Essential Questions to Ask Before Starting a Data Science Project*_F9te100jSLdIUAL

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Before the start of every project, it is important to ask questions to help you understand what you will be working on for the next few weeks or even months. Questions like what are we trying to accomplish, why are we trying to accomplish, and how is it going to benefit the end-user are really important to ask at the start of the project as they are essential to driving successful outcomes and bringing clarity to the problem you are trying to solve.

Here’s a list of questions that you should ask before the start of your data science project:

  1. Who is the client, what business domain is the client in?

Understanding what business domain the customer is in, how they operate, what matters to them, which key variables are used to define success in that space will allow you to build a solution that directly impacts what’s important to the client.

2. What business problem are we trying to address?

The book Fundamentals of Machine Learning For Predictive Data Analytics describes this perfectly:

Organizations don’t exist to do predictive data analytics. Organizations exist to do things like make more money, gain new customers, sell more products, or reduce losses from fraud. Unfortunately, the predictive analytics models that we can build do not do any of these things. The models that analytics practitioners build simply make predictions based on patterns extracted from historical datasets. These predictions do not solve business problems; rather, they provide insights that help the organization make better decisions to solve their business problems.

A key step, then, in any data analytics project is to understand the business problem that the organization wants to solve and, based on this, to determine the kind of insight that a predictive analytics model can provide to help the organization address this problem. This defines the analytics solution that the analytics practitioner will set out to build using machine learning [1].

If your company’s goal is to reduce customer churn rate, one possible solution could be to build a prediction model that would identify which customers are most likely to churn in the near future.

3. How is it going to be consumed by the customer?

Understanding how your customer will use the output of your model will allow you to create your work targeted to them. For example, are you building models that serve internal users and influence company strategy, or are you building models that are customer-facing.

4. What is the economic impact of this project?

Putting a dollar amount to a project is one of the most hardest things to do. But knowing how your data product will drive revenue or reduce cost for the customer allows you to get leadership on-board and support you throughout the project.

5. What type of decisions will our data science feature drive?

What is the model going to empower them to do that they cannot do previously.

6. What metric will we use to call this project a success and how will we measure it?

Having a specific target in mind will make sure that your project has an end result, and you don’t work on it indefinitely. Quantify what improvement in the values of the metrics are useful for the customer scenario (e.g. reduce labor costs by 20%). The metric must be SMART (Specific, Measurable, Achievable, Relevant, and Time-bound). For example: achieve customer churn prediction accuracy of 20% by the end of this 3-month project so that we can offer promotions to reduce churn [2].


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