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Data Literacy Barriers and How to Overcome Them: Expert Advice

Data Literacy Barriers and How to Overcome Them: Expert Advice

Data Literacy Barriers and How to Overcome Them: Expert Advice

This is part of Solutions Review’s Premium Content Series, a collection of contributed columns written by industry experts in maturing software categories. In this submission, Data Society Co-Founder and CEO Merav Yuravlivker offers advice on data literacy barriers and how to overcome them.

SR Premium ContentData literacy is far more than the sum of tools and terminology you have at your disposal. This holds true at both the individual level and the organizational level. Beyond understanding data, a data-literate workforce knows how to make optimal use of data assets by effectively extracting and communicating relevant data-driven insights. Organizations keenly aware of the critical role this kind of self-service business intelligence can play in driving their success are wisely investing in data science programs. However, common barriers impede the collective shift that organizational data literacy demands.

A successful transition to a data-driven culture is a function of both the correct skillset and the right mindset. One obstacle many organizations face on the road to data literacy is a limited understanding of how data science skills and tools fit into the larger picture of their industries. Data literacy, storytelling, and other technical capabilities don’t exist in a vacuum. Like other skills, they are elements of a broader collection of tools that are most effective when proficient practitioners combine them with conceptual knowledge about how to leverage them to meet specific industry needs.

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The contextual application of data literacy skills is a skill in and of itself. Just because you know statistics and have a programming background doesn’t mean you know how to use those skills to solve marketing problems or that you are effectively applying them to day-to-day challenges you face. This is why scenario- and project-based learning are so important. Though developing bespoke scenario-based learning opportunities can be both complex and very expensive, it is often worth the investment based on the efficiencies for team members that result from the initiative.

In addition, the continuum of conceptual skills ranges from basics, such as descriptive statistics, to advanced probabilistic models. Employees fall at various points on this continuum based on their previous knowledge and comfort with these skills. Viewing these tools as isolated assets, rather than in relation to other skills along the spectrum of data science capabilities, hinders a deeper understanding of where individual employees are on their journeys to data literacy and where they can go. Therefore, it is vital for managers, their teams, and the organization to have a clear learning map of technical and conceptual skills that is tailored to individual professionals and their particular skills.

Couple these pain points with the budgetary pressures that Learning & Development (L&D) teams face in today’s environment, and it can seem daunting to eliminate these common barriers to data literacy. To provide a larger conceptual framework as a guide for learners, it’s important to develop a roadmap of skills that allows learners and organizations to navigate the field of technical skills and place them into an operational and technological context. This roadmap helps people and teams calibrate their level of data maturity as compared to the industry at large and in light of tools that are widely commercially available. If you think of individual skills as building blocks, this roadmap helps L&D teams and learners envision the fully realized structure they are collectively working toward.

Further, learning is most effective when it is in a context that links it to the real-world applications that matter to students. Instruction that draws a clear connection between the course content and the functions students perform in their work helps learners absorb and retain their training. For this reason, there’s an opportunity to leverage AI in this process to develop customized training materials based on real organizational data sets, ensuring that students can perform analyses under conditions most relevant to their work. This enables users to tailor courses and develop entire learning roadmaps that are appropriate for each learner’s level of existing knowledge and training goals.

Another important factor to overcoming these barriers is developing awareness among managers, learners, and L&D teams of the boundless potential a data-driven culture holds. Attaining fluency with data opens avenues toward organizational and individual professional growth opportunities, and being able to visualize a range of possible routes and destinations is a powerful driver of proper understanding. Learning platforms and initiatives should help managers and L&D teams gain a broader and deeper knowledge of what their workforce can accomplish through data science training and give them the tools to craft tailored learning journeys that propel them toward success.

While the road to data literacy may be littered with obstacles, clearing the way need not be a daunting task. An important first step on this journey is to cultivate an informed concept of what it means to be data literate, particularly among organizational decision-makers. In addition, achieving meaningful data literacy—or the level of data maturity that transforms organizations—requires acquiring technical, conceptual, and contextual knowledge about the application of data science across roles and departments. Taking these actions can help prepare organizations to conquer their data literacy challenges through the development of an understanding of how exactly their data skills will ultimately serve them. Thus guided, organizations can successfully navigate the roadblocks between them and the promising future that data literacy holds for them.

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