Bryan Smith, ThinkData Works
To kick off the first Founders’ Scrum interview, I met with Bryan Smith, the Co-Founder and CEO of ThinkData Works. ThinkData Works is a Toronto-based startup focused on the aggregation and distribution of data. Their external data management platform Namara provides organizations with an end-to-end solution that enables them to identify and connect to any number of high value sources, neutralize variety issues, and access clean, updated data on demand. Prior to ThinkData, Bryan worked for the Canadian Federal Government as a Sr. Policy Advisor for the President of the Treasury Board of Canada. In this role, he helped launch Canada’s Open Data Movement, leading to the free release of hundreds of thousands of previously unavailable data sets from all departments government-wide.
LH: Tell me a little about how ThinkData Works got started?
BS: ThinkData sprouted out of a simple idea: People were publishing high value data online, but there was no common way to access it. We set out to fix that. The company was really born out of the idea of providing a platform for anybody to access open data in a standard, usable way. Over time, our vision evolved into a more complex set of products and tools that allow people to get the most out of that data and integrate it with their existing big data environments, but the principle of what we do has stayed constant; We’re making data more accessible.
LH: How did you validate the idea was worth pursuing before spending or raising significant capital? What signs should founders look for?
BS: We validated the idea by looking at the data and then asking the question, “why isn’t anyone using this stuff?”. When we started turning over rocks and getting our hands on the data itself, we realized it was super high value and useful information. But none of the organizations we thought would have a need for it were currently plugging into it. So we asked, why?Speaking with a seemingly endless number of developers and businesses, it became clear that accessing the data in a clean way and validating that the data is good were two huge hurdles holding back a wave of external data adoption. If we could solve those problems, then we had a product. That’s when we decided to quit our jobs and raise money.
LH: What did you do first after you got your initial funding?
BS: We started our countdown to zero. We knew we were on to something but we knew we didn’t have enough runway to fully get there. So we significantly scaled back our vision to get us an MVP (Minimum Viable Product) that someone would give us money to use. We figured once we had a paying client, we would get more and that would solve our cash issue. If anyone spends anytime in our office, they will hear us say that the best type of funding is from happy, paying clients.
LH: What was the process you used to scale back your product idea? How did you determine what features were to go and ones that were not viable?
BS: We actually have an image in the office that helps guide us through product and feature development. It’s a picture of a skateboard, then a scooter, then bike, then a motorcycle, and then a car. Rather than trying to build a “car” piece by piece and not having a usable product until the entire thing is built, we aimed to build a workable product at every stage of development. Our initial product didn’t have a tenth of the features our platform has to date, but it still worked. That allowed us to constantly sell and land clients basically since day one. We never had to wait until the product was done before we started selling; sales and development were symbiotic where client feedback and new development fueled one another.
LH: Who did you learn the most from as you were growing?
BS: We took queues from large companies who had sophisticated products and sales machines. Ultimately, we were heading into enterprise sales and needed to learn how to punch above our weight. We made sure that we had equivalent material and were capable of solving problems in an equivalent way. Then we relied on our agility to deliver a better experience than an average client could expect going with a traditional player in our market.
LH: Was there anything that was misunderstood about what you were trying to do?
BS: A lot of things. To this day we get VC’s saying, “but I don’t understand, it’s just public data so why don’t companies get it themselves?” In the early days, these sorts of comments definitely made us question our vision, but we were also lucky to find folks who did see the opportunity and value prop of our technology. It used to bug us a lot more than it does now. Understanding the value of external data and the problems associated with accessing it is getting more mainstream now. we always knew the wave was going to break eventually and our largest concern was whether we had good timing, or were a bit too early. Luckily we had the right partners to keep us going even in the early days.
LH: How did you continue to describe your product idea? What advice would you give startups whose product similarly pushes boundaries in the current space?
BS: Case Studies. Put yourself in your client’s shoes and ask yourself the hard questions like, “why do I actually need to spend money on this?”. A lot of companies are selling products that would be nice to have, but the necessity to purchase for the client is not articulated as well as it should be. By generating case studies, you are defining the problem for a potential client in a language they understand and allowing them to simply trace an ROI. This is key when you are dealing with large companies who have hundreds of opportunities to choose from.
LH: How did you start acquiring your customers?
BS: From a product perspective, we built a platform that solved a real problem, and it worked. From a sales perspective, there’s no tricks in enterprise sales, especially as a startup. You have to be prepared to kick down a hundred different doors within the same company and not take no for an answer. Some of our largest clients now are ones who ignored or barely entertained my constant stream of nagging. If you want to build a successful company, you have to be able to close. And that means having someone on your team who can read the room, align with a buyer beyond the sale, and get the job done. It’s not as easy as it sounds.
LH: Did you bring in a specialist for this? Or was it all hands on deck wearing multiple hats? When do you determine to keep a task in house, or hire a specialist to take a bit of the reigns?
BS: Ultimately, no one should be able to sell your product better than you can as a founder. So if you can’t get initial traction on your product, it makes more sense to reconsider your product and market before trying to hire a sr. sales person to come in a “fix” your BD pipeline. Regarding when to consider bringing in a sales person to run point on all BD, I think a reasonable time to start considering this is when your personal network and the network of your investors has dried up. That’s when a new person with a new Rolodex who knows how to sell can have the greatest impact.
LH: Did you have any apprehension from your larger customers since you were just starting out? If so, how did you navigate through this?
BS: I think every company feels apprehension when pitching large clients. In our world, alternatives are companies like IBM and the consulting firms who are pitching expensive service contracts for custom built solutions. These guys have the advantage of size, but startups have a lot of tools on their belts that they can use to seem like a more attractive fit, particularly speed and agility. We would break huge contracts down into tiny chunks that we could execute on in days rather than months and launch them as proof of concepts. We found once we started, it was expand.
LH: As you grow, how do you maintain this level of speed and agility of a startup, while catering to a larger client base? How are you scaling operations?
BS: This is something we are figuring out as we go. In our minds, it comes down to great communication between our product development team and our outward facing sales and communications channels. Our feedback loop is strong and we’re a super transparent organization. So everyone knows what are clients are asking for and the trends we’re seeing develop. All of our operations decisions are governed using this sort of client-centric approach.
LH: Given the enterprise focus of ThinkData, how did you get product feedback in the early days before you signed major clients?
BS: We spent most of our early days telling our clients what they needed to solve the problem. Transparent and standard access to external data enterprise wide is a new concept for a lot of organizations. If we asked them for what they wanted in the early days, they probably would have pointed towards traditional ways of siloing data. Now that we are deployed in a number of institutions, we seek constant feedback on feature and UI development. We get huge customer feedback engagement numbers and we love it. It makes our product better and makes our clients feel really engaged in the technology.
LH: How did you ask for feedback? Emails, face to face, online surveys? What do you think works best for major/larger clients?
BS: Really, all of the above. We reached out to everyone in every way we could. We found people were actually very interested in providing feedback once we started reaching out with a more personal touch. It was a bit more laborous than blasting out Mailchimp survey requests, but the result led to significantly higher levels of engagement.
