A lot has been written about data observability by authors, analysts, and vendors over the past few years as it is becoming an increasingly important component of organizations' data architectures. This blog will examine why organizations need data observability and how they should approach the buying experience/cycle.
Let's first level set with a true definition of Data Observability: “Data Observability empowers organizations to detect, predict and prevent data health issues at run-time as data flows through their data pipelines in motion as well as when data is at rest. Resulting in healthier data pipelines, higher trust in data, and a significant reduction in data engineering time”
In other words, the essence of data observability, as opposed to other data quality techniques, is the ability to detect, predict and prevent at run time from within the data pipelines.
Here are some key questions to ask when looking at data observability platforms.
If the answer to the above is “yes,” then carry on reading.
Next, you need to think about the use cases for data observability. That is to say, Why do we need this tool anyway?
Data observability is a critical component in helping IT and, in particular, the planning teams identify redundant data sets. This, in turn, allows business decisions to be made on their validity. Data observability can also provide augmented metadata through advanced lineage to ascertain the ownership of data sets, allowing Fin/Ops teams the ability to accurately cost model and cross-charge business units.
As Data teams evolve into Data/Ops, they have alerts coming at them from all directions: specific custom dashboards, database logs, email chatter, and other sources. Often the sheer volume of alerts becomes overwhelming, and the ability to focus on the real issues is lost. A single data observability platform will alleviate the pain, in much the same way that Dev/Ops benefit from tools like Datadog or Splunk. Data observability provides contextualized run-time alerts and messages only when a rule is triggered, meaning the Data/Ops team can go about their day, without the worry of having to regularly check logs and dashboards in case something has happened.
One of the key differences between traditional data quality applications and a data observability platform is the ability of the platform to learn. A data observability platform is constantly monitoring data pipelines as they execute and can spot anomalies and make rules recommendations. These recommendations can be implemented instantly with just a few clicks, helping protect the data and prevent data issues from propagating across the organization.
Above, we have seen some hard and measurable use cases and benefits of a well-implemented data observability platform. However, you must also take into account the soft non-measurable benefits:
With maturing organizations, we see that building out your data infrastructure with data observability at its heart makes both operational and financial sense. Our customers are reporting not only exceptional return on investment figures but also significant improvements in all of the softer immeasurable areas such as trust, morale, and improved business decision-making.
To learn more, do not hesitate to read the case study about how Kensu enables UniCredit to mitigate data incidents.