers are completely at sea.

But what I found most compelling of all is that the vast majority (85%) of data-literate people say they are performing very well at work; that figure drops to 54% when one includes the whole workforce. In other words, data literacy provides a competitive advantage because the more data literate a company’s employees are, the better they − and thus the company − will perform.

Horses for courses

One needs to pause here to point out that not everybody’s needs are the same. A person who wants enough French to order easily in restaurants and enquire directions will not need the same level of expertise as somebody who wants to study Molière or engage with French scientists about nuclear fission.

Not everybody needs to be a data scientist, but everybody needs to have the level of data literacy needed to do their job effectively.

In the typical organisation, one could categorise data users into four broad groups. Foundation users do not yet recognise the value of data in the organisation and have not had any training. Their jobs typically have not required them to interact with data regularly.

Consumers work with data − generally on spreadsheets − but they need guidance on visualising data and how to leverage the investments that have already been made in data platforms in order to make the insights their data contains more apparent and accessible to everybody.

Power users understand data but don’t know how to communicate what insights the data is generating and why it is valuable.

Lastly, elite users are the data champions who drive data opportunities and deal with key decision-makers in order to encourage greater data literacy across the organisation.

These types will use data differently to answer an ascending sequence of questions: What happened? Why did it happen? What will happen? How can we make X happen?

In my next article, I will look at the data life cycle and its implication for who uses the data, and then consider practical ways in which companies can go about making their staff data literate. 



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