Data-informed decision making

Data is information. It's the numbers and feedback available to you about different aspects of your project. Metrics are how you measure your data.

Productivity metrics

Productivity metrics typically measure progress and output over time. They allow you to track—or predict—the effectiveness and efficiency of your project team.

  • Milstones
  • Tasks
  • Projections - helps to predict an outcome based on the information the project team has now
  • Duration

Quality metrics

  • Changes
  • Issues
  • Cost
  • Happiness and satisfaction
  • Customer satisfaction scores

Adoption metrics

For a product or service release, like an app, software program, delivery service, or gym membership, would be similar to the party example. However, they can be a bit more complex if you need to track metrics for more than one thing, like whether users make additional purchases or sign up for premium features.


  • Conversion rates
  • Time to value (TTV)
  • Onboarding completion rates
  • Frequency of purchases
  • Providing feedback (rating the product or service)
  • Completing a profile


Data bias

A type of error that tends to skew results in a certain direction.


  • Sampling bias is when a sample is not representative of the population as a whole. For example, maybe your sample did not include people above the age of 65. Or maybe you excluded people from certain socioeconomic groups.
  • Observer bias is the tendency for different people to observe things differently. For example, stakeholders from different parts of the world might view the same data differently and draw different conclusions from it.
  • Interpretation bias is the tendency to always interpret situations that don't have obvious answers in a strictly positive or negative way, when, in fact there is more than one way to understand the data. Data that does not provide an obvious set of conclusions makes some people feel anxious, which can lead to interpretation bias. For example, a team member might interpret inconclusive survey results negatively, while other team members might be able to think more carefully and assess the data from different angles.
  • Confirmation bias is the tendency to search for or interpret information in a way that confirms pre-existing beliefs. For example, you might ask only specific stakeholders for feedback on parts of your project because you know they are the most likely to have the same perspective as you.

The six steps of data analysis

  • Ask What is the problem? When defining the problem, look at the current state of the business and identify how it is different from the ideal state. Identify stakeholders and understanding their expectations.

  • Prepare Collect and store the data you will use for the upcoming analysis process.

  • Prosess Clean data.

  • Analyse Draw conclusions.

  • Share Use data visualization.

  • Act Solve the original business problem