Making Data-Informed Decisions in the Public Sector

• General experience in public policy, program delivery, or service management
• Familiarity with government processes or decision-making workflows
• Comfort interpreting charts, reports, or key performance indicators

Audience

• Policy and program analysts
• Public administrators and managers
• Government decision-makers and strategic planners

Making Data-Informed Decisions is the practice of integrating relevant data, evidence, and analysis into the decision-making process to improve public sector outcomes and accountability.

This instructor-led, live training (online or onsite) is aimed at public sector professionals with limited or emerging experience in using data for decision-making who design, evaluate, or manage government programs and wish to enhance their ability to interpret, validate, and apply data to real-world decisions.

By the end of this training, participants will be able to:

• Understand what it means to be data-informed (versus data-driven or intuition-based).
• Identify and assess data relevant to a policy or service context.
• Ask better questions of data analysts and interpret dashboards and reports effectively.
• Apply data to justify, refine, or defend decisions in public service contexts.

Format of the Course

• Interactive lecture and discussion.
• Hands-on use of decision scenarios, data sets, and mock policy briefs.
• Guided exercises focused on practical decision-making use cases.

Course Customization Options

• To request a customized training for this course based on your department's workflows or internal tools, please contact us to arrange.

What is Data-Informed Decision-Making?

• Key definitions and distinctions: data-informed vs data-driven
• Examples of data-informed decisions in public administration
• Common sources of evidence in government contexts

Accessing and Assessing Data

• Identifying relevant metrics, indicators, and reports
• Evaluating the reliability and completeness of data
• Understanding data provenance and limitations

Asking the Right Questions

• Framing decisions and defining information needs
• What to ask of data teams, analysts, or dashboards
• Clarifying objectives before diving into data

Understanding What the Data Says

• Interpreting tables, visualizations, and dashboards
• Recognizing correlation vs causation
• Spotting gaps, anomalies, and biases

Applying Data to Real Decisions

• Using evidence to support or refine a policy recommendation
• Weighing tradeoffs and risks with partial data
• Documenting rationale in memos, briefs, or presentations

Communicating and Defending Decisions

• Storytelling with data for non-technical stakeholders
• Building transparency into your recommendations
• Responding to challenges or scrutiny with evidence

Summary and Next Steps