UPDATED 12:15 EDT / DECEMBER 16 2021

AI

Machine learning algorithms help orgs maintain error-free data

Companies are stockpiling swathes of data, the likes of which haven’t been seen before. Code is required to visualize and analyze the data for insights, or alternately, feed them into applications and other related use cases.

Since no single person or dev team is above mistakes, FirstEigen LLC hopes to help organizations keep this very essential property (data) free of potentially damaging errors.

“What we have done is created the software DataBuck that autonomously looks at every data set to discover inconspicuous errors,” said Seth Rao (pictured), co-founder and chief executive officer of FirstEigen. “Since they’re hard to find out or detect, our machine learning algorithms figure out those errors before those errors impact the business.”

Rao spoke with Lisa Martin, host of theCUBE, SiliconANGLE Media’s livestreaming studio, during AWS re:Invent. They discussed the nagging industry problem that FirstEigen and its DataBuck solution seek to remedy. (* Disclosure below.)

Solving a nagging industry problem

While human ingenuity is irreplaceable, we can sometimes miss some of the finer details. In a field like computing, these problems can be expensive and time-consuming to solve.

“Companies can take a process that takes man-years or even man-months and compress it into just a few hours’ worth of work,” Rao explained.

Helmed by founders with extensive experience in fields like artificial intelligence and machine learning, the very idea for FirstEigen’s conception was eliminating the critical errors that make it through to an organization’s public release pipeline.

“Over the summer, a very big-name Danish bank had to send apology letters to its customers as they overcharged them on the mortgage, because the data in the backend had some errors in it and didn’t realize it was inadvertent,” Rao stated.

Given the fact that most companies and organizations today are data-driven, with a myriad of tools to analyze, mine and visualize this data, it has to be validated at the point of entry, according to Rao.

Watch the complete video interview below, and be sure to check out more of SiliconANGLE’s and theCUBE’s coverage of AWS re:Invent. (* Disclosure: FirstEigen LLC sponsored this segment of theCUBE. Neither FirstEigen nor other sponsors have editorial control over content on theCUBE or SiliconANGLE.)

Photo: SiliconANGLE

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