Get Control by Automating Data Quality Processes at Scale
Requirements for Successful Data Quality Automation
Identify Hidden Patterns
Easy Model Configuration
What If Data Quality Scenarios
Explainable Decisions Guide
Your Powerful AI Data Quality Workbench
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
Capture and See What's Going On In The Data Flows
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
Assign relative quality scores to records in flight between systems
Drill down to the field level as it's flowing through
Learn More About How To Succeed With Put It Forward Delphi
5 Keys to Successful Data Governance
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.
What is Data Integration?
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.
Scaling by AI Automation
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.
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Control Your Data Story - Why Partner With Put It Forward?
Complete Data Platform
Secure and Compliance Ready
Key Features Of A Predictive Data Analytics Platform
Frequently Asked Questions about Data Quality
Data quality automation is the process of using machine learning and artificial intelligence to ensure that data is of high quality and free of potential errors.
The process can include steps like making sure that data is complete, accurate, and free of duplicates. Intelligent data quality automation helps organizations improve the quality of obtained data by catching and fixing errors before they become a problem.
An automated data quality platform by Put It Forward gives organizations full control over their data and its quality.
With its use, organizations can test multiple different scenarios efficiently and identify hidden patterns or correlations by adjusting the data models with new parameters.
The automated data quality platform can also be used to predict the impacts of correlations and modify operational workflows to trigger specific business events.
There are many data quality automation benefits, including the ability to:
- Automate data quality checks and improve their accuracy and efficiency,
- Streamline data management processes,
- Improve data governance and control,
- Reduce costs associated with manual data quality management processes,
- Increase consistency and accuracy of data across systems and departments,
- Reduce the need for manual data quality checks,
- Improve data quality by identifying and correcting errors before data is entered into systems.