top of page


As organizations become more data-driven, poor data literacy will become an inhibitor to growth. According to Gartner Annual Chief Data Officer Survey, 2019 poor data literacy is ranked as the second-biggest internal roadblock to the success of becoming data-driven.

At the same time, McKinsey Global Institute indicates that data-driven organizations are 23 times more likely to acquire customers, six times as likely to retain those customers, and 19 times as likely to be profitable as a result. It is no wonder that leading companies are focusing now on data literacy.

data-driven organizations are 23 times more likely to acquire customers and 19 times as likely to be profitable

By 2022, 30% of Chief Data Officers will partner with their Chief Financial Officer to formally value the organization’s information assets for improved information management and benefits.

According to Gartner by 2023, data literacy will become an explicit and necessary driver of business value, demonstrated by its formal inclusion in over 80% of data and analytics strategies and change management programs.

Watch the video or continue reading below it

What is data literacy?

Gartner defines data literacy as 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.

This all boils down to a simple question, “Do you speak data?”

To be data literate you need to have the ability to derive meaning from data and communicate that meaning to others. Data literacy competencies include the knowledge and skills to read, analyze, interpret, visualize and communicate data as well as to understand the use of data in decision-making.

Data literacy also means having the knowledge and skills to be a good data steward, including the ability to assess the quality of data, protect and secure data, and take responsibility for its ethical use. The ethics part of this should be non-negotiable.

Data literacy is an underlying component of digital dexterity, which is an employee’s ability and desire to use existing and emerging technology to drive better business outcomes, another important skill for digital business.

MIT professor Catherine D’Ignazio and research scientist Rahul Bhargava describe data literacy in a paper as the ability to:

  • read data, which means understanding what data is and the aspects of the world it represents.

  • work with data, including creating, acquiring, cleaning, and managing it.

  • analyze data, which involves filtering, sorting, aggregating, comparing, and performing other analytic operations on it.

  • argue with data, which means using data to support a larger narrative that is intended to communicate some message or story to a particular audience.

What you should do now?

Start by assessing data literacy at your organization with a few questions:

  • How many people in your business do you think can interpret straightforward statistical operations such as correlations or judge averages?

  • How many managers are able to construct a business case based on concrete, accurate and relevant numbers?

  • How many managers can explain the output of their systems or processes?

  • How many data scientists can explain the output of their machine learning algorithms?

  • How many of your customers can truly appreciate and internalize the essence of the data you share with them?

Then Establish a data literacy program

Start by identifying the fluent and native data speakers. They carry the data literacy torch for you and act as change agents.

Second, look for areas where communication barriers mean that data isn’t being utilized to its full business potential.

Third, try a data literacy proof-of-concept workshop in an area where language gaps exist. Have participants describe real-life common use cases as well as a use case specific to the organization.

Fourth, ensure that teams are speaking data in all meetings when discussing business outcomes and in other business situations - lead by example.

Finally, ask Data Product Business for further assistance and guidance to make your data track smooth and efficient.


bottom of page