Data Literacy for Public Service:Understanding and Using Data in the AI Age

Understanding and using data is an essential skill for every public professional, regardless of role or technical background. Whether designing policies, delivering services, managing programs, or communicating with the public, public professionals need to know how to ask good questions of data, interpret evidence, and recognize what the numbers can—and cannot—tell them.

AI tools are changing how people can work with data, making it easier for non-specialists to explore datasets, generate analyses, identify patterns, and create visualizations. But easier access to data analysis also introduces risks. Poor-quality, incomplete, biased, or sensitive data can lead to poor conclusions, and AI-generated analyses can introduce errors, overlook important context, or make findings appear more certain than the evidence supports.

This introductory, three-part series will build the foundational skills to work with data more confidently and responsibly. Every session will examine data and AI from two perspectives: how public professionals can use them as practical tools to support their work, and the risks and limitations they need to recognize and manage. Through hands-on exercises and public sector examples, participants will learn how to ask better questions, understand basic statistics and visualizations, assess data quality, use AI to support analysis, and communicate findings responsibly.

No prior experience with data analysis, statistics, coding, or AI is required.

By the end of the series, participants will be able to:

• Ask clear, answerable questions and identify the data needed to inform public sector decisions.

• Interpret common statistics, charts, comparisons, and patterns without specialized data expertise.

• Use AI tools to explore, analyze, visualize, and communicate data.

• Assess whether data is appropriate and reliable, including recognizing problems related to quality, completeness, bias, privacy, and context.

• Evaluate AI-assisted analyses for errors, misleading interpretations, and conclusions that are not supported by the underlying evidence.

• Communicate findings, uncertainty, and limitations clearly to support evidence-informed public decisions.