Data Literacy

This content is part of Tableau Blueprint—a maturity framework allowing you to zoom in and improve how your organization uses data to drive impact. To begin your journey, take our assessment(Link opens in a new window).

Every day, your people use data to make better decisions in their personal lives—from what route to take somewhere, to monitoring diet and exercise, and managing finances. Think about that: Almost everyone uses some kind of data-driven tool to measure their progress or modify behaviors for improved outcomes. So why should it be any different at work, especially when the amount of data collected has never been greater?

To become a more data-driven organization, you will quickly realize that it requires more than deploying software and considering the work complete. Even with the right data and analytics technology, it’s not easy to make data-driven decision making a default behavior for everyone across your organization.

The fundamental skill people need is data literacy—defined as the ability to explore, understand, and communicate with data. By prioritizing data literacy as the baseline, you can empower your people with the new language of business, just as humans have used symbols, words, and language throughout history.

Data literacy is not only a skill for data scientists and analysts; it should be viewed as the prerequisite for additional skills which are developed on the path to proficiency. Everyone, regardless of their position or department, must know and embrace the language of data to help their organization tackle its difficult problems (e.g., new or developing market trends, customer activity and needs, or unexpected crises). This means putting tools and processes in place that people will actually use, teaching them new skills, encouraging new behaviors and continuous learning, and recognizing when there are data-related wins.

Your users will be vast and varied in their data skills, as well as the skills that need to be developed. To assist your organization with establishing data literacy as a fundamental skill, Tableau’s Data Literacy for All free eLearning offers five hours of practical training and resources to help everyone. The following topics are covered within seven on-demand eLearning modules:

  • Foundational data literacy concepts
  • Recognizing well-structured data
  • Exploring variables and field types
  • Exploring aggregation and granularity
  • Understanding distributions
  • Understanding variation for wise comparisons
  • Using correlation and regression to examine relationships

While modules can be completed in any order, employers should ensure completion of all modules by manually collecting the certificate of completion. Employees can access their certificate of completion as a PDF, download it immediately after finishing the seventh module, and provide it to their employer. Organizations may also consider holding competitive team or individual challenges to encourage engagement and training completion as they collect certificates. For organizations with varied data skill levels, this is a simple, easy way to also test basic data literacy, so you have a baseline to build from.

Having and developing a community will also go a long way in nurturing a more data literate workforce. Community spaces like a regular user group or an internal discussion forum offer your employees a place where they can explore and communicate with data using real-world scenarios or company-specific data challenges. In these venues, it’s easier to practice these evolving skills, and identify knowledge gaps or cultural behaviors that may hinder individual or collective progress—obstacles that aren’t always captured when people just complete a training.

Once your users have established the baseline skills by completing Tableau’s Data Literacy for All free eLearning, continue to promote educational development with the prescriptive Tableau learning paths. For more information, see Skills by Tableau Education Role. For smaller deployments, see Skills by Tableau License Type.

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