Put high-quality data in the driver's seat

When you invest in protecting the quality of your customer data, you can:

Empower your marketing and product teams to make decisions with confidence

Instill trust in customer data among teams who rely on it

Activate real-time use cases more quickly, without ad hoc development

Watch the video

Read the docs

Why prioritize data quality?

Faster time to value
When teams don’t trust their data and have to double-check it for accuracy, opportunities are lost. Protecting data quality means faster activation.

Get more out of downstream tools
With accurate and up-to-date data, you can be sure you're getting the most out of your engagement and analytics tools.

Spend less time debugging
With validation tools ensuring that the data you collect matches the data you expect, engineers will spend less time fixing data collection code and more time building core features.

Learn more

Debug customer event collection in real time

This video demonstrates how mParticle’s Live Stream gives you a real time visibility into your incoming data, and flags events that do not match the specifications of your data plan.

Ask an mParticle Solutions Consultant: What is data quality?

Andy Wong is a senior leader on mParticle’s Solutions Consulting team, discusses what data quality means, why it is important prioritize, and how having a centralized data planning team can help organizations collect and activate high quality data.

Three threats to customer data quality (and how to avoid them)

In this video, Jodi Bernardini--a Senior Solutions Consultant at mParticle--discusses three major threats to customer data quality, and offers advice on how to avoid them.

"Since adopting data plans, quality violations have dropped significantly. We're spending less time fixing data downstream and more time building new capabilities."

Jeremiah Mercury

Data Science Vice President, EPIX

Webinar

Protect data quality with mParticle

Learn how mParticle's data planning tools help you build cross-functional data plans, improve developer efficiency, and increase the efficacy of downstream applications.