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Data Literacy Best Practices

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data literacy best practices

The term “data literacy” refers to the ability to extract meaning from data. IDC, the global market intelligence expert, has predicted a “ten-fold increase in worldwide data” by 2025. It’s no wonder many data-driven organizations are in a race for an enterprise-wide data literacy initiative, with the vision of transforming their employees into data-literate workers. Global enterprise leaders now realize the importance of data literacy best practices to make this transformation a reality. 

In a data-first age, organizations must become data-literate for enhanced productivity, higher efficiency, and more competitiveness. While anyone seeking a career in data science, analytics, or business intelligence (BI) must begin with data literacy skills, data literacy is not just for aspiring professionals in the data field. All employees of an organization are typically mandated or encouraged to take data literacy courses, workshops, or training programs to remain relevant in their businesses. 

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Without data literacy skills, an average business employee will fail to communicate and grow in an increasingly data-powered business world. A lack of understanding of how data works will cause workers to misinterpret data, which can lead to negative effects on their data analysis and decision-making in their daily work life. 

Data literacy is also essential to organizations leveraging big data to remain competitive and efficient in the data age. Being a data-literate organization means being aware of the implications of different actions, having processes in place to manage and analyze the data, and understanding the value it provides to be able to confidently take action.

Mckinsey has made these predictions about tomorrow’s data-driven enterprises:

  • By 2025, data will enable “smart workflows and seamless interactions among humans and machines” in corporations. 
  • By 2025, most employees will leverage data to support their daily work, leading to agile resolution of problems.
  • By 2025, cloud, in-memory, and data technologies together will facilitate “vast networks of connected devices,” delivering insights in real time. 

How Can Organizations Become More Data-Literate?  

This Gartner guide indicates that the “appetite for improving data literacy” is strong globally, with 59% of companies aspiring to become more data-literate.

To improve data literacy, companies must be conscious of what data they are collecting, how that data is stored, and if the data is of high quality. In a data-literate organization, the top management often has a solid plan that describes how they will handle their data. A Data Management strategy describes where data lives and who has access to it. It also describes how data is processed and who is authorized to access it. 

The Data Management strategies in these organizations are backed by strong Data Governance programs, which mandate rules governing data access controls, data ownership, and data use policies. Some of these rules and policies become the basis for data literacy best practices, used for designing and developing data literacy training programs.

Measuring the Effectiveness of a Data Literacy Training Program

Data literacy programs can be measured by tracking the number of employees who have positive attitudes toward data, have increased confidence in their data skills, and are eager to learn more about data. Also, the percentage of employees willing to share their skills and knowledge with others. 

Another way to measure the effectiveness of a data literacy program is to see how many employees are improving their skills daily. This is important because it shows that acquiring data skills isn’t a one-off experience but rather a process that requires continuous growth.

Top Data Literacy Best Practices

Another notable feature of measuring data literacy training programs is the continuous testing and evolution of newer and better data literacy best practices:

  • Build a team of subject matter experts. It is crucial that data analysts partner with subject matter experts. These experts will help with the interpretation, exploration, and processing of data. Without the assistance of subject matter experts, data analysis efforts will be limited and ineffective. Subject matter experts can include data scientists, data engineers, data analysts, and business leaders. The ideal composition of the team will be based in part by the scope of the operations and the data source being used. Depending on the source and the type of data, a data engineer, data scientist, or analyst may be included in the analysis team.
  • Develop automation for data processing and analysis. Automation is important when it comes to a robust data analysis program. Using tools such as AWS Lambda can create an automated workflow for data processing and analysis. AWS Lambda is an excellent resource for increasing operational efficiency by automating tasks that were previously managed manually. It can create an environment for processing and analyzing data that is powered by AWS services. This helps to ensure that the data program is highly scalable and reliable.
  • Build an understanding of data sources and use cases. This will assist in the exploration and interpretation of data, as well as in crafting appropriate use cases. The type of data that the data source provides, the key attributes of the data, and the rules that govern the data’s use are important. An understanding of these aspects can build an understanding of the data’s intended use cases, which can be incorporated into operations.
  • Use APIs to integrate data into operations. One of the best practices for data analysis is to incorporate APIs into operations. APIs are gateways that allow data from one source to be accessible to the system. They allow integration of data from multiple sources, thereby creating a more reliable and scalable program. APIs can help create an ecosystem for data processing and analysis, and come in various types based on features and functionality.
  • Get commitment from the C-suite. If the C-suite becomes data-literate, they will successfully drive a data-driven culture throughout the organization. Also, no longer will the senior management have to turn to analysts for their routine decisions.
  • Start a training program combining hard and soft skills. A combination of data analytics skills, statistics, and communications will probably be the best training program to transform business users into power users.
  • Incorporate reward systems. Frontline employees, routinely engaged in customer interactions, peer training, and company growth through self-service data analytics can be rewarded for their efforts. 
  • Collaborate with IT. The data-literate employees should routinely collaborate with IT teams to clean, optimize, and structure data.  
  • Create a “Center of Excellence.” Enterprises developing teams of power users and data-driven people will easily create Centers of Excellence. This strategy goes a long way toward evangelizing the rest of the company.

Conclusion

According to Daniel Castro, director of the Center for Data Innovation and vice president of the Information Technology and Innovation Foundation, “There’s this new skill set that pretty much everyone needs, moving to a data-literate world where understanding how to use and interpret data will be essential across the board.”

Data literacy skills are now a necessity to “unlock the true business value from analytics.” By following data literacy best practices, organizations can help their business users get involved in: 

  • Locating trusted and relevant data visualizations
  • Viewing “analytics in context” for a broad understanding of the algorithms and information behind the data
  • Accessing the metadata related to data analysis based on enterprise Data Governance standards

Image used under license from Shutterstock.com

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