Without Observability, DataOps Is Doomed
A business is only as healthy as its data. Organizations rely on data not just to accelerate and adapt, but increasingly, to perform the most basic of business operations, from hiring new personnel to launching and moving products.
Much like DevOps in software development — which served as a model for this newer function — DataOps acts as a bridge between different technical data functions, including engineering, operations, and analysts, and even, to some extent, nontechnical business users. The purpose of DataOps is to get a baseline picture of the data landscape and establish best practices for data use throughout the organization. It is a blend of protocols, people and technology that are essential for maintaining healthy data.
Without DataOps, it’s next to impossible to keep data flowing in all the ways the business needs. Yet without data observability, DataOps is running blind.
The Data Landscape Is Fragmented and Frantic
Even just a few years ago, the data lifecycle was centrally managed by IT. If there was an issue, the engineers who built the pipeline could catch it and correct it. But the landscape has become fragmented.
Every year, there are more and more vendors, more and more tools, more and more pipelines. Today, the average company draws on over 400 data sources.
Meanwhile, the data itself has become increasingly monetized: Companies depend on real-time data to orchestrate supply chains, and consumer brands live or die based on the accuracy and relevancy of recommendation algorithms.
We are no longer living in a world where teams can review reports or dashboards a few times a week, with the luxury of hours — or even days — to resolve issues with data when they occur.
What Is Data Observability?
Most people like to think of data observability as a window into the state of their data. But data observability isn’t static or passive — it is an active process. At its core, data observability covers four key components:
- Data quality. Can you confirm that the data you rely on is current, accurate and in the correct format?
- Data lineage. Do you trust the source of your data? Do you understand how it has moved through your systems and where and when it is being used?
- Data monitoring. Does every pipeline operate 24/7? Is the pipeline transmitting all the data you need, not just 90%?
- Notifications. Does your data tell you immediately when something goes wrong, or does it wait until you review a report or run a diagnostic — at which point, it is already too late? And when you do receive a notification, is it complete and relevant enough to point you to the right solution?
“Observability” means that DataOps can watch the data infrastructure, the flow of data and the data itself. Then, when there is an issue, automated alerts notify DataOps or the data engineers so they can resolve the problem — or at the very least, pause the people and programs that are trusting that untrustworthy data.
Bringing Data Observability to Life
How do you ensure that your organization has data observability? As with everything in data, there is no easy, one-size-fits-all solution.
For smaller organizations and startups with a modern, modular data tech stack, the answer will probably be to add on a purpose-built data observability solution. Several software startups have popped up just in the past three to four years that position themselves explicitly as data observability tools, while some slightly older data catalog solutions offer data observability features.
But for larger companies with a more mature data tech stack, the solution may already be in your hands. If your comprehensive data platform offers functionality for data inventory and console management, you can implement a data observability initiative by prioritizing the components described above.
So, is data observability just another trend? In a word, no. A world without data observability is a world where, by the time you discover that your data is wrong, it’s already too late. This is an opportunity for DataOps to lead the charge and improve the health — and value — of data for the entire business.