Data Wrangling in the Cloud - with Adam Wilson, CEO Trifacta

**Ray Rike** (0:00)
Hello, I'm Ray Reich, Founder and CEO of RevOp Squared, and your host of the Metrics that Measure Up podcast. We talked to a wide variety of B2B, SaaS, and Cloud thought leaders, executives, investors, and people just like you to discuss the metrics and benchmarks they use to make metrics-informed decisions. Now on to today's show. Welcome to today's episode of the Metrics That Measure Up podcast. Today, we are joined by Adam Wilson, CEO at Trifacta. Today with Adam, we'll be covering three main topics. First, the value of establishing thought leadership in your industry. Second, the five key strategies to creating that thought leadership. And third, Data Wrangling and Data Engineering Cloud, the what, the why, and the how. Adam, please take a moment to give us a brief background overview of your journey to becoming a guest on the Metrics Measure Up podcast.

**Adam Wilson** (1:07)
Awesome. Well, Ray, thanks for having me. It's great to be with everybody today. It's been a really exciting journey for me. Really, I've spent my entire career in data integration, data transformation, data cleansing. Most notably, before becoming CEO of Trifacta, I was in a variety of general management and leadership roles at data integration company that was an early pioneer in ETL, or extraction, transformation, and loading, a company called Informatica that just re-IPO'd yesterday. It's been a fun journey to see how this space has really grown up and exploded since the early days. For me, jumping into Trifacta was a chance to be part of what's new and what's next in creating a category around self-service data preparation, and around data engineering. That led me to think very deeply about thought leadership, and ultimately resulted in this article that was published recently in Medium, which connected the two of us. That's a little bit of how we got here.

**Ray Rike** (2:08)
Was, in fact, someone that you highlighted in that article is a very close colleague of mine and a previous guest on the show, and that's Dave kellogg. You highlighted him as a great example of establishing thought leadership in a particular category, which is SaaS Metrics, right?

**Adam Wilson** (2:23)
Yeah, absolutely. Dave is somebody who I have followed for years and just incredibly generous with his experiences, which are broad and deep, having both run marketing and also been a CEO, and been an investor, and an angel. He's really a man of many talents who has been just phenomenal in capturing a lot of the lessons learned from over the decades, and through his kellogg sharing that, but also at conferences and on podcasts like this. So he's somebody that I admire quite a bit, and I'm proud to call him a friend.

**Ray Rike** (2:56)
I followed Dave for many, many years also. And when I saw your article, I knew I had to reach out. And the first question I want to ask you about your Medium article is, before we talk about the five key things to establishing thought leadership, my first question is why? What's the value of establishing thought leadership, both as an individual in your industry, and also as a company? And I'm really asking, how do you measure the value?

**Adam Wilson** (3:22)
Yeah. Well, I think it was interesting for us, and I'll just give you maybe an anecdote from our own experience at Trifacta. We were starting out doing work in an area around how do you take raw data and to refine it. And there were a lot of different approaches that had come before, dating back well over a decade in terms of how people were doing this work. And I think the original research that created the company was born out of a PhD thesis that Sean Kandel did when he was at Stanford, but was also working in collaboration with professors at Berkeley as well. And their observation was that most of the time the cost, a lot of the pain frankly, was still in this part of the puzzle of how do you get eyes on raw data, refine it, and make it useful to someone so that you get nice clean rows and columns. And their hypothesis was that maybe the reason this is still so hard is because the people who know the data best can't do the work. This has become the exclusive purview of the highly technical. And people who understand databases and data models and know how to do structured programming are not always the best people to take the data and understand how it gets applied to make business decisions. And so for them, context really mattered. And so they started really thinking about, well, what would we do differently if we wanted to democratize this process and if we wanted to welcome more people into the exercise of engineering data products and into the exercise of preparing data for analysis? And they decided that they wanted to turn it into a user experience problem that would be powered by machine learning, where you could learn from the data and you could learn from how the user interacts with the data in order to automate a lot of the really complicated things and to make it possible for people who are data driven and data savvy but not necessarily structured programmers to do some of this work on their own.