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Prepare Your Data for AI
Data preparation can make or break your AI project. Learn how to define your data requirements, create a data inventory, and evaluate the data for AI readiness.
The secret to AI project success is your data. High quality, unified data streamlines the learning process for AI algorithms, helping them operate more efficiently and produce more accurate results.
- Review the data requirements that you identified in the AI project planning stage. What objects, fields, and external data sources are needed to make the features work correctly?
- Create a data inventory to help manage diverse data assets. For example, you can list the project’s required data sources, capture how the data is classified, how often the data is refreshed and so on. See Data Fundamentals for AI.
- Evaluate whether the data is ready for AI. What’s the quality of the data? Do you need to establish governance for the data? Do you need to migrate and centralize the data?
After evaluating the data’s readiness, you can start resolving the most critical data challenges, which typically include quality issues, integration hurdles, gaps in the data, and sometimes even outdated data infrastructure. If you don’t resolve those issues early on, your AI project might be built on unreliable or inaccurate data.
For more detailed information about the process of preparing your data for AI, see the AI + Data: Project Planning module on Trailhead.
