Angel Vina, Founder & CEO of Denodo, discusses strategies for mastering data management and unlocking valuable insights in the age of AI and cloud.
In the digital economy, data transcends its traditional role as a mere byproduct of business operations, emerging as a pivotal asset at the heart of strategic decision-making. This paradigm shift redefines data from being an Information Technology (IT) afterthought to becoming a cornerstone of business value creation. The transformation is driven by the recognition that data when effectively managed and analyzed, offers unparalleled insights into customer behaviors, market trends, and operational efficiencies.
Consequently, organizations are re-evaluating their approaches to data management, treating it as a core business asset rather than a subsidiary function relegated to IT departments. This evolution necessitates a holistic view of data management strategies, integrating technological advancements with business objectives to harness the full potential of data in driving competitive advantage and innovation. As businesses navigate this shift, adopting robust data management practices becomes central to achieving operational excellence and strategic agility in a data-centric world.
As organizations grapple with the complexities introduced by cloud computing and artificial intelligence (AI), new strategies are emerging to manage vast data assets effectively. In an exclusive interaction between Nisha Sharma, Principal Correspondent at CIO&Leader, and Angel Vina, Founder & CEO of Denodo, we delve into the challenges and solutions shaping the future of data management in a hybrid and cloud-centric environment.
This Q&A session explores the impact of data virtualization, compliance challenges, and the evolving role of Chief Information Officers (CIOs) in leveraging data for business growth.
Founder & CEO
Denodo
CIO&Leader: How has integrating cloud computing and AI affected data management?
Angel Vina: The shift towards cloud computing adds complexity to data management across various cloud environments. AI introduces a dual challenge, necessitating effective data management strategies for AI applications and using AI to enhance data management processes. Data virtualization has emerged as a critical technology, providing an abstraction layer that simplifies data access across disparate environments.
CIO&Leader: What are the main compliance challenges in data management, and how can they be addressed?
Angel Vina: Balancing data utilization with compliance requirements is increasingly complex. Implementing logical data architectures enables organizations to apply governance and compliance rules effectively, ensuring regulatory adherence without extensive system changes.
CIO&Leader: How does data virtualization support AI and data strategies in organizations?
Angel Vina: Data virtualization is crucial in connecting AI applications with the necessary data sources, facilitating efficient data access and utilization. This support is essential for businesses integrating AI into their operations, providing a solid foundation for generating actionable insights.
CIO&Leader: Can you discuss market-specific strategies in data management?
Angel Vina: Strategies are tailored to address the unique challenges and opportunities within specific markets, including developing local expertise and focusing on large enterprises with complex data ecosystems. These targeted approaches aim to enhance the effectiveness of data management solutions in different regions.
CIO&Leader: How is the role of CIOs changing with these developments in data management?
Angel Vina: The CIOs are shifting from focusing on infrastructure management to becoming strategic business partners. Data virtualization aids this transition by easing the burden of data management, allowing CIOs to focus on data-driven decision-making and strategic business growth.
CIO&Leader: What implications does data virtualization have for financial applications?
Angel Vina: While specifics vary, data virtualization in financial sectors demonstrates its potential to support critical operations, highlighting the technology’s reliability and security in managing sensitive financial data.
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