Data Literacy: Its Importance and How It Helps Build a Data-Informed Company

May 5th, 2022

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Modern organizations of all sizes are built on data. Data drives product launches, hiring, competitive analysis, sales, financial decisions, and everything in between. Yet studies show that only 25% of employees feel they’re fully prepared to use data effectively.

As companies attempt to democratize data, the value of having a data literate workforce cannot be emphasized enough. Research shows that ninety-three percent of business decision-makers believe it is important for employees to be data literate. Today’s business environment demands that for a company to remain competitive, its teams across departments, functions, and corporate hierarchies must have the ability to access, organize, and analyze data.

Let us understand the meaning of data literacy, discuss how building data literacy skills gives organizations an edge, and finally demonstrate how to build a data literacy program that fosters a culture of data.


What is data literacy?

Gartner provides a detailed definition of data literacy that can be broken down into three specific abilities:

  • Ability to read, write and communicate data in the context
  • Ability to understand data sources and constructs, analytical methods, and techniques applied
  • Ability to describe the use case, application, and resulting value of data

Similar to general literacy (i.e. reading and writing in one’s native language), data has its own complex “language” of standardized terms that, when understood, enable others to read data sets, properly pull data for reports, analyze the information, and communicate conclusions in an intelligent manner.

Every company is a data company.” wrote Amir Orad, the CEO of Sisense in Forbes and hence data literacy is no longer a “nice to have” it is essential for any company to function competitively.


Why is data literacy important?

We are living in the data decade. The purpose of building data literacy skills is to take full advantage of data availability and work data into the fabric of your business so you can improve and scale. Underneath that umbrella, data literacy can accomplish the following goals:

Enable communication

Data science expert Piyanka Jain stated that “data is the new currency, it’s the language of the business. We need to be able to speak that.” If a company doesn’t speak the language of data, miscommunications that fall victim to subjectivity and bias proliferate, leading to poor productivity and decision-making.

Smooth employee relationships

When departments are playing phone tag with IT analysts to pull the right data for reports or projects, relationships can become strained. When all employees are data literate, each group is empowered to manage their own data with less support from IT.

Break down corporate silos

It’s easy to lose track of what data a company owns and where it resides. This can result in data silos. Building data literacy skills will ensure employees are accessing the right data from the right place — a central repository accessible to everyone — so that reporting and results are consistent.

Improve analytics

When business users have data literacy skills, they are better equipped to use the right data to build the right reports that will lead to valuable insights. They can also spot inconsistencies and ask contextually relevant questions that will help them improve their approach to analytics.


Who needs to be data literate?

We’ve already answered this question to some extent: Everyone needs to be data literate. The pursuit of data democratization is useless unless everyone at an organization who has access to the data knows how to use it. Let’s drill down and get a better understanding of why specific individuals across an organization benefit from data literacy.

The executive team

Executive teams, including Chief Executive Officers, Chief Marketing Officers, and other similar positions, have heavy responsibilities that require precise analytics. These individuals must reach beyond data literacy and see technology as a core company strategy and enabler of growth. Nike took this to heart in October 2019 when it announced that it would replace its CEO of 13 years, Mark Parker, with John Donahoe, a veteran tech executive. This move signaled an organizational shift toward data-driven business with literacy as a top priority.

Department heads

Department heads include leadership at the director or VP level. One core area where these individuals need strong data literacy is in quarterly budgeting. They must pull data for analysis so they can determine where to cut the budget and where to invest. This allows them to deliver more effectively on the company strategy. Budgeting — in addition to setting production goals, monitoring salaries and headcount, and using technology to enable more efficient processes — is just one area where data literacy is essential to department heads.

Information technology

Information technology teams build the infrastructure and architecture that supports all data assets and are responsible for maintaining data integrity, data standardization, and data security for the entire company. Data literacy is integral to their work; it enables them to create systems, processes, and applications that support the needs of everyone across the organization.

Individual contributors

From sales and marketing teams that pull regular campaign performance data, to supply chain managers who balance supply and demand, to human resources professionals who evaluate employee satisfaction, every individual in every department needs to be data literate.

Knowing how to work with and analyze data enables each of these roles to accurately interpret the results of their reports and make cost and revenue adjustments that will keep the company competitive.


How can my company build a data literacy culture?

Developing functional data literacy across an organization starts with company culture. While some teams may be proactive in their data education, it’s the responsibility of company leaders to provide the tools, methods, resources, and expectations that will create a data literate workforce.

So how do you begin?


Creating data literacy program

There are multiple ways to approach the development of a data literacy program. You can invest in an online platform, build an internal program, or follow thought leaders who provide a framework for teaching data literacy. For example, this article by Dataversity suggests a four-step data literacy process: communicate, assess, train, and iterate.

Regardless of what method you use, there are some basic data literacy criteria worth establishing at your organization, such as:

Knowing the level of skill each team needs and why they need it

While an IT team may need training in basic data science and your department heads need a solid understanding of business statistics, your individual contributors might only need a rudimentary understanding of the data they need and what it means. Your management teams should work with their direct reports to identify what data literacy skills they should pursue, and explain to them how these skills will help them meet their goals.

Know what tools they will need

Whether your organization is using business intelligence (BI) applications, collaboration tools, data visualization platforms, or all of the above, each team needs a primer on how and when to use these tools. Different teams may have access to different tools — and maybe using them in different ways — so catalog which tools and features apply to which job functions so you can build out training accordingly.

Establish common language

Facilitating communication is a core function of data literacy, and establishing common terms and definitions is the first step on that path. Publish a standardized data dictionary and business glossary and assign a committee to update and maintain them so your employees will always have reliable reference points.

Acknowledge common analytics pitfalls

Analyzing data is never as easy as it sounds. There are a host of factors that can account for any given conclusion, and it’s easy for even the most intelligent professionals to misread data.

Here are some common pitfalls to avoid:

  • Confirmation bias: Searching for data that supports one's preferred conclusion. Example: If a marketing analyst believes that email is the most effective marketing channel, they might search for studies that prove that out, rather than comparing data on the performance of a variety of channels.
  • Cherry-picking: Presenting only the data that supports your conclusions. Example: If a CEO believes their company should invest in retail real estate and finds evidence that suggests this is a poor investment, they might choose not to include this information in a presentation to the board of directors.
  • Confusing correlation with causation: Deciding that two consistent data patterns are evidence of causation when it might only be a correlation. Example: A supply chain manager that notices that the average shipping time decreases when customers order swimsuits might conclude that swimsuits ship faster than other articles of clothing. Upon further digging, they may discover that swimsuits are typically purchased in the summer when shipments aren’t subject to adverse weather delays.
  • Ignoring inconsistencies: Observing inconsistencies in data and disregarding them instead of mitigating them. Example: A sales director sees that quarterly sales numbers don’t match up between their report and the report from the CEO. Rather than addressing and discussing the issue, they choose to ignore it and go with the CEO’s results.

How to measure success

This article from MIT states that data literacy is about reading, working with, and analyzing data. Once employees have these competencies, many companies consider their work to be done. But MIT introduces a fourth skill that is crucial: arguing with your data. This means asking difficult questions about your data and how it was pulled to diagnose process errors or test assumptions and biases. The first conclusion you come to should never be the final conclusion — and if it is, it should only be deemed final after thorough vetting.

When your business units are proficient at not just analyzing data, but arguing with their own reports, you’ll know data literacy has truly taken form within your organization.


Final thoughts

Competition is a driving force for organizations in any industry. In the data decade, you either build data literacy and expertise into the fabric of your employees, systems, and processes, or you lag behind the companies that do. The only successful way forward is to embrace the value and complexity of data and build teams that master its management. Following the suggestions in this article will enable all of your employees to develop the data literacy skills they need to make better and smarter business decisions, ultimately giving your company a competitive advantage.

Learn how one company introduced data literacy into their organization by creating a data catalog. Read this article to learn how they did it.

Learn how one company recognized a need for source-of-truth documentation in a user-friendly format - in order to fully leverage data literacy in their existing culture. Read this article to learn how they did it.

Data Catalog Primer - Everything You Need to Know About Data Catalogs.

Adopting a data catalog is the first step towards data discovery. In this guide, we explore the evolution of the data management ecosystem, the challenges created by traditional data catalog solutions, and what an ideal, modern-day data catalog should look like. Download now!