Business Understanding: Verify a Hypothesis
Having a hypothesis before starting an analytics project is critical
Before we plunge into an analytics project, it's important to ask questions and discuss the 'business understanding' with all the major stakeholders.
This is essentially an exercise to define the question: What is the problem we are trying to solve, or what is the opportunity we are trying to uncover? Hearing the answer to that from the Product owner or other stakeholder clarifies the goal of the projects and is an anchor for any work.
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A few things to keep in mind at this stage:
- Avoid the ‘solution space’. We don't want to solve the problem, we want to fully understand it.
- Stay focused on what the value driver is and how it can be influenced. This is the main criteria that we are looking to change. In a processing plant this might be an increase in recovery, or proxy for recovery. In an improvement project, this might be the dollar amount saved.
- The goal of this stage is to end up with a clear, concise scope or 'problem statement' that can be referred to throughout your analytics tasks, particularly as more is learned. Do not proceed without a clear scope.
It is easy to lose track of the aim/purpose of an analytics project when there is a lot of data available. Returning to the hypothesis and the value driver regularly will help to prevent scope creep.
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In this section we will take you through a typical data science workflow. We will dive a bit deeper into each step and will provide you with hints about what to look for.
Next article - Connect your data: Upload a dataset
After creating your hypothesis, it's time to think about connecting your data. Let's get into into a typical Data Science Workflow.