Discover the untapped potential of DataOps

Is DataOps the same as DevOps? It’s not, and I’m going to explain why.

Hi, I’m Peter Nichol, Data Science CIO.

One of the challenges we see with development operations (“DevOps”) is that folks don’t understand what it means. The term is becoming more and more popular, so I thought I’d take a minute to explain it. DevOps is a lot different than DataOps. DataOps focuses on automation. It’s a process-oriented methodology that data-science teams use to simplify or streamline a data-science workflow, typically generating analytics. Where did the concept of DataOps originate?

It came from Edward Deming, an American engineer, statistician, professor, author, lecturer, and management consultant. He’s also referred to as “the father of quality.” In general, he focused on quality and production control and, more specifically, on the area of statistical process control. If we look at DataOps—specifically the role of DevOps—we start to understand what DataOps is all about.

Let’s break down what DataOps means. The foundation of DataOps has three main principles.

First, DevOps is focused on the delivery and development of some entity. Second, it’s based on agile—commonly applied to software development—and on the Theory of Constraints, which centers around removing obstacles and trying to take the shortest path to achieve an outcome. Third, DataOps is made up of lean manufacturing principles. Lean manufacturing emphasizes quality and production efficiency and uses statistical process control to monitor and control process variance or deviations.

Okay, now that you understand the foundation, where’s this going? When we start to think about value and how it comes into the picture, we can add a data factory or data-orchestration concept. The benefit of data operations is that it’s not only the outcome that we’re looking for—i.e., a validated analytical model with visuals—but we’re also making sure the data goes into the process correctly. We focus on the throughput as well—what’s happening with that data and information as they translate through the model.

Lastly, who’s using data operations? This is the big difference between DataOps and DevOps—the users. DevOps typically has engineering—software engineers that know multiple languages all dedicated to streamlining the orchestration of that technical delivery. In DataOps, we have other roles. We now add data scientists and typically data analysts that don’t care about all the technical-orchestration details. They want to make sure that their models can be simulated, executed, and that the data or the outcomes—and, ultimately, the insights—are usable and can be transformed into some benefit for their business users.

As you think about DataOps versus DevOps, reflect for a minute. There might be an opportunity in your environment to emphasize DataOps to orchestrate and streamline the workflow of how your team generates quality analytics for your business partners.

Hi, I’m Peter Nichol, Data Science CIO. Have a great day!

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Peter is a technology executive with 19 years of experience, dedicated to driving innovation, digital transformation, leadership, and data in business. He helps organizations connect strategy to execution to maximize company performance. He has been recognized for Digital Innovation by CIO 100, MIT Sloan, Computerworld, and the Project Management Institute. As Managing Director at OROCA Innovations, Peter leads the CXO advisory services practice, driving digital strategies. Peter was honored as an MIT Sloan CIO Leadership Award Finalist in 2015 and is a regular contributor to CIO.com on innovation. Peter has led businesses through complex changes, including the adoption of data-first approaches for portfolio management, lean six sigma for operational excellence, departmental transformations, process improvements, maximizing team performance, designing new IT operating models, digitizing platforms, leading large-scale mission-critical technology deployments, product management, agile methodologies, and building high-performance teams. As Chief Information Officer, Peter was responsible for Connecticut’s Health Insurance Exchange’s (HIX) industry-leading digital platform transforming consumerism and retail oriented services for the health insurance industry. Peter championed the Connecticut marketplace digital implementation with a transformational cloud-based SaaS platform and mobile application recognized as a 2014 PMI Project of the Year Award finalist, CIO 100, and awards for best digital services, API, and platform. He also received a lifetime achievement award for leadership and digital transformation, honored as a 2016 Computerworld Premier 100 IT Leader. Peter is the author of Learning Intelligence: Expand Thinking. Absorb Alternative. Unlock Possibilities (2017), which Marshall Goldsmith, author of the New York Times No. 1 bestseller Triggers, calls "a must-read for any leader wanting to compete in the innovation-powered landscape of today." Peter also authored The Power of Blockchain for Healthcare: How Blockchain Will Ignite The Future of Healthcare (2017), the first book to explore the vast opportunities for blockchain to transform the patient experience. Peter has a B.S. in C.I.S from Bentley University and an MBA from Quinnipiac University, where he graduated Summa Cum Laude. He earned his PMP® in 2001 and is a certified Six Sigma Master Black Belt, Masters in Business Relationship Management (MBRM) and Certified Scrum Master. As a Commercial Rated Aviation Pilot and Master Scuba Diver, Peter understands first hand, how to anticipate change and lead boldly.