Everpure wants you to get your data AI-ready

"Data readiness" has quickly become one of the key challenges facing enterprises dabbling in AI innovation, according to research from IDC, and Everpure wants to help enterprises sharpen up their capabilities.

The flash storage and data management company confirmed the general availability of a new platform aimed at streamlining paths to production for AI, helping them prepare and refine unstructured data for use in AI applications.

Data Stream, which was first announced at Nvidia GTC, is based on the Nvidia AI Data Platform design and aims to reduce raw data preparation from "months to minutes".

Speaking to assembled media ahead of the event, Everpure's chief technology and growth officer, Rob Lee, said the solution fundamentally aims to make data "more usable" and cut down on what has traditionally been an extensive, manual process.

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"One of the problems that we're trying to solve is, how do we automate, how do we make just the whole process of enterprise customers tapping into their vast amounts of data and making that more usable, more easily plumbed into AI workflows," he said.

Lee noted that Data Stream will allow enterprises to connect "all their data sources" and data streams, with the platform thereafter automatically ingesting data, curating it, classifying it, and indexing it.

Data Stream will "basically automate the process, which today is altogether too manual".

Data readiness in the age of AI

The move by Everpure comes amidst a sharpened focus on data management for the firm, following a recent rebrand and acquisition of data intelligence firm 1Touch.

Everpure unveiled the Data Intelligence platform at its annual conference in Las Vegas. And, like Data Stream, it aims to simplify and streamline data management and bolster AI readiness for firms.

A common recurring hurdle for enterprises on this front has been data quality, per IDC research. Analysis from the consultancy found 94% of IT leaders identified this as "important or very important to AI project success".

Similar research from Riverbed also highlighted data quality as a major factor in AI project failures. In a poll last year, 87% of respondents said that high quality data is critical to AI success, yet more than two-thirds (69%) said they had serious doubts over how effective their firm is at maximizing the use of data.

The Everpure Data Intelligence problem addresses this challenge directly, as ITPro reported, helping enterprises to map data dependencies and therefore providing AI systems with relevant, contextualized data.

On a broader front, both Data Stream and the Data Intelligence platforms could play a vital role in helping enterprises push AI projects into full production – an area which many have struggled with in recent years.

Research from Dynatrace in January this year found roughly half of agentic AI projects were stuck in proof of concept (PoC) stages, with IT leaders failing to push initiatives into full production.