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

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"Ask an mParticle Solutions Consultant: What is data quality?"

"Debug customer event collection in real time"

As a Senior Solutions Consultant at mParticle who helps mParticle's customers solve data-related challenges every day, Jodi Bernardini has excellent insight into managing data quality at scale. In this discussion, Jodi offers up some real world insights into data quality, and why investing in protecting it is absolutely worth any organization's time and effort. To start, Jodi he lays out some benefits that companies can expect to realize when they prioritize the quality of their data, like faster time to value, and a greater ease of use among the teams who rely on it to make decisions and drive growth. Jodi then moves into discussing some factors that pose significant threats to data quality, including:

• Handling data ingestion from multiple sources
• Multiple engineering teams implementing data collection from inconsistent (or non-existent) data models
• A lack of intuitive naming conventions or inefficient data modeling decisions

Finally, Jodi offers some advice to companies on how to conduct data planning in a way that ensures data quality from the point of collection.

Interested in learning more about mParticle's data quality tools and templates can help with data quality? Watch this webinar!