How do you measure and improve BI hypotheses?

1 What is a good BI hypothesis?

A good BI hypothesis should be specific, measurable, actionable, realistic, and time-bound. These criteria are also known as SMART goals, and they can help you define the scope and purpose of your BI project. For example, a bad BI hypothesis would be: "Improving customer satisfaction will increase sales." This hypothesis is vague, hard to measure, and does not specify how or when the improvement will happen. A better BI hypothesis would be: "Increasing the response rate of customer feedback surveys by 10% in the next quarter will lead to a 5% increase in sales revenue."

2 How to measure BI hypotheses?

To measure BI hypotheses, you need to identify the key performance indicators (KPIs) that will track the progress and impact of your BI project. KPIs are quantifiable metrics that reflect the goals and objectives of your hypothesis. For example, if your hypothesis is about increasing customer feedback response rate, your KPIs could be the number of surveys sent, the number of surveys completed, and the average satisfaction score. You also need to establish a baseline and a target for each KPI, and compare them over time to evaluate the results of your BI project.

3 How to improve BI hypotheses?

To improve BI hypotheses, you need to test them against data and feedback, and refine them based on the findings. Testing BI hypotheses involves collecting and analyzing data that can support or reject your hypothesis, and using various methods such as data visualization, statistical analysis, and machine learning to uncover patterns and insights. Feedback is also important, as it can help you validate your hypothesis with stakeholders, customers, and users, and get their opinions and suggestions. Based on the data and feedback, you can modify your hypothesis to make it more accurate, relevant, and feasible.

4 How to communicate BI hypotheses?

When communicating BI hypotheses, you should present them in a clear and compelling way that can convince your audience of their value and potential. You can use different formats and tools such as dashboards, reports, slides, or stories; however, regardless of the format, you should always start with a summary that highlights the main points and outcomes of your BI project. Additionally, use simple and concise language that avoids jargon and technical terms. Visuals and charts can also illustrate your data and insights. Be sure to include evidence and examples that back up your hypothesis and KPIs, as well as storytelling techniques that engage your audience and show them the benefits and implications of your BI project.

5 How to learn from BI hypotheses?

To learn from BI hypotheses, you need to review and reflect on the process and outcomes of your BI project, and identify the lessons and opportunities for improvement. Various methods and frameworks can be used for this purpose, such as the PDCA cycle, the 5 Whys, or the SWOT analysis. When reflecting on your BI project, ask yourself what worked well and what did not; what were the main challenges and obstacles; what assumptions and risks were made; what insights and discoveries were made; and what are the next steps to improve your BI project?

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?