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Anomalo Launches Pulse – A Data Quality Dashboard for the Entire Enterprise

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Data quality platform company – Anomalo – released the Pulse dashboard, which provides an in-depth overview of data quality within an organization and aggregates all key data quality indicators in one place. Executives now have unprecedented visibility into the quality of their enterprise data for the first time.

“As customers deploy Anomalo, they want to understand if data quality is improving over time and if they’re doing a better job with the health of their data. Pulse answers that question, “said Elliot Shmukler, co-founder and CEO of Anomalo.

Just several companies have the tools to measure data quality improvements, and it is never clear what is good enough. No data is without flaws. Data is inherently chaotic and changes over time. Pursuing data perfection is both intractable and impractical. Having data quality KPIs and setting goals based on them is critical to resolving the data quality issue.

Pulse offers a deeper dive into each of these statistics, so it’s clear which next steps should be prioritized for improving data quality in an organization. Buzzfeed VP and Head of Data Science and Analytics Gilad Lotan said: “This feature has been incredibly helpful for our team! Before the Pulse dashboard, it was a laborious and time-intensive task to monitor the health of our tables and checks. This made it difficult to identify where clean-up opportunities might be. Since it launched, our teams have been able to clean things up swiftly and quickly grasp the scope of data issues that surface.”

Data owners can easily understand data quality performance over time with Anomalo’s Pulse dashboard by viewing metrics and KPIs.

Data quality coverage – identify how much of the enterprise data is being monitored for data quality issues, define the gold standard, and identify blind spots

Time of data arrival – Identify tables whose data is late or doesn’t meet SLAs

Data quality trends – visualize monthly improvements in data quality by comparing the number of issues discovered and resolved by the data team

Problem areas – develop the next set of goals based on the ranked list of problem areas


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