Data literacy 101: Building a public sector workforce for the future

When the Soviet Union launched the Sputnik satellite in 1957, it literally rocketed ahead of the U.S. in technological innovation. Fearful of Soviet dominance of space, the nation pivoted to an updated science curriculum and “new math,” an approach to mathematics that emphasized understanding the hows and whys of mathematical concepts, rather than rote memorization. 

Fast forward 65 years, and we’re seeing a similar pivot. The emergence of artificial intelligence has educators, businesses and governments alike pushing the accelerator on efforts to make the workforce  data literate. Today, everybody needs to be competent with data on some level, be it students, consumers, voters, employees. But the stakes are particularly high for the government. AI parses through vast amounts of data, and if that data is inaccurate, irrelevant or incomplete, it can lead to flawed  decisions. This, in turn, can have real-world consequences for those in the criminal justice system or  individuals dependent on social services for food or housing. Unfortunately, even unintentional missteps can lead to more mistrust in government.

Enter data literacy. State and local governments are scrambling to ramp up training for current employees and hiring a future tech-savvy workforce. The challenge is multifaceted: States and localities will have to overhaul decades-old policies and procedures, and they’ll have to work with the tools at their disposal, which in many cases means outdated software. And while all government employees and consumers should have at least a passing understanding of how data can be used—and manipulated—not everyone needs to develop the same level of expertise.

As governments look to build a data literate workforce, let’s pause to revisit the basics.

What It Is

So, what is data literacy? 

At its most basic, data literacy refers to the ability of staff to interpret, analyze and leverage data in their work as public servants. As data literacy moves up the organization and increases in complexity, the definition expands to cover data governance, data quality and data sharing, as well as data privacy, ethics and security. Data literacy is foundational to virtually all of government.

Why It’s Important

Government collects massive amounts of data that must be managed responsibly. Data literate staff are less likely to click on malicious email links that introduce malware into government networks. They can evaluate the validity of survey data by checking the sample size, the date of the research, the funding source and the logic of the baselines. They understand the difference between correlation and causation. They can read, analyze, and create charts, visualizations, and maps from government data and use the information in their work. Because they understand how data is being gathered and analyzed, can recognize the biases and have worked out what the data actually tells them, they can make data-informed recommendations.

As state and local governments increasingly move citizen services online, they’ll need to collect and analyze user experience data so they can measure the effectiveness of their online programs. Federal funding programs, like the Justice40 initiative, are requiring applications to submit more granular data in various categories to identify disadvantaged communities. Agencies with a good handle on their data and processes can streamline workflows and improve efficiency. And because data literacy transcends mastery of individual software packages, staff will be better able to adapt to new environmental, public health or social challenges.

Perhaps most important is that a government’s ability to make accurate and timely decisions that improve services and drive strategy depends on its ability to read, understand, analyze and communicate with data. 

Who’s Responsible

In data-forward jurisdictions, a city, county or state’s data policies and practices are often the responsibility of a chief data officer—someone who manages and advances the application of data to derive the maximum benefit from it.  CDOs handle data strategy, ensure data quality and security, provide access to data through analytics tools and platforms, and promote the ethical use of data for decision-making.

A relatively new position, CDOs have been installed in more than 30 states and in many of the larger counties and cities. They offer strategic advice on technology procurements, consult on grants and budgets, and, perhaps most important in this moment, are responsible for data literacy training of government employees.

How to Get There

Data literacy starts with reading comprehension, but it’s not enough for employees just to know how to read a financial report or filter data in a spreadsheet. Staff in a data-driven organization must be able to creatively use data to solve their problems and communicate their insights to stakeholders. Of course, not everyone will have all the requisite skills, but all need some. 

In its introductory course on data literacy, the Texas Department of Information Resources, or DIR,  identifies two types of data users: information producers and information consumers. Information producers include employees such as IT professionals and data analysts who collect raw data, clean and transform it, and then interpret the data through statistical analysis. Information consumers are business unit staff, program managers and leaders who take the data prepared by information producers and use it for business decisions. 

DIR outlines six core competencies of data literacy, running from the most basic skills for information consumers to more complex expertise information producers need:

1. Understanding data. All staff should know what is meant by data and how it impacts business decisions and outcomes. 
2. Finding and obtaining data. Employees should be aware of what data is available to them and which sources will best help them answer their questions. 
3. Reading data. Workers must be able to interpret data presented in multiple formats and know how to evaluate data and results critically. 
4. Managing data. Information producers understand data management principles such as data quality, master data and metadata management, records management, and privacy and security. 
5. Using data. IT professionals and data analysts should be able to prepare data for analysis, explore data with the appropriate tools, and understand and promote the ethical use of data. 
6. Communicating with data. Staff who use data to support a larger narrative should tailor their analysis to their particular audience.

Data literacy is essential to the future of government. While it will make it easier to adapt to a changing economy, data-literate workers will drive the accelerated pace of innovation. The change won’t be quick. As agencies build data literacy into the workforce, they should be prepared for a long, iterative process that involves continual learning and adaptation.