How can you communicate machine learning model results to non-technical stakeholders in BI?

1 Explain the problem and the goal

Before you dive into the details of your model, you need to provide some context and motivation for your audience. What is the business problem or opportunity that you are trying to address with ML? What is the goal or objective of your model? How does it align with the strategic priorities and key performance indicators (KPIs) of your organization? By framing your model results in terms of the problem and the goal, you can help your stakeholders understand why they should care and how they can benefit from your analysis.

2 Simplify the model and the metrics

One of the biggest challenges of communicating ML results is to avoid overwhelming your audience with technical jargon, complex formulas, and abstract concepts. Instead, you should try to simplify your model and the metrics you use to evaluate it. For example, you can use analogies, metaphors, or examples to explain how your model works and what it does. You can also use visual aids, such as charts, graphs, or diagrams, to illustrate your model structure, inputs, outputs, and performance. Moreover, you should choose metrics that are relevant, intuitive, and easy to interpret for your audience. For instance, you can use accuracy, precision, recall, or F1-score to measure how well your model classifies data, or mean absolute error (MAE), root mean square error (RMSE), or R-squared to measure how well your model fits or predicts data.

3 Highlight the insights and the actions

Another important aspect of communicating ML results is to focus on the insights and the actions that your model generates, rather than the technical details. What are the main findings or patterns that your model reveals? What are the implications or recommendations that your model suggests? How can your stakeholders use your model results to make better decisions, improve processes, or create value? By highlighting the insights and the actions, you can show your audience how your model can help them solve problems, seize opportunities, or achieve goals.

5 Invite feedback and collaboration

Finally, you should not treat your communication as a one-way presentation, but rather as a two-way conversation. You should invite feedback and collaboration from your stakeholders, and listen to their questions, concerns, or suggestions. How do they perceive, interpret, or use your model results? What are their needs, expectations, or preferences? How can you incorporate their feedback or input into your model development or refinement? By inviting feedback and collaboration, you can foster a culture of learning and innovation, as well as create value for both your data science and your business teams.

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?