Data literacy is quickly becoming one of the most crucial skills in a world that’s increasingly dominated by stats, business processes, machine learning, computing, and AI. Unfortunately, a massive skills gap has developed, producing adverse effects within every company that uses data in its operations.
Issues of data literacy have emerged all the way up to the C suites of multinational corporations, with one study showing that just 24% of business decision-makers have the necessary skills. Worse, there appears to be zero generational benefits for folks who grew up in the data age, with digital natives posting a 22% rate of acceptable data literacy. The U.S. lags Europe, and worldwide the issue is problematic in even the best of environments.
Only 17% of businesses report openly encouraging data literacy training, even though employees have almost uniformly expressed a desire for it. Likewise, only 36% of the same businesses report providing any incentives, such as higher salaries, for employees to upgrade their skills.
Defining Data Literacy
Among the trickier issues is defining what data literacy means. It’s important to distinguish data literacy from digital literacy. Many people handle basic digital skills well, but they commonly lack grounding in reading and comprehending data. There is also a high level of dependency on machines to get the answers right without really knowing what the implications of such attitudes are.
Data literacy in business predominantly consists of three main areas. These are:
- Data science
Each of these fields is underpinned by the discipline of statistics. That means people reading data, for example, need to be able to understand and apply concepts like:
- Sample size
They also need to be familiar with issues such as data preparation, analysis, archiving and presentation. Even professionals who are highly skilled at preparing and analyzing data, for example, may lack skills on the visualization side of the equation. It’s important to understand how particular presentations of data may aid or hurt comprehension, especially when the audience is being introduced to the information for the first time. As nit-picky as it sounds, the precise choice of one style of chart versus another imposes biases and creates possible comprehension issues.
What’s the Cost?
Unsurprisingly, all this comes at a cost. An estimate from the Data Literacy Project indicates that for a company valued at over $10 billion, the cost of the data literacy gap may cause economic damage to the order of 5% of the firm’s market cap. That represents more than $500 million worth of value. Despite these severe numbers, only 8% of businesses reported making significant changes to how they approach their data issues.
In addition to the immediate costs, there are also opportunity costs which are much harder to estimate. The absence of data-driven decision-making at many companies means those organizations are falling behind more progressive competitors in fostering data cultures. It’s not uncommon for businesses to lack data management titles such as a Chief Data Officer, this signifies the absence of governance overseeing many organizations’ data strategies. This also highlights organizational issues that aren’t preparing employees to be assuming those data executive roles 10 and 20 years from now.
How to Close the Gap
There are two areas where change can immediately occur. These are:
- Providing incentives
Solving these two issues is well within the means of modern companies. As previously stated, the majority of employees at companies are eager to join a data literacy effort through training. Likewise, providing incentives is not only logical in terms of encouraging training, but it becomes a retention issue. After all, you don’t want to spend money training people who end up at other organizations solely based on better pay.
At a larger scale, the adoption of a data-centric culture is critical. Data can’t just be a work product that’s presented to the higher-ups. From the C suites on down, every company needs to understand how to read data, interpret it and talk about its implications. With a focused effort, your organization can become one that starts taking advantage of new opportunities.