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

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

According to a recent survey by Wakefield Research, bad data affects over 26% of enterprise revenue and takes more than 40% of data engineers’ working hours. Now, while Monte Carlo’s capabilities have been tackling this problem, enterprise users have long missed the ability to gain a broader bird’s-eye view of their data quality and the status of efforts to improve it. This is where the new dashboard steps in.

“Data leaders know data reliability is important, but typically lack the tools to measure it. Monte Carlo’s Data Reliability Dashboard will bridge this divide and provide better tracking for critical (data) KPIs…,” Lior Gavish, CTO and cofounder at Monte Carlo, said. “This new functionality will also give data practitioners and leaders a common language to measure and improve the quality of their data platforms, as well as the ROI across their data products.” 

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What kind of KPIs will data reliability dashboard track?

The dashboard, set to be available in the fourth quarter of 2022, is designed to provide insights around three key areas: stack coverage, quality metrics and incident metrics and usage.

Under stack coverage, it will paint an overall picture of data monitoring and observability practice in place, helping enterprises figure out what’s missing and adopt operational best practices. Then, as part of quality metrics, the solution will look at the five pillars of data observability — freshness, distribution, volume, schema and lineage — and generate notable data reliability trends over time. This will help companies see if their reliability investments are working or not.

Data reliability dashboard
Data reliability dashboard user metrics

Finally, with incident metrics and usage, the dashboard will provide critical action insights such as time to detection and time to resolution of data incidents, as well as user engagement metrics with the said incidents. This, as Monte Carlo said, will help teams measure and improve their efforts on the incident response side, thus minimizing data downtime and optimizing data trust.

What’s more 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 enables data engineers to use an interactive map of their data lineage to diagnose and troubleshoot data breakages.

The capability brings critical troubleshooting data, from data freshness to query logs, in a unified view of affected tables and their upstream dependencies, allowing data engineers to correlate all the factors that might contribute to an incident and accelerate the time to resolution.

The company also announced an integration with Power BI, allowing data engineering teams to properly triage data incidents that impact Power BI dashboards and users, as well as proactively ensure that changes to upstream tables and schema can be executed safely. 

In the data observability space, Monte Carlo competes with players like Bigeye, Datafold, Datadog and Anomalo.

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