In a recent survey conducted by Forrester Consulting and Tableau, 60% of respondents said they did not have the data skills they were expected to have for their jobs.
The problem is not that organizations are looking for employees to be data scientists. Instead, they need employees to be data literate, that is, to have the ability to explore, understand and communicate with data.
In recent years, most agencies have focused on the domain of data scientists, addressing issues around data governance, data quality, data management and so on, said Srinivas Kosaraju, Senior Director of Public Sector Solution Engineering at Tableau, a Salesforce company.
“Now it’s about unlocking value for end users,” Kosaraju said. “It’s the last mile solution, using analytics and visualization to understand the ever-growing volume of data.”
The Starting Point: Visualization
In part, literacy is a challenge because data is complex. People who lack formal training often struggle sifting through and making sense of the data they have.
It’s like watching a financial expert work with a spreadsheet. However complex that spreadsheet might be, the expert will quickly figure out what data is important and what it means, while everyone gets a glazed look in their eyes.
But turn that spreadsheet into a dashboard, with charts and graphs, and now everyone can talk the same language. That’s why visualization is so important. “Not all people are going to understand R and Python, which is data science coding language,” Kosaraju said. “But do you know what all our people do understand? Charts and graphs.”
Putting Data to Work
Another key to data literacy is teaching people how to put that data to work. For example, a dashboard will show that constituents are experiencing long wait times at an agency’s field office. Now, how do you use data to streamline that process and reduce wait times?
Technology can go a long way toward helping people answer such questions, but you must understand how to use that technology.
Think about artificial intelligence and machine learning. If they don’t know better, employees might worry that AI and ML could take their jobs. In fact, the goal is to have employees use their experience and domain knowledge to figure out how the system can help them do their jobs more effectively. “It’s a collaboration between skills and systems,” said Kosaraju.
Not Just Tools but Partnerships
Kosaraju suggested that agencies look to their technology providers for ideas in improving data literacy. Indeed, they should look for vendors who are ready to serve as advisers and partners.
Tableau, which is now part of Salesforce, has a long history of helping agencies across the public sector build data literacy through culture, community and technology. In its early years, the company
received funding from the Defense Advanced Research Projects Agency to help advance the use of visualization, and today it continues to support agencies across the national security community through cooperative research agreements.
“Don’t just buy a tool, but invest in the relationship with that technology provider,” Kosaraju said.
This article is an excerpt from GovLoop’s guide “Your Field Notes for Data-Driven Decision-Making in Government: Case Studies on Work Culture, Equity and More.”
