Anodot’s Ira Cohen and the Importance of User Experience with Autonomous Analytics

For Ira Cohen, founder and chief data scientist of Anodot, timing is everything. Despite having 11 years of work at Hewlett Packard under his belt, he always knew that when the right idea and timing struck, he’d make his way into the startup world. The right timing turned out to be about four and a half years ago. The right idea? Anodot, an autonomous analytics company that uses machine learning techniques to equip business owners with the tools they need to learn and grow using data.

Machine learning was already a critical part of online life before Anodot’s inception. The science was responsible for everything from recommending a movie you might like on Netflix to helping you avoid a congested route via your Waze app. But as advancements in the field continued to make machine learning and data analysis even more complex, Cohen wanted to focus on creating a product that would do the heavy lifting for their customers.

Rather than devoting a team of data scientists to monitoring and analyzing ever-shifting data, a company can turn to Anodot’s autonomous analytics platform. The system relies on machine learning to provide business owners with a bigger look at the behavioral patterns, anomalies, and statistical outliers available within their data. Anodot’s algorithms are able to pinpoint the anomalies that matter faster than any human could and immediately alert the customer, helping businesses fix an issue before it becomes a bigger problem. The platform can also use real-time data to forecast future values, allowing customers to prepare for what’s ahead and avoid unexpected loss. Thanks to Anodot’s algorithms, business owners can focus on running their business with feedback from Anodot’s data analysis, rather than trying to figure out what to do with all that data themselves.

Cohen understood the importance of creating those stellar algorithms when he started Anodot. What he had to learn, though, was that those algorithms were only one part of the data package.

“The user experience around a machine learning–based product is as important as the actual machine learning algorithms and results,” says Cohen. “You can have the best algorithms in the world, but if you can’t serve the results to users in a way they understand, it’s meaningless. So a lot of the additional work that we have done as a company and around the product was actually to serve the results in a way that non-machine learning experts understand it, without having to know anything about machine learning. That’s actually as hard as the machine learning itself.”

Getting that user experience just right was not an easy journey, Cohen notes, especially since Anodot was an innovative startup without many competitors to model. He and his team spent weeks testing their product and tinkering with brand new ways to analyze and present data. They even completely scrapped weeks worth of work on the user interface when they realized too many mistakes had been made. But thanks to a lot of hard work, the company has recently launched a new service dedicated to forecasting.

“[There’s] no data scientists required, no development required, and this is really designed for business users that have no coding [skill]. They know their data, but they don’t know anything else about how to use tools,” Cohen says.

That emphasis on helping those customers without a data background is allowing Anodot to usher in a new era of machine learning and autonomous analytics that allows any business owner to get the most out of their data.