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Predictive models are transforming the AI landscape. They can forecast future events, identify past occurrences, and even predict present situations. However, building a successful predictive model is not as simple as it seems.

To achieve an effective predictive model, you need to consider three crucial moments: the prediction time, the prediction window, and the data observability time. Overlooking any of these elements could lead to the downfall of your model. In this article, we’ll discuss these key moments and the potential risks if you ignore them.

The Prediction Time

The prediction time is the moment you execute a prediction. It could be the moment a client arrives, the first day of each month, or every time a user asks for help. It’s the trigger that generates your prediction. Everything in your model will be defined with respect to this central time.

The Prediction Window

The prediction window is the time interval during which you expect a prediction to occur. For example, you might predict the weather for tomorrow or whether a client will buy a subscription in the next seven days. This period is important as it limits the time interval during which you try to observe patterns and collect data. It defines your predictive target and has to be aligned with the actions that you will execute.

The Data Observability Time

Data observability time is often ignored, but it’s crucial to your model’s success. It’s not a single time period; rather, it’s a time point per data source. Suppose you’re running a prediction today, but you don’t have all the data integrated. If your model relies on two or three data sources, you need to know the latency of integrating this data into your pipeline.

More importantly, you need to be aware of the data integration schedule on your specific infrastructure so you can simulate this time with respect to the central prediction time. This will help prevent a bias in your model.

Risks of Ignoring These Crucial Moments

Here are some risks you might be exposed to if you ignore the critical moments for making predictions:

1. Data Leakage: This happens when you feed the answers to your model, allowing it to “cheat.” As a result, the model performs unnaturally well during training but does poorly in real-world use.
2. Bias: You may accidentally introduce some biases in your model during training that won’t be available during testing. This can lead to delayed data, where the information isn’t accessible in real time and is provided after a certain delay.
3. Wrong Timing: You may build a model that you can’t put into practice because you didn’t realize the right timing for predictions.

The End Goal

The end goal is to shorten the prediction time and the data observability time. If you can make predictions for a shorter time and still make actionable decisions, you will have a more accurate model. Similarly, the longer the data observability time, the more uncertainties you will accumulate. Thus, you should also figure out how to shorten this time. This can improve your infrastructure and consolidate data faster.

Conclusion

Building a predictive model is a delicate balance between business and technical considerations. You need to optimize the three key moments and align them with your business strategy. Furthermore, to minimize risks, focus on shortening the prediction and data observability times. By doing so, your business can adapt more swiftly and make decisions based on the most current and relevant data.

If you want to optimize your AI predictive models and drive your business forward, book a meeting with us at NILG.AI to explore solutions tailored to your needs. You can also download our ebook to help perfect your predictive modeling approach.