How can you make your data analysis framework clear to non-technical stakeholders?

1 Tip 1: Define your problem and goal

Before you dive into the data, you need to have a clear and specific problem statement and a measurable goal. This will help you focus your analysis on the most relevant questions and metrics, and avoid getting lost in the details. It will also help you explain to your stakeholders what you are trying to achieve and why it matters. For example, instead of saying "We want to improve our website performance", you could say "We want to increase the conversion rate of our landing page by 10% in the next quarter".

2 Tip 2: Choose an appropriate framework

A framework is a structured approach to data analysis that helps you organize your thoughts, break down the problem, and identify the key steps and variables. There are many frameworks available, such as SWOT, PESTEL, SMART, or CRISP-DM, depending on the type and scope of your problem. You should choose a framework that suits your problem and goal, and that is easy to understand and follow for your stakeholders. For example, if you are working on a customer segmentation problem, you could use the RFM framework, which stands for Recency, Frequency, and Monetary value.

3 Tip 3: Visualize your data and results

One of the best ways to make your data analysis framework clear to non-technical stakeholders is to use visual aids, such as charts, graphs, tables, or diagrams. Visuals can help you show the trends, patterns, correlations, or outliers in your data, and highlight the main findings and recommendations. They can also make your data analysis more engaging and memorable for your audience. However, you should avoid using too many or too complex visuals, and make sure they are relevant, accurate, and easy to read. For example, you could use a pie chart to show the distribution of customer segments, or a bar chart to compare the conversion rates of different landing pages.

4 Tip 4: Explain your assumptions and limitations

No data analysis is perfect or complete. There are always some assumptions and limitations that affect your data quality, validity, and reliability. You should be transparent and honest about these factors, and explain how they impact your results and conclusions. This will help you build trust and credibility with your stakeholders, and avoid misleading or overconfident claims. For example, you could say "We assumed that the customer segments are stable over time, but this may not be true in reality", or "We only used data from the last six months, which may not capture seasonal variations".

5 Tip 5: Use simple and clear language

When you present or write your data analysis framework, you should use simple and clear language that your stakeholders can understand and relate to. You should avoid using jargon, acronyms, or technical terms that may confuse or alienate your audience. Instead, you should use plain English, concrete examples, and analogies to explain your data analysis concepts and methods. You should also use active voice, short sentences, and transitions to make your data analysis framework more coherent and logical. For example, instead of saying "A linear regression model was applied to the data to estimate the relationship between the independent and dependent variables", you could say "We used a simple formula to see how one factor affects another".

6 Tip 6: Provide actionable recommendations

The ultimate goal of your data analysis framework is to provide actionable recommendations that can help your stakeholders solve their problem or achieve their goal. You should make your recommendations clear, specific, and realistic, and link them to your data and results. You should also anticipate and address any potential objections, risks, or challenges that your stakeholders may have, and provide evidence or arguments to support your recommendations. For example, you could say "We recommend that you test two new versions of the landing page, one with a different headline and one with a different call to action, and measure their conversion rates against the current one. This will help you find out which one works best for your customers and increase your sales".

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?