5 Ways to Transform DataOps With Human-in-the-Loop Automation – DevOps.com

We are in the middle of a data renaissance. Today, its not just about data instrumentation but also learning how to make DataOps a real business advantage for the entire organization. Data has become an inextricable part of products, used to enhance the quality of user experiences. Reliable access to data and the integrity of that data is imperative to drive innovation and business success.

But just like any process, problems will arise. There will be pipelines that break or data that isnt instrumented properly. The question then becomes, how can teams more quickly identify problems and continually improve? How can the journey toward data integrity become one of iteration and improvement?

DataOps teams are by no means strangers to automationautomated CI/CD pipelines and infrastructure-as-code (IaC) are a huge part of their skill set. But there are some components of automation that are less pervasive throughout DataOps, ones that can help promote data integrity and encourage a culture of improvement.

It all starts with a different approach to automationa human-in-the-loop (HITL) approach. Human-in-the-loop automation enables parts of a process to be fully automated while enabling humans to step in at critical points to take action, make decisions and decide the path forward.

Here are five ways to use human-in-the-loop automation to drive continuous improvement and achieve optimal data integrity.

As is life, inevitably things break, issues arise and teams need to step in to remediate. The goal should be to enable better interruption of these problems when they do occuridentifying issues earlier and bringing the right stakeholders together more easily and swiftly.

HITL automation can help teams not just identify issues earlier but kickstart the incident response and coordinate across teams and stakeholders. Ideally, automated workflows can be triggered from an incoming signal; for instance, from an observability tool like DataDog or BigPanda. An incident response could be started automatically, triggering further automationlike creating tickets, starting a Zoom meeting, creating a Slack channel and bringing together the right team members.

By instantly bringing the right people together, the team can investigate collaboratively and more quickly understand the state of the issue. HITL automation should then enable teams to run further scripts to take action and remediate as needed.

Many companies today are asking, How do we make sure that we have that full integrated loop, down to the collection point? Communication between teams is critical to this endeavor. But even before teams can collaborate on improvements, they need to understand the current state of their data so that if, for instance, a team finds that a report isnt providing whats needed, theres a really clean way to get that communication back to the DataOps team to improve the instrumentation in the first place.

HITL automation can bake improvement into DataOps workflows by automatically documenting every human and machine action throughout a process. Because its connected through APIs to all a teams tools and services, a self-documenting workflow slurps up every action and creates an audit trail. This audit trail is the basis by which teams can more accurately understand not just the integrity of their data, but also how teams are implementing and running processes.

DataOps teams play a big role in making sure the right data is being collected. However, a lot of people are still using systems that are very manual. They may have a good understanding of their log and monitoring data but still dont have a good sense of the human data around their organizationwhat processes and workflows people are running.

Understanding how humans run processes is just as important as understanding technology problems. The self-documenting workflow described above should include documentation of human data, from manually-run scripts to Slack or Teams conversations and, ideally, even auto-update tickets so even maintaining a system of record becomes less manual. Only by looking at the full pictureof human and machine data togethercan teams find areas of improvement in their automation, communication and workflows.

Dont let perfect be the enemy of done. Every great transformation Ive seen started with a small team, a great mandate, and strong support from the executive team. Adding tons of new technology and hiring more people may not be the silver bullet teams need.

Instead, teams should look at the expertise they have in-house firstthe people, technology and processes. Then, begin by taking an incremental approach. HITL promotes incremental, approachable automation by enabling teams to first codify institutional knowledge, analyze, learn and then begin to automate pieces in small batches.

Diving head-first into a fully automated approach can leave teams in a worse state than nothing at all. Automation is a journeywe learn by doing, by recording human and machine data and then, using insights as our guide, begin to find real value in small doses of automation. These small steps can make a huge impact and, over time, add up to substantial business value.

As I mentioned above, teams should first start by evaluating their current resources. What skill sets and technology do they already have and how can they use those resources to achieve their data goals? At the same time, many teams starting their automation journey face challengeslack of development resources, scripts that live on one persons machine, difficulty bringing all the human and machine data together into a recorded timeline.

To help data teams effectively and reliably implement HITL automation, investment into an off-the-shelf platform can help bridge the gaps teams and organizations have. Platforms should enable automation through low-code interfaces while providing code-level customization. This combination enables teams to buy for industry standards while building for the gapsnot managing the platform means development resources can be focused on expanding and improving automation.

Platforms should enable teams to easily connect to APIs and adapt to custom APIs. The continuous change of DevOps processes and tooling coexist with rapidly changing APIs. Therefore, the most promising automation platforms must manage the complexity of APIs, enabling users to focus on the intent, not the mechanics of custom APIs. Automation technologies should seamlessly handle identity, authentication, pagination and caching.

As data becomes increasingly ingrained in product life cycles and user experience, automation holds the key to faster innovation, product quality and enhanced reliability. Creating a culture of continuous improvement and implementing technology like HITL automation that promotes data integrity throughout the product life cycle is where Ive seen the greatest leverage for organizations.

To hear more about cloud-native topics, join theCloud Native Computing Foundationand the cloud-native community atKubeCon+CloudNativeCon North America 2022 October 24-28, 2022.

Read more here:

5 Ways to Transform DataOps With Human-in-the-Loop Automation - DevOps.com