Why DataOps Matter

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If you’re building a data product, these questions are likely occupying your mind: how do you get your customers to trust your data? How do you know your product’s something your customers will want? How do you produce those products more quickly without compromising accuracy? Today we talk with someone who has a lot of experience answering these questions. Ginette: If you’re building a data product, these questions are likely occupying your mind: how do you get your customers to trust your data? How do you know your product’s something your customers will want? How do you produce those products more quickly without compromising accuracy? Today we talk with someone who has a lot of experience answering these questions. Ginette: I’m Ginette. Curtis: And I’m Curtis. Ginette: And you are listening to Data Crunch. Curtis: A podcast about how data and prediction shape our world. Ginette: A Vault Analytics production. Curtis: If you’re a company aiming to research emerging technologies, like AI, ML, IoT, or edge computing, and you find your company lacking expertise, we know where you can the expertise to pad your research team: this team is a group of ex-fortune 500, b2b tech product managers with in-depth market analysis, product planning, and development expertise in bringing successful products, software, and services to the market, and they have significant in-depth technology skills on their team. They drive emerging tech research, product strategy, and tech marketing that resonates with customers, and they’re good at it. If a service like this would be helpful to you for a proposal you’re writing or a for a product that you’re creating, reach out to us at hello@vaultanalytics.com, and we’ll be in touch. Ginette: Now let’s jump into today’s episode. We’re talking with someone who’s worked with data teams for many years and has learned a thing or two. This is Chris Bergh. Chris: I’m Chris Bergh. I'm head chef of a company called Data Kitchen in Cambridge, Massachusetts, and we're a company that helps teams of people who do AI or machine learning or data engineering or data visualization deliver insight faster with higher-quality, and so how did I, how did I get to this point to found a company to focus on what we called dataops? Well, I guess I'm a working class kid from Wisconsin. I went to, in the late 80s actually, I went to Columbia to study AI back when AI was just a corner of the world that people, no one knew what it was, and you didn't walk through an airport and run into it, and then I worked on some AI systems at NASA and MIT to automate air traffic control, and then I sort of got into software development and managing software teams. Curtis: To fill out this picture a little more, Chris has two patents under his belt and has had two companies acquired, one by Microsoft, while he was building the company in the C-suite. So he’s no stranger to the difficult experiences that come with companies’ growing pains. Chris: About 10 years ago I got into data and analytics, and the company I worked for was about a 60 person company. We did everything that you could do in analytics, and we did data visualization. We had data scientists. We had data engineers. We even decided to build our own complete software platform that did everything in analytics, and I was the chief operating officer, and I worked with a guy who was from Harvard Medical School, really knew, it was a healthcare analytics company, really knew health care and really could talk to customers and figure out what they wanted, but then he'd come back to me and say, “Chris, here I've got this idea. Customer has this pain. Could you get some people together and figure out how to solve it, so I would go off and pull the data scientist and maybe data engineer and maybe someone who knew Tableau and maybe a software engineer in a room, and we’d talked it through. And I’d, I’d, you know,

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