Skip to content

A buyers guide to choosing the most suitable data observability platform

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.

  1. Are the observations made at run-time within the data pipeline?
  2. Can the platform “circuit break” (i.e., stop the execution of) a broken data pipeline to prevent the propagation of bad data?
  3. Can the platform be agnostic to its implementation location, on-premise, public cloud, hybrid cloud, or multi-cloud (that is to say, not tied to a specific cloud provider or a cloud-only deployment model)?.
  4. Does the platform learn from historical data by analyzing data at rest?
  5. Does the platform integrate with all the major ticketing systems, such as Slack, Jira, or PagerDuty?

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? 

Pipeline performance monitoring

Data engineers can spend an enormous amount of their time troubleshooting and dealing with failed pipelines, with the impact of poor data having a significantly adverse effect both in terms of decision making and ultimately the bottom line.    Data observability from within the pipeline will provide immediate alerts if a pipeline fails. These alerts contain contextual information to assist the data engineers in better identifying the issue(s). 


Fin/Ops (cost modeling and capacity planning)

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.

Eliminate alert fatigue

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. 

Identify unknown unknowns

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:

  1. Improved trust in data
  2. Improved data team morale resulting in higher staff retention rates
  3. Improved business decision-making as data is of a higher and more trusted quality. 

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.