How Can We Help Our Colleagues Become More Data Literate?
Data democratization — making data more accessible to all of an organization’s users — is predicted to be the top data-centric trend of 2023, according to research group TDWI. But is data democratization truly effective if users do not understand how to interpret the data they’re accessing?
Unfortunately, that is the case with a significant number of non-IT users. A recent study by Boston Consulting Group revealed that only 45% of respondents said that their company promotes data literacy among all employees, even though 73% expect the number of nontechnical data consumers to grow.
Being data literate is essential for everyone, however. By becoming data literate, application developers can build more intelligent applications, sales managers can better understand customer purchasing decisions and C-level executives can gauge how well their businesses are performing.
A data-literate organization also helps us, the data scientists. We don’t spend our days analyzing data and building models just for the sake of a paycheck; we want our work to be deployable, usable and beneficial to our colleagues and organizations.
So, how can we help others in our organizations become more data literate?
Double Down on Data Visualization
While we live in data, C-suite executives, sales managers, customer-service executives and others live in PowerPoint presentations. They understand visuals: pie and bar charts, infographics, demographic maps and the like.
To help our colleagues become more data literate, we must present data in a way that makes sense to them. Often, that means presenting findings in a highly visual manner that shows important information at a glance.
For example, at Red Hat we build models to help our customer-service representatives better understand the journeys our customers are taking with us. We have collected vast amounts of internal data culled from various sources throughout the organization, including sales, customer care, product management, product marketing and others.
Our data-science team analyzes the input and creates visualizations of the output in the form of user dashboards, reports and presentations — whatever visual format our colleagues are most comfortable using. They are not data scientists, nor do they want to be. Yet they have become data literate, not by being able to analyze data, but by being able to visualize and understand the intelligence behind the data.
Launch a Data-Literacy Education Program
Presenting data in a user-friendly way may not be enough. We need to ensure that users understand the importance of data to themselves and their organizations.
This can be done through data-literacy education programs. These programs can be modified based on the needs of different organizations, but education should be a consistent and foundational pillar.
Education should include regular and ongoing training on the value and importance of data literacy. Courses and modules can range from the basics (“What is data literacy and why is it important”), to the advanced (“What is data storytelling?”), to the specific (“The value of data visualization tools and how to use them effectively”).
The curriculum should be developed and taught by people who regularly work with data. For example, I helped develop a data-literacy training program at Red Hat. I wrote several courses and answered students’ questions. It’s been a great opportunity for me to share my expertise and help others learn how understanding data can boost their own careers.
Practice Proper Data Governance
International laws like the General Data Protection Regulation (GDPR) and the growing prevalence of state and local data privacy laws have made data protection and governance critical business initiatives. It’s important that anyone who handles data manages that data responsibly, especially if the data is personal and sensitive in nature.
Still, it’s not always easy to know what can, cannot or should be shared and with whom. Regulations and laws are continually changing, and data is often incomplete and inaccurate. And while many organizations, including Red Hat, have teams dedicated to data sovereignty and good data governance, it will sometimes be up to the individual to determine how to use data in a given instance. In these instances, users should take a moment and consider several factors, including:
- What does the data contain? Does the data contain personally identifiable or proprietary information?
- Who will see the data? Will the data be accessible to all or a wide group of users? Should it be accessible to such a large number of people?
- Is the data accurate? Is it good enough to share on GitHub or a similar repository?
Helping our colleagues understand how to ask and answer these questions and giving them the knowledge and tools they need to interpret data will help create a data-literate culture. That will benefit everyone, especially as data continues to be a driving force for better business outcomes.
