Kensu: Data Reliability Challenges

Eleanor Treharne-Jones, CEO
For years companies have been investing in data, positioning it at the core of their business strategies. This evolution has forced data teams to grow their activities and infrastructures, where the probability of data incidents, such as missing data or inaccuracies, has increased proportionally.

While the source of these incidents may vary (e.g., human errors, new regulatory changes, divergent business requirements), their consequences are the same. On the one hand, data practitioners do not have the information to troubleshoot them quickly; consequently, they waste time identifying and resolving problems. On the other hand, managers make decisions based on inaccurate insights, customers face a poor experience, and the company loses revenue.

Enter Kensu.

Kensu’s Data Observability Platform helps tackle data incidents at the source in real time. Their AI-powered solution, which can be deployed in any cloud environment or on-premise, monitors data in motion and at rest, so data teams have 360° visibility over their data. At any time, data professionals can access contextual insights, such as application information (e.g., pipeline name, version), schema, metrics (e.g., max, min, mean, standard deviation), and lineage.

This way of observing data inside and outside the applications in real time is a game-changer for data teams. Instead of wasting hours or days understanding where the issue is coming from, data teams can troubleshoot them immediately and prevent them from propagating. This capability has several benefits:

● Data professionals stop wasting time resolving data issues.

● Trust in data is restored, and backfires from business users decrease.

● Decisions are based on reliable data.

“There has been a lot of hype about data observability. At Kensu, 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.”

One of Kensu’s clients, a large financial institution, uses a 6-year-old CRM platform to help thousands of employees make daily decisions. Over time they had to build and maintain a complex data infrastructure where a minor incident could impact the whole data chain and take days to identify and resolve. This summer, they deployed Kensu across 12 countries, and they can now troubleshoot such problems within hours.

Among the different trends influencing the data landscape, data observability is one of the most impactful, as it significantly helps data teams to get more value from their data, cut resolution time, and improve the trust of the stakeholders

Kensu is also being used to maintain data catalogs which consume more resources as data environments become complex. Kensu’s platform can automatically populate hundreds of data sources and feed data catalogs seamlessly with this information. This capability also saves time and reinforces trust in data as the information in the catalogs is up-to-date.

To find out more. Kensu offers a Community Edition which is the first in the data observability category. Technically or non-technically savvy users can explore Kensu’s platform in a dedicated environment and access all product features and management capabilities. They have the opportunity to leverage real-time, contextual data observations in a developer environment without any additional cost. As a result, they can understand and deliver the potential value of data observability within their organizations. In parallel, the community members can access learning resources and discussion channels to collaborate with other data practitioners.


San Francisco CA

Eleanor Treharne-Jones, CEO

Kensu’s solution monitors data in motion and provides business-critical information such as application information, profiling, and lineage. Thanks to these insights, data teams can troubleshoot issues faster and prevent them from propagating across the data value chain