Data literacy describes the ability to read, work with, analyze, and argue with data, according to Raul Bhargava and Catherine D’ignazio from MIT and Emerson College.
Easy access to data sets are essential to exercising these skills. All employees in an organization involved with data-driven decisions should learn to think critically about the data they use for analytics and how they assess and interpret the results of their work. A business team could discover where data needs clarification for a project, for example. Augmented analytics for quickly providing critical data and making reports more understandable is important to data literacy. Businesses must take steps to educate their employees to become data literate.
Other Definitions of Data Literacy Include:
- “Engaging people so that their actions and decisions drive better decision making for the organization.” (Amber Lee Dennis)
- “Having the ability to read, write and communicate data in context, including an understanding of data sources and constructs, analytical methods and techniques applied, and the ability to describe the use case, application and resulting value.” (Gartner)
- “Using data effectively for business actions and outcomes.” (Forbes)
- “Deriving meaningful information from data, just as literacy in general is the ability to derive information from the written word.” (Techtarget)
- Understanding what data means: including how to read charts, drawing correct conclusions, and recognizing when data is used in misleading or inappropriate”. (Eastern Michigan University)
Data Literacy Examples Include:
- Using the Adoptive Framework to create a Data Literacy Program.
- An employee, working with spreadsheets, learns why a set of data led to a decision, gains deeper understanding of the business domain or argues for a different course of action.
- A work team spots where data needs clarification for a project.
Businesses Need Data Literate Employees in order to:
- Communicate in a common language of data to better understand conversations about it
- Spot unexpected operational issues and identify root causes
- Prevent making poor decisions due to data misinterpretation
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