There is a digital skills gap in many companies, and closing it could bring new opportunities and rewards, according to this opinion piece by Hjalmar Gislason, CEO and founder of GRID. He is also a partner at Investa, an early-stage investment fund, and previously was vice president of Product Management at Qlik.
As the role of data in business expands beyond anything the world has seen, the workforce is not keeping up.
For four years in a row, one survey has found that “big data and analytics are top of the skills shortage critical list.” But amid the focus on high-level data skills, there’s another gap in workforce preparation: Brookings reports by the time they enter the workforce, students haven’t had enough exposure even to simpler data tools such as spreadsheets and enterprise management platforms. It’s time to “expand digital literacy across the board,” CityLab declares.
As a tech founder in both the United States and Europe, I’ve come to see that these days, every worker is a “data worker” — not just in offices, but in oil fields, hospitals, schools and more. To be able to do their jobs effectively, they need to collect, harness, learn from and share data all the time.
What data competencies do they need? I break these down into four central components. These are the new basics of data literacy.
“Every worker is a ‘data worker’ — not just in offices, but in oil fields, hospitals, schools and more.”
A Question or Problem
People often face data with the wrong mindset to begin with. They look at some data they’ve received, and think, “What questions can this answer?”
This is not the best starting point. Instead, all workers should begin by asking: “What are the most important questions I could answer if I had the data?” Then, ask whether you can answer them with data you already have or whether you need to collect additional data to provide the answer.
As a white paper from Knowledge@Wharton and WNS DecisionPoint notes, “The process of extracting smart data begins with identifying the end-issues that need to be addressed.” These can include cost savings, efficient decision making, ways to improve customer success and lots more.
If you don’t know whether data could answer a certain question, ask! Those with more data expertise in your organization will be able to advise. But it’s the workers on the front lines in every department who know the information they need — and, therefore, the most important questions.
The second component shows why all workers need basic data skills in order to operate at the speed of modern business. Currently, many workers rely on IT, engineers or others to set up a system for them to collect the data they’re looking for. But this isn’t always necessary. For many data needs, anyone can do the collection.
Workers should know how to gather data and where to find it, whether through designing a survey or exporting data from source systems. And they should know how to bring the information together in a spreadsheet. The greater proficiency all your employees have with this, the more quickly they can collect and synthesize data that will help make crucial decisions and get on with the task at hand without having to rely on others.
As part of this, it’s also crucial for workers to know how to use technologies that help scan for and prevent common mistakes.
This third basic of data literacy can feel intimidating. Many of today’s workers don’t trust that they can extract all the necessary insights from the data they’ve collected. And unfortunately, the term “big data” contributes to that problem. Some workers find the prospect of big data analytics “overwhelming and scary.”
“All workers need basic data skills in order to operate at the speed of modern business.”
It shouldn’t be. The reality is that understanding big data is no different from understanding “small data.” The only true consumers of big data are machines. When it comes to humans, we’re all looking at small data.
So all workers should have the skills to analyze and comprehend the data they collect. This includes basics in visualization, the ability to turn data into charts and graphs that make sense.
Finally, all workers need the ability to help share and disseminate the insights they glean from data. This is a big part of why I launched my tech startup, GRID.
This requires taking visualization skills a step further, and turning data into visual elements that anyone will find clear and interesting. It means knowing which kinds of charts to use for various pieces of data and why. It also requires specific knowledge, such as why columns should start on zero, you have to be careful using dual axes, and pie charts should almost never be used.
Communicating the information also requires narration skills — the ability to explain in the simplest and clearest terms what the lessons are. Long-winded, wordy explanations leave people confused and make their eyes glaze over, too often making all the hard work that went into collecting and analyzing the data futile.
“Another benefit of having employees learn all these skills is that they can then help each other.”
Another benefit of having employees learn all these skills is that they can then help each other. When one employee communicates the lessons from a set of data, another employee may notice problems or discover new lessons in that same data, or may discover a new question to pursue next.
When you have a workforce full of people with this basic level of expertise and the freedom to explore the data collected by different departments, you create an environment in which discovery and improvement become a standard part of every day.
While it’s also important to have a robust portion of the workforce skilled in more complex technologies, it’s time for domain experts to be self-sufficient with most of their data needs. In 10 years’ time, every knowledge worker will, to some extent, have to be a “data scientist.” As the labor market analytics firm Burning Glass puts it, a job candidate without skills in the “humbler world of everyday software,” including spreadsheets, word processing and billing programs, “won’t even get in the door.”