Meaningful data tells a story. It helps identify critical business issues and empowers companies to make better, smarter decisions. There’s just one challenge: finding the storyline.
Enter our latest investment, Resultid: the platform bringing greater data access, transparency and analysis to technology and other businesses in the same way that Bloomberg did for financial services. Resultid’s platform organizes and analyzes data around clear narratives which, in turn, create direction for Research & Development (R&D) strategy, generate actionable results, alleviate corporate costs and unburden researchers. This saves decades (yes, decades) of time spent on manual R&D and enriches the value of the data itself. Think of Resultid like Sparknotes for critical decision making.
Why R&D, specifically? Research & Development is an integral function for success, particularly in sectors like biotech, deep tech and pharma. It’s how these companies identify which opportunities to pursue, as well as create differentiated, defensible products and services. For many companies, it’s a primary value source. Today, however, conducting R&D is often tedious and inefficient; according to McKinsey: “In 2019, organizations around the world spent $2.3 trillion on R&D — the equivalent of 2 percent of global GDP.” In fact, 70% of companies don’t analyze their own data or insights. The effort is large, siloed and relies on manual analysis conducted by costly specialized labor (e.g., data scientists, PhDs, etc.). These researchers are burdened by the expectations of executives eager to identify new investment or product opportunities, while executives struggle to justify increasing costs without validation of clear results, stifling tension at the highest level.
Here’s How It Works
Big data has become too much data, and the world is looking for ways to understand it. Resultid is a proprietary business intelligence platform that leverages machine learning and natural language processing (NLP) to accelerate and optimize the entire R&D process. The platform is built around a proprietary API that enables aggregation of disparate and unstructured data sets. Once the data is collected from internal and external resources, Resultid runs a series of processes to analyze the data for insights. The system takes into account company-specific data, while also consolidating data sources across the entire market landscape. Collectively, this enables smarter, more insightful investments and sharper, faster decision making. Says Aditya: “It’s not about building better conclusions for our users. It’s about giving them the tools to build better conclusions for themselves.”
“It’s always been about the people,”— Kevin
These are the words co-founder Aditya Badve returns to when he talks about building Resultid. Not surprisingly, his co-founders, Kevin Magee and Sifron Benjamin agree. Aditya and Sifron met in 2013 on their second day of undergrad at University of North Carolina and have remained best friends ever since. Given Aditya’s background in quantitative finance and biomedical engineering, and Sifron’s in computer science and entrepreneurship, the two decided they would build something when the right opportunity came along
When Aditya and Sifron graduated in 2017, none other than their future Resultid co-founder Kevin Magee was sitting in the audience, watching his own daughter receive her degree. Four months later, the three would actually meet at a startup exhibition, where Kevin expressed interest in an earlier business that Aditya and Sifron were building at the time (a crowdfunding platform). After multiple coffees and hours of conversation, Aditya and Kevin realized a mutually strong connection in their shared ideas and desire to collaborate — and no, it wasn’t just the caffeine talking. “Kevin came in and challenged us to think even bigger. We really appreciated his candor and straightforward approach,” Aditya shares.
The first iteration of Resultid was an automated data pulling tool that essentially digitized the market research process, sifting, sourcing and organizing data. What the Resultid team realized as they were building was that while pulling that data, they could also map it, connect it and create a narrative from it that would allow companies to make faster, smarter decisions, setting a new precedent for the entire R&D process.
In 2019, the team felt ready to gauge market interest, exhibiting at a regional conference of tech managers; they closed two sales on the first day, giving them validation to keep up the momentum. In 2020, the team was accepted to Techstars Boston, where they doubled down on enhancing the Resultid platform and building out their network. The following summer was spent understanding what it means to be a venture-backable startup and create a product at scale, building on their model and looking ahead.
The Future of R&D
McKinsey has stated that “the next horizon [in R&D] is to generate the serendipity of chance encounters that are the hallmark of so many innovations.”
Coincidentally, it’s that same serendipity which brought the Resultid team together and created the opportunity to build something big: the R&D platform that enables powerful connections between disparate data, brings them to light, and tells a clear story.We can’t wait for the next chapter.
Written by Soraya Darabi