Data science startups focus on AI-enabled efficiency

Every five years, we invent a new technology that, when sprinkled atop existing business problems, acts as a panacea for managers. In the ‘90s it was the Web, followed quickly by SaaS, mobility, clouds, data, and now AI.

But there’s a bigger underlying pattern. Web and SaaS gave us interfaces anyone could use. Mobility made them ubiquitous, taking us away from the workday—we check our phones dozens of times a day, and, often, they’re the last thing we look at before sleep and the first thing we grab upon waking. Clouds gave us elastic, on-demand computing. Big data gave clouds something to do. And AI is a set of algorithms that make sense of that big data, teasing threads of gold from the digital hay.

Take, for example, the winners of the Strata San Jose Startup Showcase. From a field of applicants, our panel winnowed the list down to 10 finalists, and then a combination of on-site judges and audience voting helped us pick the winners:

• The first-place winner, Nexla, makes data wrangling easier with a product that allows teams to share, automatically process, transform, and monitor data. Over 80% of time is spent getting data sources to cooperate: cleaning them, moving them around, and ensuring they’re consistent and available as companies build data products atop them.
• Second-place winner Gluent tries to unify the many rivers of data in an organization, pulling them into a single, central, agnostic repository. One truism of AI is that data beats algorithms—so the more data you have at your disposal, the better the models you create and the greater your ability to test them properly, avoiding perils of overfitting and bias.
• Third-place winner Repable uses the torrent of data that gamers generate to understand publisher, brand, and player trends. E-sports is a huge industry, and any early insights into what’s happening are invaluable. But gamers are notoriously fickle, and they engage with their audiences across a wide range of platforms. So, pulling this information together and normalizing it is a huge challenge.
• The popular vote winner, Outlier, harnesses existing business information and looks for anomalies. Most business managers don’t worry about what’s expected; they care about what’s not. Sudden growth, overnight churn, or a rash of alerts often point to something urgent. But patterns in data can often reveal hidden advantages, if the organization has the time to mine it.

These four companies underscore the unbroken link between on-demand computing, big data, and machine learning. While the ‘90s and “oughties” were about building up the front-end user interface—and in the process, making powerful technology simple enough to find billions of users—more recent years have been about laying the groundwork for adaptive, always-aware organizations.

Full disclosure: Alistair Croll is a board member of Repable; he recused himself from the voting and selection process involving the company during the showcase.