What is Data Literacy? Tips on becoming data literate.

October 28, 2020
October 28, 2020
28 October 2020

Data literacy has been a trending topic for a few years, and it is known that it is a vital skill for enterprises seeking to fully transform their organizations and become data-driven. 

As technology can be a point of failure if not handled properly, it is often not the most important roadblock to progress. In fact, in Gartner’s annual Chief Data Officer survey, the top roadblocks for success were cultural factors – human, skills, and data literacy. 

However many of these enterprises still struggle to understand what data literacy truly is, or know how to reshape their cultural organization into a data literate one. 

In its 2020 survey, New Vantage Partners observed that:

“Companies continue to focus on the supply side for data and technology, instead of increasing demand for them by business executives and employees. It’s a technology push rather than a pull from humans who want to make more data-based decisions, develop more intelligent business processes, or embed data and analytics into more products and services.”

In this article, we’d like to shed light on what data literacy is, why it is important for your enterprise, and tips on how to become a data literate organization.  

The definition of data literacy

Just as literacy means to have “the ability to read for knowledge, write coherently and think critically about printed material” data literacy is the ability to consume for knowledge, produce coherently and think critically about data.

In 2019, Gartner defined 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.”

So, based on these definitions, we can conclude that data literate people can, among other things:

  • make analyses using data, 
  • use data to communicate ideas for new services, products, workflows or even strategies,
  • understand dashboards (visualizations for example),
  • make data-based decisions rather than based on intuition

In summary, being data literate signifies having the set of skills to be able to effectively use data individually and collaboratively. 

Why is data literacy important?

Gartner expects that, by 2020, 80% of organizations will initiate deliberate competency development in the field of data literacy to overcome extreme deficiencies. By 2020, 50% of organizations will lack sufficient AI and data literacy skills to achieve business value.

The increasing volume and variety of data that businesses are flooded with on a daily basis require employees to employ higher order skills such as critical thinking, problem-solving, computational, and analytical thinking using data. And as organizations become more data-driven, poor data literacy will become an inhibitor to growth. In fact, in their survey “The Human Impact of Data Literacy”, Accenture found that: 

  • 75% of employees are uncomfortable when working with data.
  • 1/3 of employees have taken a sick day from work due to headaches working with data.
  • A lack of data literacy costs employers 5 days of productivity translating to billions of dollars in lost productivity per employee each year.

 Furthermore, a Deloitte survey conducted in 2019 found that 67% of executives are not comfortable accessing or using data resources.

Data uplifts the success of organizations in creating both physical and digital business opportunities—improving accuracy, increasing efficiency and augmenting the ability of the workforce to deliver greater value. It is therefore important and essential to be able to interpret, analyze and communicate findings on data to be able to uncover the secrets to successful business and competitive advantage. 

Tips on how to become data literate

In order to build a successful data literacy program, here are some tips to help your organization on your data fluency journey:

Tip #1 – Develop a data literacy vision and associated goals

Any organization investing in data and AI capabilities should have already undertaken the creation of a data vision and  roadmap. In the process of doing so, data and IT leaders will have identified and prioritized the areas of business where data can produce value.

These steps are critical to creating a data-literate organization and reducing the friction around understanding and using data.

Management and HR need to communicate across the entire enterprise that data is a strategic asset that creates value. Using the data vision and roadmap as context, they should be able to explain to all employees why data matters, how it creates value, and how it impacts the business.

The absence of a clear vision for data and a plan to create value out of it, will create frustration and, as a consequence, employees will lack understanding of why they are being asked to make efforts and therefore, not have the motivation to do so.

In addition, a data literacy vision should detail desirable skills, abilities, and the level of literacy required for different business units and roles.

Business, IT, and HR leaders need to create a framework to achieve literacy goals, measure progress, and create a way to maintain data literacy.  This includes deciding what skills are required, how to measure & track skills development, and to what degree different parts of the organization should use data in achieving their strategic objectives.

Tip #2 – Asses workforce skills

Data literacy skills should ideally be assessed during the recruitment process for new hires.  In this way, HR will already know what kind of data literacy learning should be offered to the new hire over time.

However, for already existing employees, HR can map current employee data skills based on the roles and responsibilities provided in the above steps, and determine where there are gaps. 

Tip #3 – Create data literacy modules

According to Qlik, only 34% of firms provide data literacy training.

In most cases, the HR department is responsible for helping business managers identify and track areas of improvement and development opportunities for employees. They are also in charge of organizing the procedures for learning specific organizational skills as well as the time it takes. It’s no different when it comes to becoming data literate.

Once HR and managers have a general idea of an employee’s or a business unit’s strengths and weaknesses in data skills, HR can begin to construct personalized and efficient learning programs that allow employees to upskill in data and analytics responsibilities.


Tips #4 – Track, measure, and repeat

A successful data literacy program takes time to put in place. Business leaders must allow their employees to invest the time required to become data literate and improve their skills.  Over time, data thinking will become part of the corporate culture.

Finally, it’s important to communicate data literacy progress across the enterprise and on an individual basis. Tracking and communicating on the progress is key to continuing the evaluation of your organization’s data roadmap, vision and literacy.

This type of long-term planning and investment in educating the entire organization about how to access, understand and analyze data on the job will accelerate the efforts and investment that data science, machine learning and AI teams are making.

The results of data literacy efforts will allow organizations to finally be able to embrace and leverage data across the enterprise and for maximum value! 

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At Zeenea, we work hard to create a data fluent world by providing our customers with the tools and services that allow enterprises to be data driven.

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Chez Zeenea, notre objectif est de créer un monde “data fluent” en proposant à nos clients une plateforme et des services permettant aux entreprises de devenir data-driven.

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Das Ziel von Zeenea ist es, unsere Kunden "data-fluent" zu machen, indem wir ihnen eine Plattform und Dienstleistungen bieten, die ihnen datengetriebenes Arbeiten ermöglichen.

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