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Data environments have grown increasingly complex, and data issues can have catastrophic consequences if they are not spotted on time and handled in a timely manner. According to a recent Wakefield survey, “71 percent of respondents said end users were making business decisions with old or error-prone data, 85 percent said their enterprises had made bad decisions that cost them revenue, and 66 percent said their C-suite didn’t even know this was happening”.
To help data teams face these challenges, the AI-powered Kensu platform now leverages data observations inside and outside data pipelines to automatically suggest and maintain monitoring rules. With less effort, data teams are notified as soon as data issues arise anywhere in their data environment and have all the insights (e.g., lineage, application name, file name, owner) they need to troubleshoot the incidents quickly.
In addition, the Kensu platform can automatically stop data applications when data expectations are not matching the AI-suggested rules. This circuit-breaker mechanism prevents data breakdowns from propagating across the infrastructure and reaching dashboards and models relying on it. In complex environments, this reinforces trust in data and allows all stakeholders to focus on building value from it.
This product release also includes several other new features, such as:
• Agent Remote Controller: a new intuitive user interface requiring no coding experience to set up and maintain the agents observing the data from within the data applications
• Observability Dashboard: a new dashboard where users can see all their projects and their corresponding status in real-time and drill down to understand further the events driving the figures.
“There has been a lot of hype about data observability. With this release, we offer companies the true 360° view and control over their data they’ve been searching for”, said Eleanor Treharne-Jones, CEO of Kensu.
“Rather than just focusing on data at rest, our AI-powered platform is the first in the market to monitor data at rest and in motion, in real-time. At a time when budgets are under pressure, this disruptive approach will save countless hours fixing broken data pipelines and ensure businesses maximize the value from their data and their data teams.”
