What is data literacy?
Literacy as a whole is the capacity to make sense of a written word or text and derive information from it. Likewise, data literacy is the ability to understand and analyze data and – most importantly – to draw meaningful conclusions from it.
Data literacy also refers to the specific knowledge and skills required to leverage data optimally – and therefore troubleshoot issues and make informed decisions. It assesses the company’s ability to gather insights from data in order to guide decision-making. Also, since data is everywhere in our current data-driven world, it is now a non-optional expertise for many professionals in various fields.
The data literacy skill set
You can assess your teams’ data literacy by judging to which degree they possess the following skills:
- Being familiar with data analysis techniques (like descriptive statistics or hypothesis testing) and tools (like dataviz tools or analytics platforms) and being able to use them properly
- Mastering and making sense of various data formats (such as spreadsheets or databases) and data types (such as numerical or categorical data)
- Knowing which data type/format is the best fit for a given purpose
- Knowing how to share data with others thanks to accurate and relevant data visualization, as well as understanding the graphs and charts designed by others
- Being aware of data ethics and privacy rules such as data ownership and data security
- Being able to notice when data isn’t used properly and is interpreted misleadingly
How to advance data literacy
All in all, a data-literate stakeholder is a stakeholder who knows how to work with data.
But how does one become data literate?
- Continuous training: one cannot “reach” Data Literacy for good, because new data analysis techniques and tools emerge constantly. Data literacy actually refers to an ongoing process, which implies consistent learning and upskilling – for instance through statistics or dataviz courses.
- Hands-on experience: learning by doing is known as an excellent way to discover and master new skills. Business Users can collaborate with Data Analysts, who can help them try out various data analysis techniques, handle actual datasets or work on data visualization and communication processes.
Support: Data Analysts can guide Business Users in this whole process, by implementing quality monitoring and coaching programs, and writing clear-cut and detailed reports.
