“People don’t necessarily consume data in the same way. NLG becomes critical because the analyst can scale their expertise.” – Adam Smith, COO

Last week, I sat down with our CEO, Marc Zionts, who shared with me the mission and vision of Automated Insights. Continuing our three-part series of what makes Automated Insights the industry leader in natural language generation, this week I spoke with our COO, Adam Smith, about our market approach and position. As COO, Smith oversees ongoing business operations within Automated Insights, including our Wordsmith platform, partnerships, and implementations in addition to product ideation and go-to-market strategy.

Q: Tell me about the story of Automated Insights. You’ve been at the company the longest—eight years now. What was the move into business intelligence like?

A:  When we started the company in 2007 our thought was, “How do you democratize data? How do you make data more actionable?” We started in sports when we noticed a lot of the technologies were just visual. You can do a lot with that, but if you really want to dig into a player’s performance acutely and over time then you want to actually write it out. So we threw ourselves at that and built a technology that allowed you to not only spot insights within the data but also communicate them in a way someone can easily digest—the written word.

From there, we’ve done tens of thousands of stories with the Associated Press, helping them scale reporting in a way they never could have before. We’ve worked with companies like Yahoo! to tell personalized stories to every single one of their fantasy football players, millions of people a week. But where we got really excited is telling a personalized story to an individual user in an actionable way, and that’s the core focus of the company that hasn’t changed over 10 years. We now have a self-service platform that lets other people do it. We’re the only technology of its kind that allows a data-focused person to scale themselves to tell that personalized, customized story to every person who may be looking at a dashboard or using data. That’s been our mission all along, it’s just looked different in each step.

Q: Why is NLG so critical right now?

A: Business intelligence has really come of age over the last 10 years or so. It’s always been about reporting technology, but the problem is that it’s been limited to your data analytics team. It hadn’t quite permeated to every level of the enterprise. That’s why you end up with analysts spending a good bit of their time writing reports about the dashboard they just created, setting up a meeting to walk someone through a dashboard, or putting it into powerpoint, emails, PDF, etc. NLG becomes critical because the analyst can now scale their expertise. They can write what they know about that data and what’s critical for specific users into a technology that then communicates it on a regular basis to every single group within the company in a role-based way. Whether you’re an executive, a busy manager who’s on the road without time to dive into a dashboard or even a less data literate user intimidated by data, NLG allows that analyst to expand the story to every layer of an organization and tell people what exactly it means and what to do about it.

Q: Is there a common thread that runs through all of our customers?

Adam Smith
A: Our customers are innovators. They’re forward thinking and they care about their data. They’re trying to create an organization that’s powered by data with individual employees who are improving their performance and acting on that data in an educated way. As you expand within the enterprise, you’ll find that employees have varying levels of data literacy. As a result, they don’t always act on the data in a way that you would hope, whether that’s in speed to insights or the step you’d like them to take. Our customers are all looking to make the most of their BI investment. They’re already utilizing a business intelligence platform, but are looking to go further—to reach every layer of their organization and tell a story that enables each one of those employees to perform better, to be more knowledgeable about what they’re doing, and to understand what’s actually going on with their performance metrics.

Q: Talk to me about our work with partners.

A: For years we’ve been working to evangelize NLG, growing it as a technology. There wasn’t a space before we started. At the same time, there’s a bunch of other organizations trying to do similar things—allow people to tell stories with their data that give customers an edge and help them understand and act on data in a better way. We don’t view NLG as a replacement for visualizations, charts, or dashboards. We view them as complimentary; they shouldn’t solely say the same thing. If you look at a dashboard and the text is just a replication of what the dashboard portrays, it’s pretty much useless. We’re looking to provide context, to enable someone to look across the entire dashboards and talk about what those visualizations mean together and what they mean for the specific viewer. There’s a large base of customers who already use business intelligence tools and are wanting to leverage it fully to empower employees to perform better, so it’s a natural place for us to start to work with these platforms and integrate NLG to make the tools even more powerful.

We’re also working with systems integrator partners, both globally and nationally, that are working in the business intelligence industry to help people work on complex projects and dive into building big, complex dashboards and establish reporting for enterprises. We give them another tool to communicate more clearly at scale.

Q: Great. To wrap it up, what are your thoughts on how 2019 will look for the industry?

A: I’m very excited about 2019, probably as excited as I’ve ever been in the history of the company and I think the reason is that natural language generation has taken another step. There’s a focus on NLG technology that didn’t exist before, partially because the technology wasn’t there, but more importantly, I think because people are realizing they can utilize natural language. They can utilize natural language processing to structure data and try to understand it, they can utilize natural language query to ask questions, and they can utilize natural language generation to tell a story about the data in a way people can make sense of and act upon.

Missed our first video in the series with our CEO, Marc Zionts? Check it out here.