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The importance of data science literacy in 21st century business

The importance of data science literacy in 21st century business

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Data science is increasingly important for businesses which want to grow and drive new revenue streams. Boris Paillard, CEO, at Le Wagon, a coding school based out of 43 cities in 25 countries, explains how data science literacy is equally important.

Over the last several years, data science has become one of the most valuable tools for businesses to increase their operational efficiency and drive new revenue streams. While data science skills are in extremely high demand, equally important however, is data science literacy.

It’s becoming increasingly important for the entire workforce – from the C-Suite down – to have a functional level of data science literacy. It’s no longer enough for a company to hire data scientists – they must ensure that the work they do is understood across everyone in the business.

What is data science literacy?

Data science literacy, though often discussed, can be difficult to define. To be ‘literate’ with data science is a fairly broad term, owing to the nature of data science as an inter-disciplinary field – drawing on a mix of hard mathematical, scientific skills and softer business skills.

For this reason, rather than pointing at an example of data literacy, it’s often easier to spot the absence of certain data skills and work backwards from there. For data scientists and analysts, this process is happening organically. As they increasingly collaborate with business leaders, stakeholders and less-technical colleagues, they discover the areas where a little more knowledge would go a long way in easing communication.

Much of the time, what is missing is not hard technical know-how. Rather, it is more generalised data-driven problem-solving skills that constitute ‘data science literacy’. At the most basic level, this can be manifested simply by asking the right questions – understanding which available data sets and variables are relevant to the task at hand.

This diagnostic ability to identify data is the first step in becoming data science literate. The next step is interpretation. Once the relevant data is identified, it must be interpreted – the meaningful needs to be separated from the meaningless and useful insights need to be highlighted. The third and final component of data science literacy is the ability to communicate effectively using data. This aspect is the most vital for businesses. If those working within the growing data function of companies are going to talk coherently with the other functions, the employees across the company need to understand what is being said.

For our purposes then, we might define data science literacy in business as
‘A functional proficiency in the identification, interpretation and communication of meaningful data and datasets’.

How is data science literacy created and encouraged?

Data is everywhere. “Data is the new oil”, as first pointed out by mathematician, entrepreneur and, among other things, inventor of the Tesco Clubcard, Clive Humby. For businesses looking to boost their data science literacy, this means that there’s no shortage of examples for employees to learn from. The first stage in encouraging data literacy can simply be to raise awareness of its impact – share use-cases with employees, examples of how data has informed company strategies or driven powerful decision making.

Raising awareness is one thing, however a company that wants to get serious about creating a data literate culture should consider incorporating at least some basic training – setting a minimum level of competence from the C-Suite down. It’s a worthy investment as it allows you to future-proof both your employees and your business. The tide of Digital Transformation is irresistible and it’s important not to get left behind.

This training could take the form of an in-house platform, for example, a library of open-source learning materials for employees to access, perhaps with an incentive to do so. This kind of broad, long-term solution is well-suited to large companies with many employees, but a smaller company may want something more bespoke. For smaller companies, the most accessible way to boost data science literacy could be to schedule a few immersive training days. In as little as four days, it’s possible to acquire all the necessary skills to be considered literate with data science. If everybody in the business takes part, the effects can be transformative.

Why does this matter to business?

It’s no overstatement to say that data science is transforming the world. Every department, in every company, in every industry, is experiencing the pull of ‘Big Data’ – for which the applications seem innumerable. Less than 0.5% of all data produced is ever analysed and used. That 99.5% represents pristine, untapped potential.

According to research from Gartner, almost nine in 10 (87%) senior business leaders cited digitisation as a company priority, however only 40% of organisations have actually implemented digital initiatives at scale. Most businesses are lagging behind in data-driven operations; here lies the opportunity for those willing to take it. In 2019, research from Adobe found that digital-first companies were 64% more likely than their peers to have exceeded their top 2018 business goal. At this stage, embedding data science literacy into your business is a matter of staying competitive.

If we have to single out one unambiguous reason that data science literacy matters to business, it’s this: data makes for better decision making. It’s that simple. In an environment as complex as ours, decisions based solely on judgement just won’t cut it anymore. This is especially true in large companies with many moving parts. Data-driven problem solving is necessary, and by extension, so is having data literate employees.

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