Requirements for Successful Data Quality Automation
Designed to be used by the professional that needs to include test multiple different scenarios quickly and at scale.
Quickly adjust the models with new parameters to identify hidden patterns and correlations
Predict impacts of correlations within the data and feed into operational workflows to trigger business events
Work directly with predictive lead scoring analytics models or the data scientists on your team to define which criteria the machine learning should consider.
Quickly know the quality of your input data before it proliferates across your enterprise
Once your data is created and stored you need to plan for the management of its lifecycle. A data warehouse like Snowflake requires a solid governance plan.
Data integration comes in many styles and formats that can easily be confusing to the novice. Learn about the different types and when to use each for your benefit.
Repeatable patterns for success to scale by automation and AI driven processes. Learn from how we’ve managed to leverage these two concepts for scale.