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Hitachi Vantara Survey: Data Quality Crucial for AI Success

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Hitachi Vantara Survey: Data Quality Crucial for AI Success

Hitachi Vantara Survey: Data Quality Crucial for AI Success

MTE

Published on : Dec 13, 2024

As AI adoption accelerates across industries, data quality and infrastructure have emerged as critical challenges. According to the Hitachi Vantara State of Data Infrastructure Survey, 42% of UK companies cite data concerns as the top barrier to AI success. However, many organisations fail to take adequate steps to address these issues, jeopardising the outcomes of AI initiatives.

Survey Insights: Key AI Challenges in the UK

  1. Data Quality and Management

    • 41% of UK respondents highlighted high-quality data as essential for successful AI projects.
    • Despite this, 56% of UK IT leaders report that more than half their data is "dark," meaning unused or unanalysed.
  2. Data Storage Concerns

    • AI has driven a massive demand for data storage, with global data volume expected to increase by 150% by 2026.
    • 45% of businesses face significant challenges in managing growing storage needs, handling an average of 150 petabytes (PB) of data today.
  3. Security and Risk

    • 70% of respondents acknowledge the catastrophic impact of data loss.
    • 65% are concerned about AI being exploited by hackers to create advanced cyber tools.
  4. Sustainability and ROI

    • Only 44% of businesses prioritize sustainability in AI projects, and a similar number focus on ROI.
    • Modern infrastructure can enhance energy efficiency while ensuring long-term operational resilience.

Executive Perspectives on Building AI Success

  • Sasan Moaveni, Global Business Lead for AI at Hitachi Vantara, stressed:

    • "Trust in AI systems and outputs is crucial for success."
    • "Many companies dive into AI without a solid strategy, leading to costly failures."
    • "A robust infrastructure and sustainable practices are essential for scalable AI growth."
  • Octavian Tanase, Chief Product Officer, highlighted:

    • "Operational simplicity and automation can drive efficiency."
    • "Without robust infrastructure, AI’s potential is undercut, hindering competitive growth."

Why Data Infrastructure is Key to AI Success

  1. Controlled Environments for AI Experimentation

    • Only 12% of businesses use secure sandboxes, leaving significant vulnerabilities in AI testing.
  2. Sustainable Data Management

    • Advanced infrastructure enables energy-efficient data management, reducing environmental impact and improving performance.
  3. Mitigating Risks with Trusted Partners

    • Third-party expertise is critical in areas such as:
      • Hardware: 27% require scalable and sustainable hardware solutions.
      • Data Storage & Processing: 37% need assistance in data processing, and 26% in ROT (redundant, outdated, trivial) data storage.
      • Software Development: 35% seek support for secure, resilient AI models.
      • Skill Development: 46% of leaders rely on experimentation, while 42% use self-teaching to build AI expertise.

The survey underscores the importance of prioritizing data quality and robust infrastructure to unlock AI's full potential. By adopting sustainable practices, leveraging modern infrastructure, and partnering with trusted providers, organisations can overcome challenges and drive successful AI initiatives.