data literacy

What is data literacy?

Data literacy is the ability to derive meaningful information from data, just as literacy in general is the ability to derive information from the written word.

The complexity of data analysis, especially in the context of big data, means that data literacy requires some knowledge of mathematics and statistics.

To deal with that complexity, many organizations are hiring data scientists, specialists who have advanced analytical skills. Some enterprises have also added a C-level employee, the chief data officer, to ensure that the organization realizes the full value from its data.

Nevertheless, because data is so crucial to a business's success, an increasing number of organizations demand some degree of data literacy from all employees. Organizations that are largely data-literate can function more efficiently and insightfully; those that aren't, might find themselves unable to transition to data-driven decision-making.

Data-driven organizations are at the forefront of the modern business enterprise. A culture of data fosters critical thinking and better decision-making throughout, leading to more effective performance internally and in the marketplace.

What skills are required for data literacy?

Data literacy skills fall into two categories: nontechnical and technical. The nontechnical category includes the following abilities:

• Knowing which data is appropriate to use for a particular purpose.
• Performing research into the business context surrounding data to be analyzed.
• Critical thinking about information yielded by data analysis.
• Recognizing when data is being misrepresented or used misleadingly.
• Communicating information about data to people lacking data literacy, an ability sometimes referred to as data storytelling.
• Developing domain knowledge through industry-wide research and studying trends associated with specific data.

Technical data literacy skills include the following:

• Data analysis itself -- mastering the statistical techniques involved in explicating data.
• Understanding data analytics tools and methods and when and where to use them.
• Interpreting data visualizations, such as graphs and charts; understanding dashboards and their underlying data sources.
• Programming languages aren't always essential but can be helpful in data science; they enable data-literate employees to work with algorithms for data analytics and even machine learning.

The challenges of data literacy

Promoting and nurturing data literacy in the enterprise can be challenging. There are several common barriers to implementation, including the following:

• Gaps in skill sets. As mentioned above, data literacy is about more than just good intentions; several skills are involved in achieving even basic proficiency with data. Some employees might lack the necessary skills and struggle with acquiring them.
• Employee resistance. Fostering a data-driven enterprise means bringing about a great deal of change across the organization; not everyone will embrace the transition to a culture of data, because many people are uncomfortable with change.
• Lack of champions. For a culture of data to proliferate in the enterprise, data literacy must have champions in every department; too often, data-literate employees are siloed in IT and business analysis.
• Data governance. Data literacy won't be practical unless the organization has effective data governance in place. Many businesses don't have effective practices and standards in place, and without them, a data-driven culture isn't possible.

How to become data literate

There are several key steps in transitioning to a culture of data within the enterprise, including the following:

1. Assess data literacy. How data-literate is the organization already? Surveying employees to assess existing skills and knowledge is a good starting point, to gauge the scope of the required effort.
2. Identify data literacy champions. Once the assessment is complete, natural leaders will likely have been revealed in various departments. Those leaders should be on board with the data literacy program and empowered to promote and nurture it.
3. Implement a data literacy program. A data literacy program should broadly educate the workforce, offering both nontechnical and technical training.
4. Measure success. It is important to establish criteria for success in data analytics implementations, data governance effectiveness and skill levels with common data analysis and management tools. Frequent evaluations of data literacy within the organization provide the basis for refinements.

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