Tallinn-headquartered AI-native data analytics platform company Supersimple is promising its platform will enable any user to explore data and answer complex questions, regardless of technical expertise. The company says its platform exists because data analytics tools need reinvention for the age of Artificial Intelligence (AI) where insights come not just from upper-level dashboard views, but from deep specific questions and explorations – making that complex process available to non-technical users is where AI comes in.
Given the fact that Business Intelligence (BI) and data analytics tools today are a combination of static dashboards and ChatGPT-esque chat interfaces, Supersimple suggests that this amalgam of two technology worlds is ‘flat and uninspiring’. The company is betting big on a future where enterprise software in the age of AI doesn’t look like chatbots, where people are empowered with highly specific data insights, rather than a few high-level charts.
One person who likes the branding and technology on offer here is Tuomo Laine, CEO at Twice Commerce, a Supersimple customer. “What I love about Supersimple is that it lives up to its name,” enthused Laine. “We’re trying to help our employees and customers make better decisions, not configure charts and queries. Supersimple stands out by making it obvious how it came by the insights it’s sharing and making the user experience of double clicking on that as easy as ABC.”
Now focused on advancing the state of the art in explainable AI, the Supersimple team includes software developers, data scientists and product designers with track records in building deeply adopted technologies and startups. Co-founder and CTO Priit Haamer established Sixfold (a generative AI tool for insurance underwriters) where his fellow co-founder and CEO Marko Klopets also worked. The AI-based company merged with transport logistics company Transporeon. Most of Supersimple’s machine learning engineering team hails from Sixfold.
Semantic data modeling
The company says that its AI-native data analytics platform combines a semantic data modeling layer and an explainable AI element to give users reliable, consistent data. It was designed specifically for teams at B2B essential cloud-centric Software-as-a-Service companies to answer their own ad-hoc data questions. Its usage is supposed to allow even non-technical team members to go beyond pre-built dashboards for data insights.
“Most of the value in data doesn’t come from staring at a dashboard. Instead, it comes from deep, specific questions – testing hypotheses, exploring and iterating. Supersimple was built around this insight to help teams always have the right information to make decisions,” said Marko Klopets, co-founder and CEO at Supersimple. “Making structured data from data warehouses accessible and usable is the first step towards Supersimple becoming the operating system of the fastest growing companies.”
Although modern data stacks are better than they used to be, Klopets suggests that organizations are still only scratching the surface of being able to get the right information to the right people. Thanks to recent advances in data warehouses and Machine Learning (including but not limited to Large Language Models), it’s now possible to build new user experiences to detect and explain data insights and to let users explore with follow-up questions.
Forget floundering footnotes
Supersimple believes that purpose-built, complete platforms are required to offer the level of versatility and trust that’s required among enterprises and for mission-critical workflows. The company is also putting an emphasis on user experience and design in a space that has – arguably – traditionally made that part a footnote consideration.
“Over the last few years, data warehouses have become ubiquitous and data stacks are better than ever. Yet companies are still struggling to actually make use of their data,” said Eamonn Carey, partner at Tera Ventures. “When I heard [Klopets] explain his vision and I saw the platform in action, it became crystal clear that this is what the future would look like.”
The Supersimple data analytics platform gives users the ability to answer ad-hoc, complex data questions through its no-code and natural language interfaces without having to know or learn data query or programming technologies like SQL or Python, or think in terms of database concepts.
When asking questions in plain English, Supersimple’s AI explains every step it takes, which its makers helps it ‘eliminate’ hidden hallucinations, to make sure users can always trust its results. All this frees up the data team from fielding high volumes of data requests, allowing them to instead focus on company-wide strategic data initiatives.
Are modern BI dashboards grafting on AI chatbot functionalities and leaving this subsector of the industry in the uninspiring category that Supersimple suggests – well, not wholly, but certainly to some extent yes. Is the data analytics industry ripe for AI reinvention and the injection of these kinds of technologies – well, few would argue that it isn’t. Should Supersimple be using Aleksandr Orlov, an anthropomorphic Russian meerkat to feature in its advertising – too late, that one’s already gone – simples.