What Is Data Literacy?

Our Data Literacy Definition

Data literacy is the ability to understand, communicate with, interpret, ask questions about, and make inferences from data. Data literacy is a skillset that empowers all levels of workers to gain insights and make business decisions, even without programming experience or a specialized technical background.

 

Why Is Data Literacy Important?

At Correlation One we believe that data literacy is the most important skill for organizations and individuals to develop.  Across every industry, geography, and work function, data will drive decision-making, and every AI programs will augment or replace human tasks. 

To compete in the AI economy, organizations need to attract and recruit top data scientists. Companies that invest in the data literacy of their organizations  - either through hiring or upskilling - will create existing advantages for themselves and their customers.

Data can create value for organizations in numerous ways. Data intelligence enables business leaders to make smarter and more informed decisions. Automated decision-making via algorithm allows companies to spend more time on high-value services and less time on bureaucratic processes. Emphasizing data literacy skills gives businesses a leg up in a competitive and aggressive global economy; training a data literate workforce, equips each employee with data knowledge and skills they can use to help improve their company’s bottom line long-term.

Governments have a responsibility to prepare their workforces for the data economy, through education, training and upskilling.  This provides employers a talent pipeline of data literate professionals, and job opportunities for populations.

Digital literacy has become critical knowledge as the ubiquity of technology in the workplace continues to increase. Skilled data professionals are the backbone of any data-driven organization or project.

Data literacy is now becoming a foundational requirement for professionals seeking to excel in the jobs of tomorrow. What a basic excel toolkit was to the professional of the past, a data toolkit will be to the professional of the future. Every industry is becoming more data-driven, and as they do, professionals within them will need to become data literate .

A good foundation in data literacy should start early in order to keep up with digitization in our fast-paced, tech-dominated world. That said, foundational skills through prior education and work experience can bring invaluable insights into how data science can be applied in the real world.  Digitization and data are two sides of the same coin, so data literacy is an important knowledge base for students to have, enabling them to better shape and interpret their future.

 

How To Build Data Literacy in the Workforce?

  • Define Workflows

Workflows are the key repeated tasks executed by data professionals. These tasks include sourcing data, cleaning and preparing data, analyzing results, developing prediction models and automating the data pipeline, among others. 

The first step to achieve analytics and business goals is to define the specific workflows. Correlation One simplified all data-related work into 9 basic workflows to help enterprises develop analytics goals.

  • Map roles

Job titles alone do not describe job function. Depending on the organization, two data scientists could actually have very different jobs, which require very different skills. Thus mapping roles to technical data skills is invaluable. As an employer, that means precisely defining the technical knowledge required for a specific job.

As more candidates apply for roles, enterprises should rigorously assess them. For each role,  the Data Workflows Framework is used to determine the five highest priority technical and quantitative skills. Then, assess for those skills early in the process, so any candidates that move down the funnel are more precisely qualified for the specific responsibilities of that role. As a result, hiring managers save time evaluating technical skills and have more time determining a candidate’s cultural fit within organizations.

Correlation One’s Data Science Assessments have been administered to data scientists from over 120 countries providing a global benchmark of over 250,000 candidates.

C1 Connect helps companies differentiate their talent brand by intellectually engaging with top data science candidates around the world. Correlation One’s community connects companies with vetted, trained, job-ready, diverse talents.

  • Empowering the workforce with data training

Today it’s overwhelmingly recognized that data has tremendous value in the workplace, but where firms struggle is finding the right combination of talent, technology, and processes to cultivate a culture of data literacy organization-wide.

Correlation One training platform is designed to improve data literacy for industry professionals at every level of sophistication and help them stay ahead of fast-moving advances in data science.

  • Champion data communities

How data impacts employees’ day-to-day jobs should be an open conversation in any organization committed to data literacy. Out of discussions sparked in communities and forums developed around the topic, Data Champions can be identified, some of whom should have a seat at the executive table. With their superior knowledge and understanding, these champions are invaluable for turning skeptics into believers at all levels of the organization.

  • Data governance 

When opening up data to an organization, it is key to ensure proper data governance protocols are in place. As part of promoting company-wide availability of data and self-serve analytics, firm leadership must ensure the insights and answers contained in their data are vetted for accuracy.

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