Are You Doing Data Science Correctly? Three Different Use Cases
By: Opex Analytics
Tags: Artificial Intelligence, Data Science, Analytics

Are You Doing Data Science Correctly? Three Different Use Cases

by Michael Watson

As more companies are creating data science teams, it’s important that managers know what kinds of use cases this group should work on.

In our opinion, there are three basic types of data science projects or use cases. If your data science team is not working on each of these or ignoring one of them, you might be missing out on some opportunities.

Generate Insights

In this use case, the data science team uses data and algorithms to give the business insights that ultimately improve performance.

These can be profound, business-changing realizations, like discovering a very profitable set of customers that should be treated differently, determining strategies to provide same- or next-day delivery to consumers, or deciding on how much capacity you need for your e-commerce fulfillment center. But they can also be smaller discoveries that still pay dividends, like analyzing data to understand lost sales, determining the impact of a recent price discount, or predicting next year’s transportation spend.

Businesses have always needed sharp analysts to answer questions by sorting through data. When you have dedicated data scientists using advanced techniques, working on larger datasets, and scraping external data, you can ask new types of questions, and even get better answers to the ones you’re already asking.

Create Engines

In this use case, your data science team builds an algorithm that gets embedded into an existing system — that is, your team builds a better engine.

For example, you may have a system that helps your business with pricing. The data science team might build a price prediction algorithm, which you could then plug into your existing website or pricing system. Your users would still see the same interface that they’re used to, but now the system’s price predictions are more accurate.

Build Decision Products

When you need people in the business to perform the same analysis on an ongoing basis, and there isn’t a system in which you can easily embed the solution, it makes sense to have your data science team build new decision products.

For example, this could be a tool that helps determine the root cause of service failures in manufacturing or customer deliveries, or a technique that aids in understanding customer or employee churn. Any such analysis that’s repeated at a regular frequency, but lacks a natural system, is a good candidate for a new data-driven decision product.

Building decision products is the most often ignored. Data science groups tend to focus on the first one — just generating insights from the data. Or they tend to overemphasize the need to automate all the data science work and focus on the second one. But there is a lot of untapped potential in building decision products for the business users. You can allow the business user to engage with the data science algorithms to make better decisions.

In conclusion, none of these three use cases are more important than the others. However, if you find your data science team spending all its time in just one area, you might lose sight of other potential opportunities. This is especially true if they spend all their time on generating insights — you may miss out on an opportunity to have an algorithm add repeated value through an engine or decision product.

(A version of this article first appeared in SC Digest.)

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