12 Tips for Building Data Literacy

The digitization of our workplace is causing more and more tasks to be automated, which is causing another shift in how we value human contributions. A business strategy, process, and how to interact with customers require fewer workers to be producers of work and more workers to execute judgment and make decisions.

Nevertheless, workers need to learn how to use data to make better decisions faster if they are to succeed. Data is proliferating faster than ever before, and we have more data than ever before.

From the top-down, data literacy is becoming a critical skill as companies shift toward data-driven decision-making and operating models. We need to empower frontline workers with the skill to make informed decisions, even and perhaps especially so.

Employee data literacy should not be confused with technical literacy, and it does not include the high-level skills needed by a data scientist.

Instead, it is:

  • Reading, analyzing, and working with data
  • Using data to tell a story, make an argument, and persuade
  • Decisions made based on data insights aligned with business objectives
  • Improving efficiency, personalizing, and solving problems with data

What is Data Literacy?

Reading, analyzing, and communicating data is part of data literacy. Numeracy is more than just being able to work with numbers. It also involves knowing:

  • Methods and sources of data assessment
  • What can be done with a given data set?
  • Understanding data is important

12 Tips to Build Data Literacy

  1. Assess employee skills at the outset
    Understanding existing skills and agreeing on the proficiency level for different types of jobs is the first step to building data literacy. Thereafter, companies can develop upskilling plans.
  2. Democratize your data
    In technology, democratization is a constant trend. With the advent of new tools, once complex and specialized fields will become more accessible.
    The value of separating data from silos is now recognized by businesses. As a result, they use tools like Power BI to centralize their data, making it easily accessible and expanding the number of applications they can make use of. Having a single source of truth also helps them to achieve this. Everyone has access to the latest insights and the most current view without fragmented opinions or inconsistencies muddying the waters.
  3. Use the right tools
    Similar to tech, data has a steep learning curve. In addition, the less skilled employees would be attracted to sophisticated big data tools.
    Provide easy-to-understand tools for your organization. Data analysis and interpretation are made easier with the right tool. Use everyday tools your employees are familiar with instead of creating a new tool. It will be easier for employees to learn about data and generate insights when they have easy access to data processing tools.
  4. Perform assessments
    Conducting an assessment is the first step, even before you start training and developing tools for data manipulation.
    You can use assessments to determine what needs to be improved. Additionally, you can measure existing levels of data literacy before implementing new programs.
    In addition, the assessment allows you to know where gaps exist, how much training is required, and what tools and resources you need to put in place to help your team communicate best.
  5. Set a good example
    63% of enterprises believe that data literacy is important. Many companies are not supporting data literacy enough. Data literacy is not seen as important to the success of an economy by most decision-makers. When management lacks confidence in data literacy programs, employees automatically perceive them that way as well.
  6. Show support
    By encouraging employees to use their skills in manipulating data, you can help them build trust and believe in themselves. As they gain experience in processing data, give them more responsibility or ask them to provide data for their ideas or plans.
  7. Have a goal in mind
    Success is built on goals. Direction is impossible without goals. Consider mapping out data literacy for different levels of the organization in your data literacy program.
    Depending on your role, you will need different data literacy programs. A higher level of management may require a different set of data skills than those at a lower level. By setting goals, you can allocate resources and efforts more effectively.
  8. Provide incentives
    Most likely, you will find analyzing and interpreting data boring until you get used to it. Offer incentives or rewards to motivate employees to improve their data literacy skills.
    Prizes, team-building events, or conference attendance may be offered to improve their data literacy skills. Employees will be motivated to achieve the desired level of success and expertise.
  9. Develop a data competency baseline
    Do you want your employees to achieve a certain level of competency? At some point, employees should be able to perform certain tasks with data. The skills include recognizing data, asking the right questions, understanding the logic behind data, and communicating effectively.
    As a result, set qualifications for all employees. You can track the employees’ understanding of data, how they use the data, and how they interpret the results by using these qualification levels. To become data-literate, employees need to reach a certain milestone or score.
  10. Decentralize data access
    Many organizations make the mistake of trying to improve data literacy but denying their employees access to the data. So, they have a team that possesses data literacy but whose skills are eroding. Give your team more data access so they can use it and understand insights on their own.
  11. Promote knowledge exchange events
    Organizing knowledge exchange events can be internal (bringing together a specific department or aspect of the organization, for example, HR) or external (allowing employees to interact with industry experts or groups).
    Data and analytics events allow you to exchange ideas effectively, learn basics, discover how other professionals handle data, and benchmark data literacy skills.
  12. Reduce barriers to data literacy
    A company’s data literacy is often hindered by several factors. Culture is one, and applications supporting data are another. Complex or new applications may require several learning curves, or they may not integrate completely with existing applications or tools, which hinder decision-making.
    In addition, if administrators and managers adopt a discouraged attitude and aspiration, they may also stall any data literacy program. By removing any barriers to data literacy, the entire team will accept progress.

Final Thoughts

Developing data literacy doesn’t just mean helping your employees make the most of the information available to them. It is commonly believed that one of the biggest roadblocks companies face today is a lack of data literacy.

Businesses continue to rely on data to generate actionable insights, but if your staff is not kept up to date, it could inhibit their long-term growth.