Data errors have a big impact on the CIO decision-making process. If you face a situation wherein analytics and dashboards are inaccurate, you may not be able to get the true picture & thus you may be unable to solve problems and pursue opportunities. Data errors may have big implications, If you have been in the data profession or in a CISO position for any length of time, you know how difficult it could get to face a mob of stakeholders who are unhappy about inaccurate or late analytics.
In today’s organizations of medium to large sizes, thousands of things have to happen accurately to deliver perfect analytic insights. Data sources will have to deliver error-free data on time. Schema changes will have to work perfectly. Servers and toolchains must work flawlessly. If anyone or some of the things out of the thousands of things goes off the rails, the analytics are badly impacted and the analytics team is on the hook.
Data observability & monitoring in DataOps
Some DataOps best practices and industry discussion around errors have given rise to a term called “data observability.” In simple words, observability is the ease with which one can find out the state of a system by looking at its outputs. But in today’s modern IT industry, many use the term “observability” to refer to the ability to go to the root cause of a problem. Observability is a system’s characteristic. Therefore, a more “observable” system will let you to more easily reach the source of a problem.
Many argue that “observability” is nothing but testing and monitoring applications through tests, metrics, and logs. It’s a largely descent point because it emphasizes on what is important – it underlines the best practices that data teams should employ for “observability” of data analytics. We at ISmile Technologies see data observability as a DataOps component. Therefore, for data observability, we focus on the key challenges of eliminating data errors.
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Applying DataOps Principles to Data Observability
Our suggestions to CIOs is that the approach to data observability should be grounded in the DataOps methodology. In our talk about winning the war against data and analytics errors, let’s go through some fundamental DataOps principles in relation to data observability.
Avoid manual tests
Manual testing is performed step-by-step manually by the test engineer. We need to keep in mind that manual testing can create bottlenecks in our process flow. It also tends to be expensive as it requires that some environment is created in which tests are run manually one at a time. But we need to understand that these tests can be prone to human error. Therefore, automated testing is a major pillar of the DataOps, and is highly recommended for eliminating errors.
Tie tests to alerts
As & when something goes wrong, the data engineers need to know about its happening as early as possible to ensure that these errors don’t reach end users or the customers or business partners. For this, you will have to create real-time alerts, and tie your testing and monitoring mechanisms to these alerts. If you have an automated process in place that can catch errors early in the process gives the data team a lot of time to deal with the problem & resolve it by patching the data, contacting data suppliers, and rerunning the processing steps.
Focus on the process
Today majority of the problems are “common cause variation,” and in order to lessen this variation, one will have to focus on the processes, and not find out a person to blame. A relentless process focus can lead to dramatically high levels of productivity and quality. The principles of DevOps (lean applied to software development) has enabled many software development companies to do millions of software releases in a short span of time.
ISmile Technologies helps you to optimize your data to be trust-worthy & business-ready with our DataOps managed services. We provide seamless DataOps managed services for your entire data pipeline. Our scalable & multi-cloud solutions help businesses to accelerate their journey to automation & improve data quality to enable far-reaching business potential.
