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How to Build Impactful Machine Learning Models, Not “Data Science Wall Art”

One of the most common mistakes users make is that they use data science to create a beautiful dashboard – and then it just sits there. 

Creating a model whose output has no real impact on the business wastes everyone’s time and resources—from the data scientists who worked on the model, to the operators on the shop floor who could’ve benefitted from an effective machine learning model.

Models with no actionable outputs can be thought of as “data science wall art.” In this article, you’ll learn how to avoid throwing data into the ether and discover how to use it to make actionable models instead.

What “Data Science Wall Art” Looks Like and How to Avoid It 

There is a big misconception that just doing data science is enough. Sadly, it’s not. Behind every data science project, there always lurks the eternal question: So what? If you can’t answer that question, your model is useless. 

A Real-World Example of Machine Learning Models

Manufacturing is one of the industries that stands to gain the most from data science. With all the data manufacturers collect on the shop floor, there are endless possibilities to make production more efficient and cost-effective, reduce product time to market, and improve product quality. 

But, too often, engineers are so focused on creating a machine learning model that will satiate their curiosity that they don’t consider if what they’re building is worth a use case. When this goes unchecked, they end up creating wayward, useless models, and the rest of the organization starts to lose trust in data science. After all, why should they continue to invest in something that provides no business value? 

For example, say you’re using real-time sensor data to build a machine learning model. That model then tells you that in 20 minutes, your pressure gauge will give high readings. That’s great! But…

  • So what?
  • Is there a prescribed action?
  • Do you have someone on the shop floor who can do something about it?

If there’s no action to take, the model isn’t serving any purpose. 

Deploying a Machine Learning Model – Our Advice

Go back to the basics. And in data science, “basics” means the Cross-Industry Standard Process for Data Mining (CRISP-DM) cycle. 

The first phase of CRISP-DM is business understanding, and it’s what’s often find lacking in machine learning models that aren’t having their intended impact. In this stage, users scope out the project and define its business expectations.

When you design a use case, the first question you should ask is: What will you do with the information from the machine learning model compared to what you’d do if you didn’t have it?  

That will help you define:

  • If you have a viable use case, and
  • What potential actions the machine learning model will lead you to take

Especially for manufacturers, it’s essential to get your domain experts involved in this step—they’re the ones who know the business like the back of their hand.

Successful data science initiatives are a team sport; getting multi-disciplinary, multi-department involvement in your projects and getting input from team members with different skillsets and backgrounds is the best way to break down silos and ensure you’re building the best possible solution. 

Let’s go back to our real-time sensor data example. What would you do differently if you could predict pressure changes in advance?

Rather than shrugging your shoulders and sending the machine learning model into production regardless, your looped-in domain expert could document the current process and cue the model to alert the operator to pull a level and adjust the pressure levels.

Wrapping Up

When you isolate your models, they’re far less likely to have their intended impact. To design data science projects that have an actionable outcome, avoid putting your data science team in a silo and instead encourage cross-team collaboration from the moment models are conceptualized. 

To do so, you need to encourage true team transparency and build a cross-functional understanding, not only of individual models, but of data science concepts as a whole. If you want another set of eyes to help you determine your highest value data science use cases, our team is here to help. To learn more, request a free AI Assessment.