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What is Data Observability?

If you are reading this, you are probably part of an organization with a sprawling data infrastructure, a veritable warren of transactional systems, databases, data warehouses/lakehouses, file stores, message queues, and streaming platforms. All of these systems are likely interconnected by myriad scripts stored in repos and triggered by orchestrators/cron jobs with the purpose of ultimately updating reporting dashboards or serving data for ad-hoc analytics. Without data observability, there is no guarantee that what ends up on these dashboards is accurate or timely!


The Data Observability Problem


The data delivered by enterprise systems has never been more critical. It is the linchpin of daily meetings, the bedrock of key decisions, and the basis for evaluating performance and incentives. So when these data systems fail or the data is found to be inaccurate, it leads to doubt, distrust, and loss of momentum.


This has led to organizations instituting data quality checks within their data pipelines. However, these checks are embedded deep in code, where they are difficult to track, aggregate, and update. Moreover, the overall quality system is inscrutable to key stakeholders outside the engineering teams. These are the responsible administrators, and consumers who depend on the data. This lack of data observability impedes governance, and frustrates efforts aimed at continuous improvement.


The Data Observability Solution


Data observability solutions allow you to:

  1. Aggregate quality checks in one place, where it becomes easy to evaluate quality coverage

  2. Deploy new quality checks and update existing ones without having to redeploy your code

  3. Send alerts to stakeholders outside of data engineering teams when something goes wrong

  4. Empower engineering teams by surfacing relevant information from within the complex pipelines so they can diagnose and resolve issues quickly

  5. Give data engineers, administrators, and consumers a unified interface from which to communicate on data quality and hold each other accountable

  6. Provide metrics and dashboards so that data administrators can assess the health of the system at a glance

  7. Present data quality history to allow engineers to demonstrate the impact of improvements they have made, as well as to equip data administrators to report to senior management

  8. Allow data consumers to become a part of the solution by empowering them to contribute their domain knowledge via rules that can be created from an easy UI interface

As you can see, data observability defines the next paradigm in data quality, taking it from merely an engineering exercise to one that permeates the entire organization.


If you are interested in a well-built, full-featured data observability solution at common-sense pricing, check out our ExpertSense!

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