Prepare Your Training Data

What is Data Prep?

Data prep is the process of preparing your data so that it’s optimized for AI Models (formerly Einstein Studio) to train predictive models. Better predictive models derive more useful predictions and prescriptions, resulting in better decisions and improved business outcomes.

Data preparation involves aggregating and optimizing data associated with the outcome variable you're investigating, along with potential predictor variables that can influence the outcome. Data preparation is also a process of iterative refinement. As you dig deeper into your data, new clues emerge. Discoveries can cause you to reassess previous assumptions and adjust your data prep implementation accordingly.

Invest in Data Prep for Your Solution

Data scientists typically invest significant time and effort to plan and prepare their data. They know how much the quality of the output depends on the design and quality of the input. If you aren’t able to match the wizard-level knowledge, skills, and training that data scientists bring to the data prep process, you can still succeed if you:

• have domain knowledge of the data associated with the business outcome you’re trying to optimize
• use Data 360’s extensive data integration capabilities to aggregate, clean, and populate the DMO with data that’s optimized for model training
• apply common data prep techniques to produce high-quality data that is accurate, complete, representative of your business operations, and relevant to your solution
• improve business results by applying basic principles to help implement your solution - even if you aren’t a data scientist.

Use Data 360’s Data Integration Capabilities

AI Models relies on data stored in Data 360 data model objects (DMOs). Data Cloud provides a variety of powerful tools you can use to prepare (extract, load, and transform) your data. That way, you can populate a DMO with information that’s optimized for AI Models to use for model training.