Why Data Literacy is Important
Data literacy programs provide your organization with a skill set delivering a competitive advantage for your company, in particular the potential for strong gains in innovation and efficiency. Here we dive into how a data literacy program works and the impact it can have on your growth.
What is Data Literacy in Business?
Being “data-driven” means making decisions — based on data and insights — to unlock new opportunities, accelerate growth, and optimize operations.
The technology to become data-driven is available to everyone. However, in our annual Data & AI survey, we observe a wide gap between successful companies and the rest.
It all comes down to skilled professionals, and that’s where data literacy comes in.
Organizations with professionals who can read, write and communicate data in context — who are data literate — prosper. This is because in data literate organizations teams are equipped to ask better questions and innovate and operate independently.
By contrast, those companies that do not invest in building a data literate culture run the risk of falling behind — in terms of both innovation and efficiency.
What Kinds of Organizations Should Think About Data Literacy?
A wide range of businesses and organizations should prioritize data literacy.
These include companies needing to:
- Unlock new opportunities, such as deciding where to open a new branch through data analysis.
- Accelerate growth, such as forecasting more accurately, minimizing out of stock and servicing customers better than the competition.
- Optimize operations, such as automating manual, time-consuming processes.
Let’s take a look at an example. IFF (International Flavors & Fragrances), is a global business that produces flavors, fragrances, and cosmetic solutions.
Since implementing a data literacy program, the staff submitted twice as many analytics project proposals and — according to global R&D lead, Dorthe Malmqvist — each proposal was of higher quality than before. Implementing a data literacy program is enabling IFF to do “better, faster, new R&D for people and the planet.”
Which Roles Should be Data Literate in my Organization?
One common misconception about data literacy is that it is a technical skill, whereas in reality it is a professional skill.
All roles — HR, marketers, sales, operations, product managers and more — can benefit from data literacy and strengthen the business through it. In fact, a data literate culture accelerates growth as it makes the company data-driven.
How Do I Build Data Literacy in my Company?
Many businesses today collect large amounts of data — from inventory to customer relations, from sensor data to legal documents. A data strategy is necessary to make it valuable.
The strategy defines the technology, processes, people, and rules to turn data into an asset and to make the business data-driven.
“People” is a key element because data literacy is a skill for everyone in the company. Companies with strong data-driven cultures have their leadership leading by example, taking decisions anchored in data.
For this reason, data literacy training programs should be deployed to ensure staff acquire this key skill. To become truly data literate, organizations need to deploy training programs that teach data literacy in the context of their business and its challenges — while being engaging for their employees.
Ensuring Data Democratization in Your Organization
Data democratization is the process to make data accessible to all stakeholders. Data Literacy is therefore a component of Data Democratization, as it ensures everyone can work with data fluently.
In our white paper, we look at how organizations can turn data into value through data democratization, from self-service analytics to data literacy.
Topics covered include:
- The process of Data Democratization.
- The benefits, risks, and main enablers of Data Democratization, such as the Modern Data Stack.
- How organizations increase data literacy amongst employees.
- The brand-new role amongst data professionals: the Analytics Engineer.
What are the Advantages of Partnering with Xebia to Design and Develop Data Literacy Programs?
If you want to bring data literacy into your organization, you have three choices:
- Working with online training platforms (such as Coursera).
- Developing your own internal training program.
- Partnering with a specialized data literacy training organization such as Xebia.
Learning is most effective when it is social (done with others), personalized (done with expert feedback), and contextual (connected directly to the business problems you are solving). For these reasons, working with online courses often falls short. And creating your own internal programs risks diverting precious internal resources.
Building a robust, lasting data-literate culture is best done with specialized training programs that have the above qualities and are done in collaboration with expert trainers such as those supplied by Xebia.
Example of Data Literacy in Business - Danone
An example of an organization that partnered with Xebia to improve internal data literacy is global food & beverage company Danone.
Heading into an increasingly data-enabled future, Danone sees data literacy no longer as just a useful skill but an essential one. Therefore, they wanted to improve the data literacy and skills of all employees. By partnering up with Xebia, EcolePolytechnique, and Quantmetry, Danone set up a Data Academy and raised awareness around Data & AI. Through different learning modalities, all employees have been enabled to use data to measure and improve their work, outcomes, and decisions. You can learn more about their journey in the video below, or read the full case here.
Courses in Data Literacy - the Xebia Data and AI Academy
Start Your Data Literacy Journey Now
If you are looking for a standardized course to help your data professionals upskill, a good place to start is the Analytics Translator training.
If you want a custom data literacy training program to train general professionals within your organization, contact us at the link below.