Successful Data Science Projects: Insights Are Great, But Outcomes Are Better

Scaling AI Tony Olson

This blog post is part of a series of guest publications by Excelion Partners.

When it comes to data initiatives, data exploration is an important part of the process. It can be very tempting to execute projects that will help you explore business insights. However, while you’re ramping up your efforts, it’s crucial to ensure you’re looking for outcomes that align with business goals and not just general insights, so you can drive an ROI from your efforts.

What is the difference between insights and outcomes?

Both insights and outcomes come as a result of your data efforts. Insights are information-based findings, such as “Did you know 40% of your customer base is going to drop you in 6 months?” Outcomes are more strategic, such as “We were able to save 40% of our customer base as a result of this change we made.”

In short, insights are the knowledge and outcomes are what happens when you modify the business process to include insights.

Why should you focus on outcomes?

Insights can be beneficial to your business, but it’s harder to put a number on the return they provide. Especially when you’re first beginning your advanced data science and AI efforts, it’s crucial to get ROI-generating quick wins under your belt.

From there, it can sometimes make sense to move onto more exploratory insights and more initiatives without specific outcomes, but you should only do so once you’ve developed a track record of ROI-generating projects.

How do I decide what outcomes are important to track?

Outcomes that have value should be tracked throughout the project. Look at anything that’s directly delivering value to your organization and ensure it is documented.

A very large piece of this is ensuring you have ROI and that your efforts are justified. Ways your project can have ROI include reducing business expenses, increasing business revenue, driving team efficiency, and giving your business a competitive edge. By reporting to executives, you can demonstrate the money you’re making or saving throughout the project.

It’s important to track outcomes over insights as you’re beginning your advanced data science and AI efforts. Whenever you implement a project, it’s crucial to determine and track those outcomes so you can effectively report them to executives (and get approval for future projects!).

This blog post is part of a series of guest publications by Excelion Partners, a data science and IoT consulting organization focused on building solutions and helping you discover evidence that creates better business decisions. Check out their blog and read about their projects here.

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