This article describes data wellness concepts and actionable steps you can take to examine and optimize the readiness of your data layer before starting with Tealium 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 to review and consider each of the following concepts.
After reviewing and understanding the concepts, advance to the following 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, we offer a "Data Wellness" services package to assist in getting your data ready to use with Tealium Predict. To learn how you can benefit from this service according to your unique data, contact your account representative.
The following steps describe actions you and your teams can take to get your data foundation ready to add Tealium Predict. Use these steps as a general guideline to take the recommended actions to ensure optimal outcome from your Machine Learning models.
For optimal results, we recommend 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.