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We all know our colleagues each have their own unique personality, but what if I told you that everyone on your team also has their own “data personality,” too?

Data literacy is the ability to read, work with, analyze, and argue with data. Having this skill set is more important than ever before. We’re in a highly competitive business landscape today, and data needs to be at the center of making smart and accurate decisions. However, according to Qlik’s recent global data literacy report, only 24 percent of business decision-makers are fully confident in their data literacy skills.

Globally, there is a widening skills gap even within the ranks of senior leaders. Only 32 percent of the C-suite is considered data literate, according to Qlik. This is potentially holding these leaders back from encouraging the wider workforce to better leverage data to their advantage.

Similarly, only 21 percent of 16-to-24-year-olds surveyed by Qlik were deemed data literate, which suggests schools and universities need to invest more in programs to prepare the younger generation for the current and future workplace.

The first step in closing the workplace data literacy gap is identifying your team members’ data-related strengths and weaknesses. To help drive that process, Qlik has identified four types of data personalities, which we’ll examine in more detail below.

Data Experts

Data experts are those who excel at all things data. These individuals are often seen in roles like chief data officer or head of data analytics. These are the internal data champions, so it’s important to highlight these people early and often. That way, they can mentor and teach other team members who are less data-savvy.

Data experts should be involved in shaping company data training, which should offer continuing education on a range of critical issues from basic algorithmic operations to the role of data in a company’s given industry.

Data Engagers

The data engagers are familiar with working with data, but they are not as skilled as the experts. As such, they may become overwhelmed by massive data volumes or shifting analytical needs. Data engagers need assistance with refining their skills in data science, algorithms, and statistical analysis. They can support the data experts in spreading messages, serving as a bridge between data teams and other employees.

Data Learners

The data learners are excited to work with data and are open to learning more. Because data learners are not yet well-versed in data analytics, they are sometimes prone to taking data at face value, which can be problematic. Data learners need more training on how to dive deep into data, and they need more practice with critical and analytical data skills.

Data Disconnectors

Data disconnectors do not understand the value of data — and many professionals today are data disconnectors.

Forty-five percent of respondents to Qlik’s survey reported making decisions based on their gut feelings — typical data disconnector behavior. Disconnectors frequently ignore data, as they believe it doesn’t pertain to them or that it is not useful for the matter at hand. Data disconnectors need education to help them change their outlook, as well as training on data basics to lay the foundation for new knowledge.

Data has become essential to almost any role and industry. As data analytics only grows more prevalent, companies must consider what can be done to educate employees and improve their data literacy skills.

The encouraging news is that employees are willing and eager to learn. In fact, 78 percent of respondents to Qlik’s survey said they would be willing to invest more time and energy into improving their data skill sets. Now is the time to put the right processes in place.

Once employees have the confidence to understand and work with data, they can make smarter and more informed decisions. They’ll see that being data literate will help them do their jobs better and give them more credibility throughout their organizations.

Jordan Morrow is the global head of data literacy at Qlik.



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