How do you validate BI hypotheses?

1 What are BI hypotheses?

BI hypotheses are statements that express a causal relationship between variables or factors that affect a business problem or opportunity. For example, a BI hypothesis could be: "Increasing customer satisfaction leads to higher retention and loyalty". BI hypotheses are based on domain knowledge, previous research, or data exploration, and they provide a direction and focus for the BI project. They also help to define the scope, objectives, and metrics of the BI project.

2 Why validate BI hypotheses?

Validating BI hypotheses is the process of testing and verifying whether they are true or false, using data and analytical methods. Validating BI hypotheses is important for several reasons. First, it helps to avoid confirmation bias, which is the tendency to look for or interpret data in a way that confirms one's preconceptions or expectations. Second, it helps to avoid wasting time and resources on pursuing false or irrelevant hypotheses that do not address the real business problem or opportunity. Third, it helps to generate reliable and actionable insights that can support evidence-based decision making and improve business outcomes.

3 How to validate BI hypotheses?

Validation of BI hypotheses is not a one-size-fits-all process, as different hypotheses may require different data sources, analytical approaches, and criteria for validation. However, there are some general steps that can guide the validation process. Firstly, define your BI hypothesis clearly and explicitly. Secondly, identify the relevant and available data sources and variables for testing the hypothesis. Thirdly, select an appropriate analytical method to test the hypothesis, such as descriptive statistics or hypothesis testing. Fourthly, interpret the results of your analysis in comparison to your hypothesis. Fifthly, evaluate the validity of the hypothesis based on the results of your analysis, considering accuracy, consistency, relevance or usefulness. Finally, revise, refine or reject the hypothesis based on the evidence.

4 How to improve your BI hypotheses?

Validating BI hypotheses is not a one-time task, but rather an ongoing and iterative process that requires learning and feedback. As you validate your hypotheses, you may find new insights, questions, or issues that require you to update or modify your hypotheses. You may also have to generate new hypotheses to explore different aspects or scenarios of your business problem or opportunity. To improve your BI hypotheses, it is important to seek feedback from stakeholders, experts, or peers on your hypotheses, data, methods, and results. This will help ensure the validity, relevance, and clarity of your hypotheses. Additionally, you should compare and contrast your hypotheses and results with existing knowledge, research, or best practices. Reviewing the literature, benchmarks, or case studies can help validate, challenge, or complement your hypotheses. Lastly, experiment and iterate with your hypotheses, data, and methods to test them and optimize the results.

5 How to use your BI hypotheses?

Validating and improving your BI hypotheses are not the end goals of your BI project, but rather the means to achieve them. The ultimate goal of your BI project is to use your hypotheses and insights to inform decision making and improve performance. To ensure that these hypotheses are used effectively, you should communicate them to the target audience through storytelling, visualization, or reporting tools. Additionally, you should link your hypotheses and insights to the business objectives and metrics with frameworks, models, or dashboards. Lastly, you should monitor and measure the impact of your hypotheses and insights on business outcomes with feedback loops, KPIs, or ROI calculations.

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?