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Every organization, in every industry, should adopt a data-literate culture. What does that mean? How can data literacy be socialized in existing organizations?

In many organizations, data is the product.  In most other enterprises, data is an essential component of operations and decision-making.  Historically, the access to data has been in the hands of selected experts, such as analysts or the decision-makers.  With the explosion of data access to practically anyone, the management of data (for ensuring properly defined and high quality data), and the literate use of that data has become a crucial concept that should be supported by every organizations.

Concepts of Data Literacy

There are many definitions of “data literacy,” and this is viewed as a reason that the concept is not well-understood or implemented effectively in most organizations.  One good definition comes from an InfoWorld article “In the context of a business, it’s the ability for employees to derive meaningful insights from data and apply those insights in a way that benefits the organization.”  Another definition has been crafted by EWSolutions, a premier data management consultancy, that has resonated with clients and some external experts. “Data literacy is the ability to read, work with, analyze, understand, and debate while using data effectively.  Data literacy focuses on establishing and socializing the competencies involved in managing and working with data to achieve stated goals.”

Stated simply, data literacy means that a person is able to identify a question or situation clearly, know what data is needed for that issue and where to get the right data, how to read / interpret that data objectively, and how to use the results to solve that problem or address that situation.

The variety of definitions demonstrates that there is limited consensus around the meaning of the term “data literacy” but this variety shows that the concept has value and should be promoted by organizations and through education

Essential Points in Developing Data Literacy

The development of data literacy includes several important aspects, including the understanding and appropriate use of the right tools and technologies for the intended purpose.  However, people need to learn to think critically and analyze data to choose the correct data and the suitable analytical and presentation methods for the situation.   

Data literacy requires a culture where accurate data is valued and managed as a critical component for operations and decision-making.  Traditionally, organizations restricted data access by role, and through the use of various technical frameworks and barriers, including the requirement that all data acquisition must be sent to the Information Technology unit.  With the advent of widespread data availability, these restrictions no longer apply or provide value.  It is important to allow data access without the need for an intermediary; this will be a change for many organizations.  A team (data governance) should identify the appropriate sources of data for each department / role and have the proper access rights granted accordingly. 

Once people have access to the right data, expect decisions to be made based on “facts and data” rather than intuition.  Again, this will be a change for many people in most organizations.  Use accurate data, from the right sources, to support decisions and other actions.  Encourage others to do the same, by asking, “Do you have data to support this position?” and by sharing appropriate data that reinforces statements regularly.

Socialize the adoption of a data-literate culture through the development of an enterprise approach to data management, including data governance and data stewardship, effective metadata management, and the expectation of high data quality in all applications across the organization.  Establish best practices and processes for data management and data usage throughout the organization, and expect their consistent implementation.  Develop appropriate education and training in all aspects of data literacy for the organization’s staff and leadership.

Ensure proper data sharing across the organization so people can view the data, manage it, work with it, and collaborate using data and the suitable technologies and tools.  Identify the optimal tools, technologies, and infrastructure to enable data sharing and decision-based data usage.  Evaluate the use of multiple data sets to answer a question or address a situation; are the additional sources necessary, and if so, for what purpose?  Determine how the data from the multiple data sets should be combined to maintain an objective view of all the data used.

Learn about data visualization and its strengths and challenges, so that the data can “tell a story.”  However, remember that data visualizations can be manipulative, so exercise care in the ethical and apt use of infographics, dashboards and scorecards, and other forms of data visualization to tell the unbiased story.  Ask questions such as, “Should the data be presented in a bar graph, a spreadsheet, an infographic, etc…?”  “Does the use of color influence the presentation and / or the analysis and interpretation?” 

Establish formal data governance and data stewardship for the organization, so the proper data management policies and processes can be applied consistently.  Do not neglect data and information security practices, to ensure proper security measures support privacy and control of sensitive or competitive data.

Learn to think critically about the data and about how it is and can be used.  Choose the right data sources for each task, problem, and situation.  Data is often outdated, incomplete, missing, or just not appropriate for the issue. Acquire critical thinking skills for data usage; data interpretation can be performed in a variety of ways, and many of them can lead to inaccurate conclusions.  Some of these challenges include confusion bias (identifying results that confirm existing beliefs), correlation vs. causation (two points may be connected, but one may not be the cause of the other), and assuming small differences always have a major effect.  All users of data should be able to question what data tells them and apply analytical skills to interpreting the data and its presentation according to the situation, problem, or task.  Strive to ensure that all results are based only on accurate data.

Data Literacy Steps

  1. Articulate a question, a problem, a proposal, or a situation clearly and fully.
  2. Identify the available data source(s) and assess each source’s fitness for addressing the question, problem, etc..  Fitness includes the completeness, timeliness, accuracy, and validity of each data element in each source, and the overall suitability of the source.
  3. Understand the data and its content in relation to the issue.  Evaluate the data in the context of the issue, including how the data is presented.  Is the data presentation objective, is it biased, is the presentation of data incomplete to skew analysis?
  4. Interpret what the data means in relation to the problem; apply analysis and critical thinking using the data selected. Some criteria for critical thinking include how the data addresses the problem, why this solution has been proposed, what are the implications of the possible solution based on the presented data.
  5. Create relevant questions to analyze the data and challenge the answers drawn from the data until objective satisfaction is achieved.
  6. Act on the results: answer the initial question, solve the original problem, offer solutions to the existing situation, develop a suitable alternative, etc.  Include facts and data in this step for support. 


Data literacy is a skill that is a vital element in every professional’s development.  The explosion of data availability and the challenges presented by the variety of data quality in many sources require that everyone learn how to become “data literate.”  Organizations should encourage and socialize data literacy concepts and practices continually.


Anne Marie Smith, Ph.D.

Anne Marie Smith, Ph.D. is an internationally recognized expert in the fields of enterprise data management, data governance, data strategy, enterprise data architecture and data warehousing. Dr. Smith is a consultant and educator with over 30 years' experience. Author of numerous articles and Fellow of the Insurance Data Management Association (FIDM), and a Fellow of the Institute for Information Management (IIM), Dr. Smith is also a well-known speaker in her areas of expertise at conferences and symposia.

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