Literacy is, broadly speaking, the ability to understand others’ ideas and communicate one’s own. When considering specific types of literacy, that same core principle remains. In this way, data literacy can be defined as the ability to understand information presented through data and to meaningfully communicate ideas using that information.
In other words, a data literate person will be able to make sense of the rows of information put before them, interpret it, apply it to a specific problem, and communicate a data-driven solution. These are the skills necessary to truly take advantage of all of the data businesses are collecting and storing and the skills that will be crucial to businesses staying relevant and competitive in the modern marketplace.
What are the components of data literacy?
Data literacy has several components, and businesses need to recognize their integrated reliance on one another. Investing heavily in one component while ignoring the others will lead to an imbalance in data literacy skills, severely hampering the potential of data-driven insights.
Accessibility
The first component of data literacy is accessibility. If businesses want a culture of data-driven insights and innovation, they have to make sure that employees have access to the data. This means that data should be available not just to data scientists and IT specialists, but to all employees. Using data to make decisions should become part of the cultural norm for teams across all departments, but that means those teams need access to the information.
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Critical Thinking Skills
Access alone is not enough to gain data-driven insights. Hiring, training, and promotion should all take into account the necessity of critical thinking skills. Companies need to provide opportunities for existing employees to hone these skills through meaningful projects that require them to take risks and do careful analysis. When hiring new employees, businesses should look for past experiences that demonstrate these crucial abilities.
Collaboration
Data silos will stifle innovation and growth. People need to be able to share insights with one another and communicate their findings and ideas openly. Businesses should create a culture in which sharing information is promoted and competition is used only sparingly and with the intention to promote collaboration rather than discourage it.
Tools
All of the other components of data literacy are made possible when companies invest in adequate tools and platforms that allow for easy, thorough access to data and provide the means to share it intelligibly. The tools used to collect, store, and disseminate data will all impact the overall data literacy competencies of the people using them.
Why does data literacy matter?
Most employees do not feel that their own data literacy skills are adequate. If individuals do not feel confident or competent in their ability to derive meaning from data, then much of that data will go unused. Businesses are spending considerable time and money collecting and storing data, but without the people and cultural shifts to make it meaningful, up to 73%of that data is not being used.
The world of data is changing rapidly, so even those who have specialized training in data analytics can feel left behind quickly. Furthermore, most employees do not have this specialized training, and their understanding of how they can leverage data in their own daily tasks can be shaky. Investing in training opportunities that teach employees not only how they can use data in their own roles but also how data is being used throughout the company can provide a framework for skill development and future collaborations.
Data literacy is a fundamental skill in today’s rapidly-changing world, and it can no longer be the specialized expertise of a select few. People with all kinds of backgrounds and daily tasks will need to be data literate in order to stay relevant and competitive.
James Nanscawen
As Director of Enterprise Analytics, James helped Thomson Reuters establish data management capabilities and an enterprise-wide analytics competency.
Latest posts by James Nanscawen (see all)
- See It to Believe It: Why Humans Need Data Visualization - December 10, 2019
- Understanding and Building Data Literacy Skills - November 19, 2019
- Two Challenges Blocking AI From Its Full Potential - November 5, 2019
