How to Keep Predictive Analysis Data up to Date

After the loading step of extract, transform, load, after you get your data into that separate database, data mart, or warehouse for analysis, you’ll need to keep the data fresh so the modelers can rerun previously built models on new data.

Implementing a data mart for the data you want to analyze and keeping it up to date will enable you to refresh the models. You should, for that matter, refresh the operational models regularly after they’re deployed; new data can increase the predictive power of your models. New data can allow the model to depict new insights, trends, and relationships.

Having a separate environment for the data also allows you to achieve better performance for the systems used to run the models because you’re not overloading operational systems with the intensive queries or analysis required for the models to run.

Data keeps on coming. Implementing automation and the separation of tasks and environments can help you manage that flood of data and support the real-time response of your predictive models.

To ensure that you’re capturing the data streams and that you’re refreshing your models while supporting automated ETL processes, analytical architecture should be highly modular and adaptive. If you keep this design goal in mind for every part you build for your overall predictive analytic project, the continuous improvement and tweaking that go along with predictive analytics will be smoother to maintain and will achieve better success.

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