Hitachi Vantara survey finds data demands to triple by 2026, highlighting critical role of data infrastructure in AI success and revealing gaps in data governance, security, and sustainability
With the rapid adoption of AI across industries, nearly two in five (37%) U.S. companies identified data as their top concern when implementing AI projects, but few IT leaders are taking steps to ensure proper data quality and management, jeopardizing the success of AI initiatives according to a new survey from Hitachi Vantara, the data storage, infrastructure, and hybrid cloud management subsidiary of Hitachi, Ltd. (TSE: 6501). The Hitachi Vantara State of Data Infrastructure Survey reinforced the critical role that data infrastructure and data management can play in terms of overall data quality and the ability to drive positive AI outcomes.
“Using high-quality data” was the most common reason provided for why AI projects were successful both in the U.S. and globally, with 41% of U.S. respondents in agreement. However, AI has led to a dramatic increase in the amount of data storage that businesses require, with the amount of data expected to increase 122% by 2026. As a result, storing, managing and tagging data to ensure quality for use in AI models is getting harder.
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The company commissioned the global survey of 1,200 C-level executives and IT decision-makers across 15 countries, including 250 from the United States and Canada. The survey found that most businesses were focused on security risks at the expense of data quality, sustainability and infrastructure management. Key U.S. findings include:
- Highlighting the disconnect with proper data management, only 38% of respondents say that data is available when they need it the majority of the time. Even less (33%) say the majority of the outputs of their AI models are accurate, and three quarters (80%) say the majority of their data is unstructured, which poses greater risk as data volumes explode.
- Few are taking steps to improve their data quality: nearly half (47%) don’t tag data for visualization, only 37% are enhancing training data quality to explain AI outputs, and more than a quarter (26%) don’t review datasets for quality.
- Security is the top priority due to the risks presented; more than half (54%) cited security of data storage as the highest area of concern with their infrastructure, which was 17% higher than the global average (37%). Additionally, 74% acknowledge that a significant data loss could be catastrophic to their operations, while 73% of respondents are concerned that AI will provide hackers with enhanced tools.
- AI strategy is lacking ROI analysis or sustainability considerations, as only 32% ranked sustainability as a priority in AI implementation. Even fewer (30%) said they were prioritizing ROI.
- 61% of large organizations are focused on developing general, larger LLMs rather than smaller specialized models, despite large-scale models being much hungrier than regular models to train, consuming up to 100 times more 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, 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.”
Why Data Infrastructure is Key in Driving AI Success
Despite recognizing data quality as the top concern for successful AI (37%) many organizations lack the infrastructure to support consistent data quality standards. More than two-thirds (74%) are testing and iterating on AI in real-time without controlled environments, leaving room for significant risk and potential vulnerabilities. Only 3% report using sandboxes to contain AI experimentation, which raises concerns around the potential for security breaches and flawed data outputs. Modern infrastructure offers a solution, as it is designed to be more energy efficient, allowing organizations to improve performance while also reducing their carbon footprint. By adopting sustainable, cutting-edge infrastructure, businesses can enhance data quality, mitigate risks, and support environmentally responsible AI growth.
“Companies want to work with partners that help them grow, help them be more efficient or reduce and mitigate risk. We are addressing risk,” said Octavian Tanase, Chief Product Officer, Hitachi Vantara. “We are providing automation which translates into operational simplicity, so companies are more efficient. If companies get more insights out of the data, that will help them compete and grow. The failure to deploy robust infrastructure for data quality and testing undercuts AI’s potential, making it essential for organizations to prioritize a solid data foundation before scaling AI initiatives.”
Having a Trusted Partner Can Help
Additionally, the survey reveals that as organizations advance AI initiatives, most IT leaders recognize the need for third-party support in critical areas, including:
- Hardware – To be effective, hardware needs to be secure, available 24/7, and efficient to meet sustainability goals. In the survey, 22% of IT leaders report needing assistance to create scalable, future-proof hardware solutions.
- Data Storage and Processing Solutions – Effective data solutions bring data closer to users while emphasizing security and sustainability. The survey found that 41% of leaders need help with ROT data storage and data preparation, while 25% seek assistance with data processing.
- Software – Secure, resilient software is vital for protecting against cyber risks and ensuring data accessibility. 31% of IT leaders require third-party expertise for developing effective AI models.
- Skilled Staff – The skills gap remains a hurdle, with 50% of leaders building AI skills through experimentation and 36% relying on self-teaching.
