Importance of data literacy in the modern enterprise

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What are data?

Data are measurable values that, when contextualized, can be considered information. As knowledge is developed through the interpretation of information, then data can be considered the foundation of knowledge.

Fun Fact! Though it is often used colloquially to refer to both singular and plural verbs, the word ‘data’ is a plural noun (think ‘geese’) and the singular form is datum (think ‘goose’).

What is data literacy?

There are many definitions of data literacy, but one that we found comprehensive and relevant is from Gartner, which 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” [1]

Why is it important?

A focus on data literacy, coupled with data-democratisation (access to data) and data-governance (managing the use, security, and integrity of data), enables people to utilise reliable and relevant data to gain knowledge and inform decisions in both their work and daily lives, instead of simply relying on prior experience and ‘gut-feeling’.

A data-literate workforce enables agility and accuracy in decision-making, through the ability to capture, critique, contextualise, interpret, and communicate information at all levels.

Data literacy enhances employee confidence, their ability to increase productivity through rapid decision making, and their ability to define and evaluate strategies that drive successful business outcomes. This means that data literacy can be considered as important to the success of a modern enterprise as literacy itself.

What is the value of data literacy?

Data Literacy is valuable because it allows businesses to implement insight-based decisions that optimise internal processes and customer-facing activities, as well as to develop new business models that expand their portfolio of products by leveraging data.

Examples of insight-driven use cases that depend on data literacy may include churn prevention, pricing strategy, promotion optimisation, demand planning and supply chain optimisation.

The value of data literacy could also be considered proportional to the costs of data illiteracy. For instance:

  • The costs imposed by employees not being able to locate/access/utilize data required for their role.
  • The cost of employees not understanding the value of data accuracy, security, storage, or structure.
  • The costs of clients not trusting enterprise data or being unwilling to provide correct data.

We should also consider the monetary value of data literacy skills in relation to professional development and employee retention. The Work Institute’s 2022 Retention Report[2] found that a lack of career development (opportunities for growth, promotion, achievement, and security) was the number one reason people left their roles.

As more workplaces shift towards models that are underpinned by effective data capture, analysis, and automation, the higher the demand for demonstrable data-literacy skills will be. When employees feel that their current workplace can develop, value and reward data literacy skills, they will be less likely to leave, avoiding the costs incurred through the loss of operational capacity and institutional knowledge, as well as hiring and training fresh staff.

Having a data-literate workforce is also beneficial as it enables enterprises to effectively leverage the value of internal technology platforms that are already available for analytics and to accurately assess analytical requirements across different business units before introducing new tools.

How can we assess data literacy?

The optimal level of data literacy skills required across roles and departments will vary, depending on whether people need to simply understand and value data or conduct analyses and utilize it in decision-making. A good Data Strategy will define and measure this and direct resources toward appropriate learning opportunities. To be successful, these opportunities should enhance both data literacy and critical thinking skills in the context of a person’s role in the business and career goals.

Creating a data literacy program

  1. Outline: The goals of a data literacy program and its corresponding value in the context of the data strategy and business outcomes.
  2. Communicate: The importance of the data literacy project in enhancing personal growth and strategic success to drive internal buy-in and unity across the enterprise. Focus on the value of learning and the importance of curiosity, creativity, and critical thinking in daily tasks. Encourage questions, feedback, and ideas about the project.
  3. Identify: Project leaders, subject matter experts, data champions and key stakeholder groups. Create surveys to outline the current level of workforce data literacy and willingness to learn.
  4. Define and Assess: Define data-personas, required skills and associated learning investment. Recruit willing individuals across different data-personae to partake in the data-literacy enhancement pilot program and assess their corresponding data literacy skills.
  5. Provide: the participants with access to the relevant data and tools to ensure that enterprise data can be incorporated into their learning in real-time. This way, new skills can be utilised and highlighted immediately, allowing the business value of the pilot to be measured.
  6. Develop: structured learning roadmaps, targeted learning plans and timeframes, with resources to suit both the data-persona and the individual’s preferred learning style. Ensure that learning can be embedded into individuals’ routines and new skills utilised in their daily tasks, with opportunities and rewards clearly highlighted.
  7. Engage: Implement the learning plans across the different stakeholder groups with active management to support the learning process, measure progress, reduce drop-out and reward success.
  8. Evaluate: the success of the pilot program based on both quantitative and qualitative assessment. Identify successful learning resources and those required to fill gaps identified in the learning paths/tools. Communicate the program’s value by highlighting successful business outcomes and the personal leadership opportunities it has provided to participants.
  9. Iterate and Scale: based on the insights gained from the pilot program, focusing on stakeholder groups where improved data-literacy will derive the most value. Use the ongoing success and opportunities provided by the program to embed a positive data-culture in the business. Program participants should be rewarded for becoming data-champions and mentoring others in their team, with platforms provided to share resources, methods, and insights.

At Exco partners, we believe that data is the foundation of information and that a data-literate workforce is one empowered to be engaged, accurate and agile in their decision-making. This allows decisions to be distributed across the business, as workers with localised knowledge can confidently make frequent and time-critical choices based on data-driven insights. De-centralised decision-making increases productivity and allows rapid responses to change, creating a culture of innovation and creativity. It also ensures that the leadership team has more time to focus on long-term, strategic planning based on accurate information provided by data-literate and data-driven teams across the business.

Exco Partners leverages our deep experience and expertise in analytics, agile thinking, and transformational change to help our clients in their data literacy journey, all the way from A (Algorithms) to Z (Zettabytes). Get in touch to find out how you can take the first step.

Some questions to aid in data literacy discussions


  • Who uses the data?
  • What do they do with the data?
  • What technology is used to interact with the data, from collection and storage to analysis and reporting?
  • What sort of decisions rely on the data? I.e., operational, strategic, low-level, critical.


  • What sort of data types are included? I.e., categorical, numeric, structured, unstructured.
  • Have schemas been defined?


  • Where did the data come from?
  • How frequently is the data collected/updated/deleted?


  • How accurate and complete is the data?
  • Have collection methods been analysed for bias?
  • ls the data in a raw state or has it been processed and ready to be used for analysis? I.e., is the structure correct, without nulls, duplicates, or outliers?


  • Who is responsible for the data?
  • Who can create, edit, and share the data and how is this tracked?
  • Where is data stored and backed up, i.e., cloud or physical servers?
  • Does the data include sensitive information and how is this treated?

[1] Gartner: A data and analytics leaders guide to data

[2] Work Institute: 2022 Retention report