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Forbes under 30 alumni secures $16.5M funding to make data analysis accessible to everyone

Enso co-Founders
Image credits: Enso

Enso, the San Fransisco-based startup making complex data analysis available to everyone is today performing a mainstream launch, supported by $16.5M in funding. The company’s primary product is an open-source platform that makes analysing complex data as simple as using Excel. The result is that non-technical users can autonomously access the insight they need while technical users can accelerate their workflows.

The startup boasts backers including SignalFire, Khosla Ventures, Day One Ventures, Decacorn Capital, Y Combinator, Samsung Next, Harvard’s Endowment, West Coast Endeavors, Innovation Nest, and more.

The problem with modern analytics

Data analysts and other business users are spending nearly 20% of their time doing repetitive work like updating spreadsheets whenever a dataset changes. This draws the focus from solving analytical problems, shifting it to focusing on how to augment data to fit their analysis inputs. A lot of time is wasted in adjusting spreadsheets where data lacks interactivity, meaning users can’t easily test their hypotheses while working. Furthermore, when trying to perform advanced analyses with large datasets, spreadsheets are prohibitively slow, needing to be manually updated every time a dataset input changes. Spreadsheets are also fragile, breaking with the slightest change in input data formatting.

Despite a workforce shortage, advanced spreadsheet analysis often requires data scientists to organize and prepare datasets. Businesses are generating more data than ever with enormous volumes of product usage analytics, market data, advertising metrics and user attributes, just to mention a few, which are stored in massive databases. The number of data engineers and data scientists entering the workforce is hardly keeping up with demand, with three times the number of job postings by businesses compared to searches by candidates for data science roles.

Granting everyone the key to complex data analytics

Enso enables both technical and non-technical users to build and automate data-driven processes by simply connecting visual components. The self-service tool is as powerful as programming languages, yet as easy to use as Excel. The platform works using components that process data and output results. Enso analyses the whole network of components, looks into the data and suggests the best next steps for users, allowing users to work on data, see live changes, understand it in real-time, and modify it by mapping visual components, as opposed to writing code.

Enso makes data analytics so accessible, businesses no longer need to spend extensive resources recruiting data scientists and dedicated engineers to support business data analytics. Data analysts and business users can directly run complex analytics processes while data scientists work more efficiently.

“Enso is alleviating much of the pressure on companies struggling to hire enough data scientists to keep up with today’s massive amounts of data,” remarked Sandhya Venkatachalam, Partner at Khosla Ventures. “It’s a game-changer for companies in the many industries that rely heavily on deriving insights from data for competitive advantage.”

Founded by a Forbes under 30 alumni

The company was co-founded by Wojciech Danilo, an award-winning engineer with more than a decade’s experience developing for the VFX space and Forbes 30 under 30 recipient who previously founded two other companies. His co-Founder, Sylwia Brodacka, is a physicist and computer scientist who previously designed rocket materials and implemented image processing libraries for VFX needs. The duo spent eight years working together before co-founding Enso.

“When my co-founder, Sylwia, and I were in our previous roles helping VFX artists process data, we were repeatedly asked by companies in other industries if it was possible to use our tools for their data,” explained Wojciech, co-founder, CEO, and CTO of Enso. “It became clear to us that there existed a severe pain point, largely driven by the shortage of data scientists.”

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