How to Improve ML Model Accuracy: Data Over Data Science

Deploying a new machine learning (ML) model in an enterprise is an exciting moment. However, it’s also one that is often fraught with worry for data teams. Their concerns revolve around one critical question: “Will the model perform well in production?” This encompasses issues such as accuracy, bias, speed, and more. Although these models are typically deployed with mechanisms to measure such variables, data teams frequently discover too late that a newly deployed model underperforms in some key facet of the business. Consequently, an entire ecosystem of tooling has emerged to enable data teams to manage this process in better, more scalable ways (MLOps). However, often a simpler approach can deliver more value: integrate the business from the start. 

Building an Accurate Model

Data teams spend a significant amount of time assessing model accuracy—quite a lot, in fact. Surprisingly though, this extensive time commitment is not always due to the complexity of the underlying algorithm needs serious figuring out (though of course they do need serious figuring out). More often, the challenge lies in determining the quality of the data used for performance assessment and ensuring it closely resembles both what the model will encounter in production and as importantly, what is of business value.

Data teams tasked with harvesting this data on their own are at a disadvantage compared to those integrated with business units. The reason for this disadvantage is simple: building an accurate model requires a deep understanding of the data, and it is the people in the business units who are most familiar with it. It would be foolish not to leverage their insights. It’s worth noting that these dynamics have happened at least in part due to the trend of sourcing data scientists from academia (mea culpa). As an industry, we’ve spent the last decade trying to familiarize ourselves with the businesses for which we’ve been tasked with doing data science.  

Expert users understand the data and can easily spot when models are missing out on nuance in the data. Further, they can identify gaps in the data and recommend reputable sources to enrich the data so that the results are more complete and more accurate than they were before.

Collaborating with subject matter experts not only helps to improve accuracy in machine learning, but it also fosters greater trust in the data, the models, and their results across your organization. 

Tamr Data Products: Designed to Keep Humans in the Loop

Tamr data products combine advanced AI with human oversight to improve the accuracy of your ML models and deliver higher quality data. Using advanced AI to compare and score diverse datasets, Tamr data products enable organizations to scale and adapt to changing business needs faster than with traditional, rules-based approaches.

Further, Tamr’s pretrained machine learning model taps into Tamr’s robust library of continuously-improving matching models to realize the accuracy and performance benefits of ML without the high upfront investment. Using Tamr ID, they identify the most likely matches in your data and narrow down results to the recommended match. And by employing semantic comparison with large language models (LLMs), Tamr data products can identify discrete similarities and differences to contextualize the data, extract key features, and improve matching accuracy.

Finally, Tamr built their data products to empower data consumers across the business to easily review data, provide feedback, and override matches via a simple UI. That way, users can collaborate with the data, give feedback, and make changes quickly and efficiently. 

Want to learn more? Take a test drive to see Tamr in action.