Gartner’s ‘Data Observability Enabling Proactive Data Quality’ Report Cites Root Cause Analysis and Recommendations As Differentiating Features

When evaluating data observability and data quality tools, it can often be difficult to understand what features and functions really matter. Gartner’s recent report, “Innovation Insight: Data Observability Enables Proactive Data Quality,” analyzes the space and highlights root cause analysis and recommendations as important market differentiators for data observability providers.

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Gartner analyst Melody Chien wrote:

“Detection and monitoring features are mandated, while root cause analysis and recommendations are considered an important market differentiator among vendors. Monitoring tells you what’s going on; recommendation tells you how to fix the issue and therefore, at the end of day, system downtime or data errors can be prevented.”

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The report validates Anomalo’s product and goal of providing best-in-class data quality monitoring through root cause analysis and recommendations. Using machine learning, Anomalo makes it easy to identify, root cause, and fix your data quality issues, ultimately giving you greater confidence in your data.
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Critical Features

Gartner’s report defines data observability tools as offering holistic monitoring of your data systems to provide visibility and insights into your data pipelines and infrastructure. While this definition is probably familiar, what stands out in this report is that detection and monitoring features are no longer differentiators—they’re table stakes.

According to Gartner, today’s data observability tools stand out in the market if they perform root cause analysis and provide recommended solutions. It’s these features that allow a data observability tool to truly satisfy the customer’s end goal: resolving and preventing data quality issues.

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Enter Anomalo’s Unique Root Cause Analysis

Data pipelines are complex, which means figuring out the root cause of any data issues can be a lengthy and difficult process. Anomalo performs automatic root cause analysis, powered by our machine learning capabilities, and includes a wide range of features to make resolving the issue faster and easier.

Samples and visualizations – When investigating a problem, being able to see the problem in the same tool that reported it is a big help. Anomalo shows you samples of bad data and provides powerful visualizations that help you understand the extent and timeline of the problem.

Ticketing integrations – Having to manage issues across multiple tools is an extra burden on your team. Anomalo integrates with your existing ticketing solution so that you don’t have to monitor multiple tools for progress on issues.

Lineage – Anomalo provides automatic upstream and downstream lineage to help you see the extent of the problem, pinpoint the origin, and resolve issues faster.

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Here’s what an Anomalo customer Julia King, BI & Analytics Director at Square, had to say:

“Using this solution, we could identify and quickly mitigate data quality issues; the time-saving in diagnosing and identifying root causes is invaluable. I can easily see companies spending hundreds of thousands of dollars on this tool in the future and deploying it across the enterprise.” 

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If we’ve got you interested in data observability and quality, chat with us about trying Anomalo with your team for free.