This document describes data wellness concepts and actionable steps you can take to examine and optimize the health of your data layer before starting with Predict.
In this document:
This section defines basic data wellness concepts you can use to examine, audit, and optimize to improve the overall wellness of your data. The goal is to ensure that you start with a trusted data foundation. As data maturity levels differ between businesses based on a number of factors, it is important for organizations to review and consider each of the following concepts. After reviewing and understanding the concepts, advance to the data readiness steps.
Defining your Strategy
Define your business goals and your strategy. Define what do want to accomplish with machine learning technology, identify your audiences, and decide what data you want to capture and act on.
Examine the volume and completeness of your data. As always, better data produces better outcomes.
Governance and Consent
Review and understand compliance practices and requirements regarding data usage.
Ensure that the insights you identify are accessible to other tools within your "tech stack".
To learn more about data readiness and review the wellness checklist, see the Data Readiness for Machine Learning Checklist.
If you need help getting started, Tealium offers a Data Wellness services package to assist in getting your data ready to use with Predict. To learn how you can benefit from this service according to your unique data, contact your account representative.
The following list describes actions you and your teams can take to get your data foundation ready to add Predict Machine Learning. Use these steps as a general guideline to take the recommended actions to ensure optimal outcome from your Machine Learning models.
Up until this point, your event data infrastructure has not yet acquired machine learning intelligence. Enrich and organize your incoming event data to improve the quality of the insights automatically produced AudienceStream. This step will enable you to immediately activate machine learning insights across your entire implementation, regardless of whether or not other components of your implementation have machine learning capabilities.
For optimal results, Tealium recommends training using a prediction timeframe that contains 90 or more days of data. In many circumstances and depending on your target use case, you can successfully train models using a smaller date range and less data. As a minimum, you must train a model on at least as many days as your prediction timeframe.