Analytics startup Unsupervised raises $35M to spot patterns in enterprise dataglobally

April 28, 2021
North America

Boulder, Colorado-based Unsupervised, a big data analytics company leveraging AI to find patterns in business data, today announced that it raised $35 million in a series B round led by Cathay Innovation and Signalfire. Unsupervised says that it intends to use the funding to hire additional employees as it continues to develop its platform.

Most enterprises have to wrangle countless data buckets, some of which inevitably become underused or forgotten. A Forrester survey found that between 60% and 73% of all data within corporations is never analyzed for insights or larger trends. The opportunity cost of this unused data is substantial, with a Veritas report pegging it at $3.3 trillion by 2020. That’s perhaps why the corporate sector has taken an interest in solutions that ingest, understand, organize, and act on digital content from multiple digital sources.

Unsupervised claims to accomplish this by analyzing unstructured and structured datasets to arrive at insights “without ignoring the long tail.” The company automates data science processes including preparation and prioritization, making predictions on data in industries spanning transportation, supply chain, ecommerce, and sales and marketing.

“We’re seeing a shift in the market where customers are seeking out analytics and AI platforms that don’t just do simple reporting — they reveal opportunities to change the business. BI and traditional AI is great for probing handfuls of known problems, but when you’re really trying to understand what’s happening you need to investigate beyond known issues,” CEO Noah Horton told VentureBeat via email. “This is where unsupervised learning is uniquely valuable. COVID really revealed the need for what we’ve built and this round will help us expand our footprint faster.”

Unsupervised says that its AI can identify statistically significant patterns that highlight the differences across subgroups within the data. Using a technique called unsupervised learning or self-supervised learning, Unsupervised’s systems can generate labels from data by exposing the relationships between the data’s parts. That’s as opposed to traditional, supervised AI systems, which require annotated datasets in order to learn patterns and make predictions.

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