It’s a perpetual problem: business professionals are virtually drowning in data yet starved for insights.
Or as Davis Clark puts it, over “the past 20 years, we’ve made extraordinary progress in our ability to collect, store, extract data, but relatively little progress in our ability to integrate and make sense of that data.”
The problem isn’t new: how do we enable the people who need data to make decisions within their area of expertise—finance, operations, sales, marketing, supply chain, whatever—but who aren’t experts in data analytics, to use data effectively?
Davis isn’t the first to try to answer the question, but he is approaching it from a unique perspective. Here’s his story, and the story of the company he founded to solve this conundrum, Futuremodel.
Tom: Hello everybody, and welcome to another Founders Interview on Webbiquity. Today, I am joined by Davis Clark, founder and CEO at Futuremodel. Hi Davis, how are you doing today?
Davis: Doing great. How are you, Tom?
Davis: It was awesome. I grew up in the Minneapolis / Twin Cities area. I went to school at the University of Minnesota, but shortly thereafter, I moved out to the Bay Area, and I was out there for about 10 years. I just recently moved back.
It’s been really exciting to kind of come full circle and get plugged in and involved with the Twin Cities Startup community. The energy of BETA was amazing, and the collaborative feel among all the founders has been a really nice change of pace. So I’ve been enjoying it all.
As Davis puts it, “We’re in the midst of a data-driven dark age.” He points out that over the past 20 years, we’ve made extraordinary progress in our ability to collect, store, extract data, but relatively little progress in our ability to integrate and make sense of it all.
As data and applications have moved to the cloud, our ability to access external data sources outside of the enterprise has expanded—but so have the difficulties brought by the lack of progress in analysis.
Davis could see this impacting both our communities—in the sense that we have more open data than ever before but no unified interface for exploring, consuming, and sharing our insights on that—and at the enterprise level, where it still takes 18 months and millions of dollars in order to implement an enterprise-wide business intelligence (BI) system. And the moment the business changes, they still break down.
Both areas are negatively impacted by the lack of technical analytical progress. “At the core of the problem,” per Davis, “all modern data technologies operate on an extremely reduced fraction of human knowledge. They can’t reason over elements like events, cause and effect, or processes.” The fact that somebody believes or asserts something which is not true does not make it true.
“If we’re going to try to reach a new level, if our world wants to progress past this and to enable smarter communities and smarter organizations, it’s extremely important that we’re able to build systems that operate at a level closer to human cognition and not just data.”
It’s focused on what Davis calls, “the first and last miles of the BI pipeline,” which are the starting point where users elicit their conception of the world, which gets translated into requirements that a team then goes off and builds; and the other end, where that team delivers to those users data products that actually allow them to explore and make those decisions about their world.
What sets Futuremodel apart from alternative data analysis technologies is its hyper-focused view of both ends of the BI pipeline through a graphical interface that lets users define the complex objects, events, and processes that make up their world, and then automates the entire end-to-end querying and data integration.
It then uses that same model to reflect back to that user the reality they defined, but streamed through their data. So now, “They’re exploring their data through the same intuitive organizational structures they use to think about their world every day,” per Davis.
The larger the organization, the more difficult and complex data analysis becomes. Futuremodel is designed to serve organizations of any type—B2B, B2C, government, and nonprofits—with more than 100 employees.
According to Davis, “It’s at that point organizations start to move beyond hiring generalists to hiring domain specialists, experts who can bring a whole new level of understanding to their individual functions. And it’s at that point that communication between groups begins to break down, making it all the more essential to have a unified system.”
The product appeals to business and analytics managers who are managing teams and making data-driven decisions; for example, a sales executive designing compensation programs for salespeople based on the incentives analyzed from prior years that have influenced behaviors that weren’t expected, or operations people trying to re-engineer the organization’s value delivery processes.
Or in public safety applications. “There are some really interesting problems going on in 911 dispatch right now both nationally and at the Minnesota level,” adds Davis.
Year founded: 2019
Funding rounds: Self-funded for the first year and a half; raised first pre-seed round in May 2021. Seed round planned for early 2022.
Company size: Seven employees
Tom: What inspired you to work on this particular problem?
Davis: Originally, when I was in school, I wanted to join the CIA, that was my dream. I was studying Arabic, and my dream had been to walk across Afghanistan on foot.
Around this time I was reading and article in the Wall Street Journal on the Afghan poppy harvest relative to the Taliban’s fighting season. And I remember the journal quoting just a bunch of numbers. And I thought, “I don’t remember what the number was from last year, or the trends here, or how this relates. This is really interesting stuff, but where can I access this data? How can I just begin exploring this?”
I discovered that there’s nothing out there to do this sort of thing. So, that’s what got me into both data visualization and the startup world, and I just fell in love with both. Over the years—I have a refined sense of outrage—there have been moments where I just feel like something’s wrong, and that drives me to begin really exploring around that.
Over time, I made my way through the data engineering and data science side of things. I started building data products for business leaders. And I found that the tools I was given at these different organizations never allowed me to build the experiences that I needed to deliver to the end users.
Then in 2018, I was at a solar company, and I was directing their end-to-end enterprise BI system. It was a brand-new system, and we were implementing Looker on GECP.
We put a ton of time and effort into trying to create something that the whole business could use together, an experience that everybody could understand that was intuitive from the start. And by the end of it, the Looker team said they had never seen a more flexible system in the field.
It was pretty clear at that point that we were onto something, that these ideas were important. And that’s when I left to found the business.
Tom: What do you see as the most promising mediums or channels for you to get the word out once you’re really ready to roll with us and get it out into the market?
Davis: An entrepreneur typically has some really big ambitious ideas for where their company can go. But you can’t just come out with a radical paradigm shift and expect it to be taken up. We know we need to have a much more focused beginning.
So for us, it’s about how we can leverage existing networks, communities of users of different products, and come out with a limited, carefully scoped version of our platform that could fix some of the failings and shortcomings in that specific product. That allows us to approach these markets and say, “Hey, we don’t want to pull you off your system, we’re just trying to maximize the value you’re getting from it.”
That’s been highly valuable is that it both gives us the ability to easily access these types of users—whether it’s on LinkedIn posts or in existing forums where users are talking about how to solve certain problems in a given technology—and deliver a really natural pitch, a value proposition around making use of our platform to maximize the impact of what they’re already doing with what they have. And so we plan on doing much more of that.
We focused first on that Looker market, but we plan on moving out from there to focusing on some of the more ancillary communities.
Tom: Right. And a benefit of going to the forums like that is you’re simultaneously educating the market and learning from the market in terms of what will be useful to them.
Davis: Exactly. We’ve found the forums to be gold mines of information for these exact purposes.
Tom: If you would, finish this sentence. “Knowing what I know now, if I were starting over today, what I would do differently is…”
Davis: Trying to quit smoking cigarettes prior to founding the company because that’s not going to happen when you’re in the thick of it!
No, but seriously, it’s a hard question for me to answer because, for a long time, I’ve adopted a mentality which is to fully embrace my failures but operate from the standpoint of doing the best I can in that moment with what information I have, and then constantly learning and evolving.
What inspired me to start this was 10 years of growth, of personal growth, of technical growth. So if I were to start over, I guess the only thing I’d do differently is I’d be starting with an extra two and a half years of some of the most rapid growth of my life.
Tom: Excellent. Thanks, Davis, this has been a great discussion. Final question, where can people connect with you and learn more about Futuremodel?
Davis: We’d love for people to go to our website, futuremodel.io. Sign up on our waiting list for the citizen experience, which is going to be the next release we’ll have in early 2022.
Follow us on LinkedIn. We have a lot of exciting news coming out in the next month. And we’re really just hoping to spark a conversation with communities about what sorts of data and data problems, what sorts of social issues are most pressing and top of mind, and really begin to release products that serve what the community cares about and values most. We’d love for you to be involved and engaged in that conversation. We can’t do this alone.
Tom: Got it. Fantastic. Thanks, Davis. Have a great rest of the week.
Davis: Thank you so much, Tom. You too. And we’ll talk to you soon.