Monte Carlo data reliability dashboard provides bird’s eye view of data quality

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San Francisco, California-based Monte Carlo, known for its data observability capabilities, today announced a “Data Reliability Dashboard” designed to help business users better understand and communicate the reliability of their data assets.

Announced at Impact 2022, the annual Monte Carlo conference, the new dashboard is a key addition to the company’s flagship platform, which is already being used by several hundred enterprise customers. The platform uses machine learning (ML) to detect data downtime—which is the recipe for broken dashboards and artificial intelligence (AI) models—then provides alerts with the insights needed to help teams assess the impact and remediate the to support the root cause of the problem as soon as possible.

According to a recent Wakefield Research survey, bad data impacts over 26% of company revenue and consumes more than 40% of data engineers’ time. While Monte Carlo’s capabilities have addressed this problem, enterprise users have long missed the ability to gain a more comprehensive view of their data quality and the status of efforts to improve it. This is where the new dashboard comes into play.

“Data controllers know data reliability is important, but typically lack the tools to measure it. The Monte Carlo data reliability dashboard will bridge this gap and enable better tracking of critical (data) KPIs…” said Lior Gavish, CTO and co-founder of Monte Carlo. “This new capability will also give data practitioners and business leaders a common language to measure and improve the quality of their data platforms as well as the ROI of their data products.”


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What kind of KPIs does the Data Reliability Dashboard track?

The dashboard, which is expected to be available in Q4 2022, aims to provide insights into three key areas: stack coverage, quality metrics, and incident metrics and usage.

Stack coverage provides an overall picture of existing data monitoring and observability practices to help organizations identify what’s missing and adopt operational best practices. Then, as part of the quality metrics, the solution looks at the five pillars of data observability – recency, distribution, volume, schema, and provenance – and generates notable data reliability trends over time. This helps companies see if their reliability investments are working or not.

Data Reliability Dashboard
Data Reliability Dashboard user metrics

Finally, the incident metrics and usage dashboard provides key insights into actions such as: B. time to detection and time to resolution of data incidents, as well as metrics on user interaction with said incidents. As Monte Carlo said, this will help teams measure and improve their efforts on the incident response side, minimizing data downtime and optimizing data trust.

What else is there at Impact 2022?

Along with the data reliability dashboard, Monte Carlo announced additional capabilities for its platform at the Impact Summit, including visual incident resolution, which allows data engineers to use an interactive map of their data lineage to diagnose and remediate data breaches.

The feature brings critical troubleshooting data, from data freshness to query logs, into a unified view of affected tables and their upstream dependencies, allowing data engineers to correlate all factors that could be contributing to an incident and reduce time to resolution.

The company also announced an integration with Power BI that enables data engineering teams to properly triage data incidents impacting Power BI dashboards and users, and proactively ensure changes to upstream tables and schemas are executed safely can become.

In the area of ​​data observability, Monte Carlo competes with providers such as Bigeye, Datafold, Datadog, Hightouch and Anomalo.

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