In this episode, we explore an area of data analytics that everyone knows they need to improve but no one knows how to do it. That is data literacy. Data literacy ensures that business people have the skills to accurately interpret data represented in charts, tables, and dashboards, as well as the knowledge to use those tools to gather and analyze data on their own. 

To guide us through the nuances of data literacy and explain how to implement it in an organization, we invited a data literacy expert to share the secrets of his trade. Kirill Makharinsky is the founder of Enki, a San Francisco-based company that provides data-as-a-second language training services. Kirill is a serial entrepreneur, having previously co-founded ETG, one the largest online B2B travel companies in Europe, and Quid, a leading research and analysis tool. 

Key takeaways:

• A mark of a data literate person is that they substantiate their arguments with data.

• To improve the data culture, not only does the workforce need to have adequate data literacy levels but they also need the appropriate behaviors and habits to implement the data skills for decision making. 

• Organizations struggle with data literacy because most departments and teams are unaware of how data skills can make them more efficient at their tasks. 

• Each individual may have different needs and goals to become data literate. For this reason, enterprises need to research the right use cases and the right training programs to make the path to data literacy obstacle-free. 

• Enterprises can’t simply achieve data literacy by hiring data analysts and data scientists. It may be an indicator of becoming conscious about data and the value it holds but until all users become data literate, power users won’t be able to amplify the insights at the enterprise level. 

• To leverage massive open online courses (MOOCs) effectively, enterprises should formalize processes to ensure participation and engagement. 

• Makharinsky recommends a minimum 15-hour program (three to four hours for four weeks) to learn the foundational skills in contextual query language (CQL) followed by a mentorship program to dive deeper into the tools and skills.