How to manage and maintain data quality in your ingestion process with CloverDX
Bad or invalid data can mean your whole data ingestion process grinds to a halt. Which means you having to spend time finding and fixing the problem, and your customers having to wait longer to get their data onboarded properly – not a great first experience.
Automating data ingestion makes your process faster, more reliable and easier to scale. But how do you make sure you’re planning for that (inevitable) bad data, and including data validation steps in your pipeline?
In this webinar we’ll walk you through the process of managing data validation in your ingestion pipeline, and show you how to detect, monitor and fix data quality issues.
You'll see how to:
- Detect common data quality issues in CloverDX
- Manage invalid data in your CloverDX processes
- Monitor your data quality continuously using notifications and reporting
- Incorporate best practices for maintaining data quality
Cullen Patel works with customers to introduce CloverDX, prototype rapid solutions to meet different use cases, and solve specific client data problems.
CloverDX has been working at the coalface of data challenges since 2007. We’ve worked with companies large and small around the world, in industries such as finance, healthcare and consulting, to help them build and manage better data pipelines and deliver better data outcomes.