The Importance of DataOps and Data Engineering - Infostrux

Written by Goran Kimovski | Sep 9, 2021 10:53:55 PM

On August 31, 2021, Mike Murchison – CEO of Ada, joined us for a Fireside Chat. Ada is a no-code AI-powered platform that empowers brands worldwide to provide personalized experiences at scale. Recognized by Forbes 30 Under 30 and EY’s Entrepreneur of the Year program, Mike is also a Fellow at Creative Destruction Lab and volunteer for VentureKids, a program for Canada’s underserved youth.

Kima: We’re still on the ground level when it comes to data and engineering practices around data operation. The emerging concept of marrying agile and DevOps and applying them to data is called DataOps and the discipline of data engineering. I'm wondering, do you have a view of the importance of that and the success of difficult data science and data analytics projects?

Mike: We're in its infancy, and I think very few people know how to do it well, so the fact that Infostrux is providing data engineering as a service is needed right now. I think that’s a really important skill set to have in your organization.

I also think it’s changing very quickly. For us at Ada, there has been a big question around the idea that given a growing dataset and growing types of data we have, how we think about data engineering needs to evolve. It's put a premium for us on aligning the product roadmap and our vision for how our product is evolving to the types of data and the amount of data we expect to have over the years ahead.

It's almost a skill set around resource planning that I think is important to connect to your data engineering practice. It's a skillset, as you know, challenging to develop, which is why it’s great to work with partners who can support that, and it’s one that you probably can't be thinking about too early.

Kima: I found that in a previous business where we did cloud engineering, hiring software engineers and teaching them DevOps was a better strategy than hiring people who claim to be DevOps without experience in cloud or software engineering. So I assume we’re going to see the same thing happen in the data – training software engineers to work with the data would be the way to do it.

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