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A Practical Guide to Effectively Evaluating and Deciding on Data to Enrich and Improve Your Models

The Importance of Data Enrichment

As a data scientist, I’ve had the opportunity to work with various data vendors to enrich our models and improve their performance. In this guide, I’ll share my experiences and provide practical tips on how to effectively evaluate and decide on data to enrich and improve your models.

The Problem with Data Vendors

When working with data vendors, it’s essential to be aware of the potential pitfalls. Many vendors may promise more than they can deliver, and it’s crucial to evaluate their data carefully before committing to a partnership. In this guide, I’ll share my own experiences with data vendors, including a particularly memorable instance where a vendor’s data tanked on our test set.

Evaluating Data Vendors

When evaluating data vendors, there are several key factors to consider:

1. Who are their customers? How many large customers do they have in your industry?
2. Cost: At least order of magnitude, as this might be an early deal breaker.
3. Time travel capability: Do they have the technical capability to ‘travel back in time’ and tell you how data existed at a snapshot back in time? This is critical when running a historical proof of concept.
4. Technical constraints: Latency, uptime SLA, etc.

Proof of Concept (POC) Testing

Assuming the vendor has checked the boxes on the main points above, you’re ready to plan a proof of concept test. You should have a benchmark model with a clear evaluation metric that can be translated to business metrics. Your model should have a training set and an out-of-time test set (perhaps one or more validation sets as well). Typically, you’ll send the relevant features of the training and test set, with their timestamp, for the vendor to merge their data as it existed historically (time travel). You can then retrain your model with their features and evaluate the difference on the out-of-time test set.

Set a Higher ROI Threshold

Start by calculating the ROI — estimate the incremental net margin generated by the model relative to the cost. Most projects will want a nice positive return. Since there’s a bunch of room for issues that erode your return (data drift, gradual deployment, limitation on usage with all your segments, etc.), set a higher threshold than you typically would. At times, I’ve required a 5X financial return on the enrichment costs as a minimum bar to move forward with a vendor, as a buffer against data drift, potential overfitting, and uncertainty in our ROI point estimate.

Partial Enrichment

Perhaps the ROI across the entire model isn’t sufficient. However, some segments may demonstrate a much higher lift than others. Splitting your model into two might be best and enriching only those segments. For example, perhaps you’re running a classification model to identify fraudulent payments. Maybe the new data tested gives a strong ROI in Europe but not elsewhere.

Phased Enrichment

If you’ve got a classification model, you can consider splitting your decision into two phases:

1. Phase 1 – Run the existing model
2. Enrich only the observations near your decision threshold (or above your threshold, depending on the use case). Every observation further from the threshold is decided in Phase 1.
3. Phase 2 – Run the second model to refine the decision

This approach can be very useful in reducing costs by enriching a small subset while gaining most of the lift, especially when working with imbalanced data.

How AI legalese decoder Can Help

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By using AI legalese decoder, you can save time and money, and ensure that your data enrichment efforts are effective and efficient.

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