How do you determine the most relevant predictive insights for stakeholders?

1 Know your stakeholders

Before you present any predictive insights, you need to understand who your stakeholders are, what their goals and challenges are, and how they use data to make decisions. Different stakeholders may have different levels of interest, expertise, and expectations for predictive insights. For example, a senior executive may want a high-level overview of the main trends and implications, while a technical manager may want more details on the data sources, methods, and assumptions. You need to tailor your predictive insights to match your stakeholders' needs and preferences.

2 Define your objectives

The next step is to define the objectives of your predictive analysis. What are the specific questions or problems that you want to address? What are the desired outcomes or actions that you want to influence? How will you measure the success or impact of your predictive insights? By clarifying your objectives, you can narrow down the scope and focus of your predictive analysis, and avoid getting distracted by irrelevant or trivial insights.

3 Choose your methods

Depending on your objectives, you may need to choose different methods or techniques to generate predictive insights. For example, you may use regression, classification, clustering, or time series analysis to model different types of data and relationships. You may also use different tools or platforms to perform your predictive analysis, such as Excel, Python, R, or Power BI. You need to choose the methods that best suit your data, objectives, and stakeholders, and explain the rationale and limitations of your choices.

4 Validate your results

Before you share your predictive insights, you need to validate your results and ensure that they are reliable, accurate, and robust. You can use various methods to validate your results, such as cross-validation, testing, sensitivity analysis, or comparing with other sources. You also need to check for any errors, biases, outliers, or anomalies in your data or models that may affect your results. You need to report any issues or uncertainties that may affect the validity or confidence of your predictive insights.

5 Visualize your insights

One of the most effective ways to communicate your predictive insights is to use visualizations, such as charts, graphs, tables, or dashboards. Visualizations can help you convey complex or abstract insights in a simple and intuitive way, and highlight the key patterns, trends, or relationships that you want to emphasize. You need to choose the appropriate type and format of visualization for your data and insights, and use clear labels, titles, legends, and colors to make them easy to understand.

6 Tell a story

Finally, you need to tell a story with your predictive insights. A story can help you connect your insights with your objectives and stakeholders, and explain the meaning, relevance, and value of your insights. A story can also help you engage and persuade your stakeholders, and inspire them to take action or make changes based on your insights. You need to structure your story with a clear beginning, middle, and end, and use a logical, coherent, and compelling narrative.

7 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?