Data Quality Toolkit review

In 2021, a guest on The Data Stack Show told us that everybody has a data quality problem. We hear validation for this statement every day in our conversations with customers and prospects. Bad data is a primary pain point for most data teams. It’s also one of the most critical problems to solve. GIGO is an immutable law. If you don’t solve for data quality, bad data will subvert all of your efforts to help your business drive revenue.

There is no silver bullet for data quality, but if you make it a priority, using proven methods and smart tooling, you can make significant breakthroughs. We released our Data Quality Toolkit for event data this week to help.

It’s the only solution for customer data quality that delivers a comprehensive set of tools in a centralized platform. We built it with a keen understanding of the challenges data teams face in ensuring data quality across complex infrastructure.

Garbage in / garbage out

Our founder knows the impacts of bad data well. During his time as a Machine Learning leader, he came to the conclusion that data quality issues, more than anything, limit a business's ability to effectively use its data to create competitive advantages. He expounded on this in a recent webinar.

Settling for poor data quality is no longer an option. You can’t deliver the cohesive end-to-end experiences that today’s consumers expect with bad data. Data quality is even more important if you want to take advantage of AI/ML.

Existing walled garden data quality solutions come up short because they only address pieces of the problem. With no central place to manage and enforce data quality, you only end up with more fragmentation. That’s why we put together a toolkit to help you manage the entire data quality lifecycle from a central command center.

Quality in / quality out

Our Data Quality Toolkit gives you tools to manage every data quality workflow and keep a watchful eye on your entire system. Here’s a recap in case you missed our launch posts this week.

• Data Catalog for collaborative event definitions – Align every team around the same event definitions and integrate data quality into your existing workflow. Read the launch blog for a deep dive, and check out the docs for our Data Catalog and Event Audit API to learn more.
• Tracking Plans for violation management – Easily enforce data quality standards on incoming events. Read the launch blog for a deep dive, and check out the Tracking Plans docs to learn more.
• Transformations for real-time schema fixes – Fix bad data in real time after collection and before delivery. Read the launch blog for a deep dive, and check out the Transformations docs to learn more.
• Health Dashboard for centralized monitoring and alerting – Monitor data quality and manage alerts across your stack from a central platform. Read the launch blog for a deep dive, and check out the Health Dashboard docs to learn more.