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How do you use data to make quality decisions?

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As a product engineer, you need to make quality decisions that align with your product vision, user needs, and business goals. But how do you know if your decisions are based on reliable data and not just assumptions or opinions? In this article, you will learn how to use data to make quality decisions in product engineering, from defining your problem and metrics, to collecting and analyzing data, to validating and communicating your results.

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Hadley Christoffels

I help organisations use data to drive better decisions

1 Define your problem and metrics

Before you dive into data collection, you need to have a clear understanding of the problem you are trying to solve and the metrics you will use to measure your progress and success. A well-defined problem statement should describe the current situation, the desired outcome, and the gap between them. A good metric should be specific, measurable, achievable, relevant, and time-bound (SMART). For example, if your problem is that your app has a high churn rate, your metric could be to increase the retention rate by 10% in the next quarter.

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Hadley Christoffels

I help organisations use data to drive better decisions
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I completely agree that a well-defined problem statement is worth its weight in gold. It will save you a ton of resources and pain down the line. Yet it's imperative that you align the initial problem statement(s) with strategic goals, and let the use cases follow on from that.

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2 Collect data from relevant sources

Once you have your problem and metrics defined, you need to collect data from relevant sources that can help you understand the root causes, user behaviors, and potential solutions. Depending on your problem and metrics, you may need to use different types of data, such as qualitative or quantitative, primary or secondary, internal or external. You should also consider the quality, reliability, and validity of your data sources, and use appropriate methods and tools to collect and store your data. For example, you could use surveys, interviews, or user testing to collect qualitative data, and analytics, logs, or experiments to collect quantitative data.

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Hadley Christoffels

I help organisations use data to drive better decisions
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I love that the selection of relevant data sources is called out explicitly, it very rarely gets a foot in. Even more so that the use of external data is referenced as this can be a source of significant competitive advantage if approached correctly. In short, go for relevance over volume and use external data with intent.

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3 Analyze data with appropriate methods and tools

After you have collected enough data, you need to analyze it with appropriate methods and tools to find patterns, insights, and answers. Depending on your data type and goal, you may need to use different techniques, such as descriptive, inferential, or predictive analytics, or machine learning. You should also use visualizations, such as charts, graphs, or dashboards, to make your data easier to understand and communicate. For example, you could use descriptive analytics to summarize your data, inferential analytics to test your hypotheses, or predictive analytics to forecast your outcomes.

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4 Validate your data and results

Before you make your final decision based on your data and results, you need to validate them to ensure that they are accurate, reliable, and relevant. You should also check for any biases, errors, or limitations that may affect your data and results, and address them accordingly. You can use various methods and tools to validate your data and results, such as cross-validation, sensitivity analysis, or peer review. For example, you could use cross-validation to check the consistency of your data, sensitivity analysis to check the robustness of your results, or peer review to get feedback from other experts.

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Hadley Christoffels

I help organisations use data to drive better decisions
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This can be fairly tricky to get right, but the value of validated data cannot be underestimated. Validated, reliable data fosters trust. And trust is essential when using that data to drive decisions.

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5 Communicate your data and results

Finally, you need to communicate your data and results to your stakeholders, such as your product team, users, or clients, to inform your decision and get their buy-in. You should use clear, concise, and compelling language and visuals to present your data and results, and explain how they support your decision and align with your problem and metrics. You should also anticipate and address any questions, objections, or concerns that your stakeholders may have, and provide evidence and arguments to back up your decision. For example, you could use a presentation, a report, or a prototype to communicate your data and results, and highlight the benefits, risks, and trade-offs of your decision.

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6 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?

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How do you use data to make quality decisions?

1

2

3

4

5

6

1 Define your problem and metrics

2 Collect data from relevant sources

3 Analyze data with appropriate methods and tools

4 Validate your data and results

5 Communicate your data and results

6 Here’s what else to consider

Product Engineering

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How do you use data to make quality decisions?

1

2

3

4

5

6

1 Define your problem and metrics

2 Collect data from relevant sources

3 Analyze data with appropriate methods and tools

4 Validate your data and results

5 Communicate your data and results

6 Here’s what else to consider

Product Engineering

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