High-quality data is the key to a successful AI project, but it appears that many IT leaders aren’t taking the necessary steps to ensure data quality.
This is according to a new report from Hitachi Vantara, the State of Data Infrastructure Survey, which includes responses from 1,200 IT decision makers from 15 countries.
The report found that 37% of respondents said that data was their top concern, with 41% of U.S. respondents agreeing that “‘using high-quality data’ was the most common reason provided for why AI projects were successful both in the U.S. and globally.”
Hitachi Vantara also predicts that the amount of storage needed for data will increase by 122% by 2026, indicating that storing, managing, and tagging data is becoming more difficult.
Challenges are already presenting themselves, and 38% of respondents say data is available to them the majority of the time. Only 33% said that the majority of their AI outputs are accurate 80% said that the majority of their data is unstructured, which could make things even more difficult as data volumes increase, Hitachi Vantara explained.
Further, 47% don’t tag data for visualization, only 37% are working on enhancing training data quality, and 26% don’t review datasets for quality.
The company also found that security is a top priority, with 54% saying it’s their highest area of concern within their infrastructure. Seventy-four percent agree that a significant data loss would be catastrophic to operations, and 73% have concerns about hackers having access to AI-enhanced tools.
And finally, AI strategy isn’t factoring in sustainability concerns or ROI. Only 32% said that sustainability was a top priority and 30% said that they were prioritizing ROI of AI.
Sixty-one percent of large companies are developing general LLMs instead of smaller, specialized models that could consume 100 times less power.
“The adoption of AI depends very heavily on trust of users in the system and in the output. If your early experiences are tainted, it taints your future capabilities,” said Simon Ninan, senior vice president of business strategy at Hitachi Vantara. “Many people are jumping into AI without a defined strategy or outcome in mind because they don’t want to be left behind, but the success of AI depends on several key factors, including going into projects with clearly defined use cases and ROI targets. It also means investing in modern infrastructure that is better equipped at handling massive data sets in a way that prioritizes data resiliency and energy efficiency. In the long run, infrastructure built without sustainability in mind will likely need rebuilding to adhere to future sustainability regulations.
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