Data Fluency & Insight
Data-Driven Decision Making
Test whether your data can answer the question, catch bias, and recommend options others can act on.
Standard version
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Having good data is not the same as making a good decision, especially when the data is incomplete, people disagree, or the pressure is to move fast. This course helps participants test whether their data can actually answer the question, recognize bias in how data gets used, compare real alternatives, and communicate a recommendation others can act on. Participants apply a complete decision process from start to finish using realistic data. The course pairs with Turning Data into Insights for a two-day program.
What you'll learn
- Distinguish data-driven decisions from data-adjacent ones and name the assumptions behind them.
- Evaluate whether available data can answer the question being asked.
- Recognize personal and organizational biases that distort how data is used.
- Define decision criteria and compare alternatives using a weighted decision matrix.
- Match the decision approach to the situation's complexity, stakes, and time horizon.
What you'll be able to do
- Spot when data cannot support the conclusion being drawn.
- Catch bias in their own thinking and in group decisions.
- Compare options using clear, weighted criteria.
- Communicate decisions that hold up in front of a skeptical audience.
- Apply a repeatable, monitored decision process instead of relying on one good call.
Having good data is not the same as making a good decision, especially when the data is incomplete, people disagree, or the pressure is to move fast. This course helps federal employees and leaders test whether their data can actually answer the question, recognize bias in how data gets used, compare real alternatives, and communicate a recommendation others can act on. Participants work through realistic workforce data and apply a complete decision process from start to finish. It pairs with Turning Data into Insights for a two-day program.
What you'll learn
- Distinguish data-driven decisions from data-adjacent ones and name the assumptions behind them.
- Evaluate whether available data can answer the question being asked.
- Recognize personal and organizational biases that distort how data is used.
- Define decision criteria and compare alternatives using a weighted decision matrix.
- Match the decision approach to the situation's complexity, stakes, and time horizon.
What you'll be able to do
- Spot when data cannot support the conclusion being drawn.
- Catch bias in their own thinking and in group decisions.
- Compare options using clear, weighted criteria.
- Communicate decisions that hold up in front of a skeptical audience.
- Apply a repeatable, monitored decision process instead of relying on one good call.