Organizations are struggling to remain competitive and relevant. Organizational success means the ability to act quickly to changing environments based on as current data as possible. To achieve this goal, organizations are increasing their use of both structured and unstructured data. Organizations are being overwhelmed by both traditional and new sources of data — with limited organizational expertise to leverage this data.

The Value of Data

The interpretation of data leads to information, which in turn, leads to knowledge. Knowledge is the derivative of data and is highly cherished. Knowledge can take the form of a final decision or a component to be considered when making a decision.

Organizations that are primarily data-driven achieve 5% higher productivity and 6% higher profits when compared to organizations that are not data-driven, according to an article in the Harvard Business Review, “Big Data: The Management Revolution.”

Regardless of how knowledge is used in your organization, you first must be able to identify, quantify, clean, organize, standardize and establish the credibility of the data so it can be used as the basis of knowledge.

What Is Data Literacy?

Data literacy is defined as the ability to find, read, manipulate, analyze and discuss a position with data. Data literacy is a critical component of data analytics.

The Need for Data Literacy Within the Organization

Most organizations are starting to realize their deficiency regarding data literacy as they start to digitize their processes. A recent study found that only 52% of C-level executives are fully confident in their use of data and 45% say they frequently make decisions based on “gut feeling” rather than data.

Data is being used throughout the organization at an increasing rate. Data is used for both strategic planning purposes and tactically for day-to-day operations.

Employees also have concerns regarding their data literacy skills. “Big Data: The Management Revolution,” also found that only 21% of employees are fully confident in their data literacy skills.

The Challenges With Using Data in Decision-making

Data analysts, scientists, engineers and data consumers are confronted with what are termed the Five V’s:

    • Velocity: This refers to the speed by which new data is being created and captured.
    • Volume: This refers to the volume of new data being created and captured.
    • Value: This refers to the perceived value the data contains. This often refers to the relevance of the data to its intended usage.
    • Veracity: This refers to the overall quality of the data.
    • Variety: This refers to the various forms and types of data that are being created and captured.

Data is constantly changing and its usage evolving to meet the needs of every organization.

The Need for Data Literacy Training

Given the broad application of all types of data throughout an organization and its proven benefits, it is necessary that data literacy training takes place at all levels of an organization. Individuals from senior-level executives to the most junior member of the association must have data literacy training.

It is also critical to make data literacy training a part of each organization member’s annual training plan and make data literacy part of each new employee’s onboarding process.

The Many Facets of Data Literacy

Data literacy has many facets that must be addressed in any data literacy training program. The range of facets and the skills necessary are broad in scope.

These skills include such areas as where to find data, how to validate the data, assessing the relevance of the data to the information being desired, how to clean, organize and categorize the data, how to analyze and interpret the data, how to present the data, etc.

Data Literacy Training

Data literacy training should prepare each organization member to feel comfortable and confident in using data to conduct their responsibilities and have a high level of confidence when presenting their analysis of the data and associated recommendations.

All data literacy training must be purpose-driven training. Initial data literacy training can utilize online training (virtual training) to reach as many organizational members as quickly as possible to establish a base-line of minimal knowledge. The training should also be designed to meet not only the current but also the future data needs of the organization as the overall organization data maturity increases. (See Figure 1.)

More advanced data literacy might benefit from in-person training to communicate more complex aspects of data literacy.

Here are some examples of data literacy training components:

    • Data source identification.
    • Data validation.
    • Data cleaning, organizing and labeling.
    • Data version control.
    • Understanding the organizational data need at both the strategic and tactical levels.
    • Data interpretation and analysis.
    • Data presentation and visualization.

Some of the above topics will include an overview of the capabilities of several well-known and easily accessible software applications. These applications include Excel, Google Sheets, PowerBi, Tableau, and RefinePro.

The goal of data literacy training is not to make the recipient of the training an expert in using any of the above applications. The goal is to expose the student to the features and benefits of these applications when managing data while performing their jobs.

Assessing Your Current Data Analytics Maturity

Data literacy skills vary from organization to organization and even within various functions of an organization. As a result, training must be broad and ongoing.

Organizations are usually assessed and classified into five levels of data analytics maturity. These levels are:

    • Descriptive analytics
    • Diagnostic analytics
    • Predictive analytics
    • Prescriptive analytics
    • Cognitive/self-learning analytics

The first question organizations should ask is where they stand on the chart. The best means to make this determination is to conduct a data audit.

A Data Audit Template

One data audit template was developed and published by The International Association for Data Quality, Governance and Analytics. This guide contains information and a series of questions that can assist you in your organizational data assessment.

This template can also act as an excellent guide for not only establishing your first data audit to determine your organization’s maturity level but can also act as an annual template to assess the effectiveness of your data literacy training.

The ROI On Data Literacy Training

Every organization seeks to find a quantitative means of evaluating both the efficiency and effectiveness of any training program. Data literacy training is no exception. There are several metrics that can be used to measure your program. It is critical that you establish a baseline for each of these metrics prior to the start of your data literacy training.

Possible metrics can include:

    • Efficiency (process efficiency): How long does it take to complete a process?
    • Efficiency: The number of individuals requested to complete a process.
    • Effectiveness: The quality of the decision that was based on data.
    • Effectiveness: The number of decisions that are solely based on data and not gut decisions.
    • Effectiveness: The number of bad decisions.

Units of measure for these metrics can include:

    • Time
    • Dollars
    • Resources
    • Scalability
    • Sustainability

Analysis of these metrics can take the form of:

    • Benchmarking
    • Time series analysis


Every organization uses data in one form or another to manage its operations and to make strategic decisions. Today, data is recognized as a competitive advantage to those organizations that know how to use the data they have. Most organizations are not prepared with general organization knowledge on how to clean, organize, validate, interpret and use data.

Organizations are realizing their data literacy deficiencies and are seeking training as quickly as possible to advance their employees’ skills. An organization’s success is based on its employee’s ability to use data to perform their tasks.