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Deploying predictive analytics at the right time

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You want to use the predictive analytics capabilities in Splunk ITSI to forecast trends and get ahead of the competition. You want to know when is the right time to deploy this capability so that you use it correctly.

This article is part of the Definitive Guide to Best Practices for IT Service Intelligence. ITSI end users will benefit from adopting this practice as they work on Service Insights. 

Solution 

To be sure you are ready to begin using predictive analytics, make sure you meet these five key requirements:

  • Accurate health service scores. If you don't have accurate historical data to base predictions on, your predictions will be worthless. 
  • Sufficient training data. At a minimum, you need 14 days of data, but 30, 60, or 90 will be much better.
  • Cyclical patterns in KPIs. The training data should contain expected cyclical changes so those don't get flagged as unexpected events. 
  • Failures in training data. Just as predictive analytics relies on accurate historical data, it also relies on failure data. It can only predict failures that are similar to those seen in the past. Therefore, if your training data is perfectly clean, predicting failures will be difficult because it has nothing to learn from. 
  • Knowledge of machine learning math. Predicting the future isn't a trivial task. Setting up a good predictive analytics model requires time and knowledge investment. When creating your model, you will have to make decisions you can only make with some understanding of machine learning algorithms.

Next steps

This content comes from Splunk .Conf presentation, The Definitive List of Best Practices for Splunk® IT Service Intelligence: How to Configure, Administer, and Use ITSI for Optimal Results, part one presented in .Conf23 and part two presented in .Conf24 session. In the session replays, you can watch Jason Riley and Jeff Wiedemann share the many awesome best practices they've amassed for designing key performance indicators (KPIs), services, episodes, and machine learning to maximize end-user experience and insights. Whether you're new or experienced, you'll come away with tactical guidance you can use right away.

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