Integrating AI into existing data systems is not easy, but siloed dark data trapped in legacy frameworks will complicate transformation more

In the digital age, organizations create and store more data than ever before.

Often, this data is not well organized, and exists in various forms in the organization. It is also spread across various disparate infrastructures such as on-premises, multiple cloud platforms and edge networks. This diversity creates complexity, causing challenges for businesses trying to manage, secure and derive actionable insights from their data.

Organizations will need to find a way to navigate this complexity to maintain operational efficiency, ensure compliance, and derive maximum value from their data. Also, in adopting AI, organizations may find that integrating the technology into existing data systems is not easy if their data management is not optimal.

Strategies for simplifying data 
Businesses need to unify their data, improve accessibility, and implement security measures to make AI effective. For business and technology leaders, 2025 will be both an opportunity and a challenge. Scaling AI successfully will require organizations to rethink how they manage their data and modernize their infrastructure.

As AI brings specific security and sustainability concerns, organizations will also benefit from having data that is visible, usable and of high quality. A seamless data ecosystem, underpinned by block storage and unified data services, can ensure that data is accessible, accurate and scalable.

By removing silos and enabling real-time inferencing, organizations and businesses can simplify workflows and prepare data for AI-driven innovation. Here are some key strategies to consider:

  • Getting primed for better AI data governance: Organizations should optimize each stage of the AI data pipeline.
  • Boosting cyber resilience: Organizations should build in security and rapid recovery for strong cyber resilience and for improving protection of sensitive AI-driven data and systems.
  • Undertaking cloud transformation: Consider migrating to the cloud and managing the agility, cost and performance of the cloud infrastructure. In the public cloud domain
  • Modernizing data infrastructures: Infusing greater intelligence and optimization into current data infrastructures via investments in scalable and adaptable technologies will also support the growing demands of AI adoption.
  • Creating common capabilities: Enabling consistent and seamless data infrastructure management across on-premises and cloud environments also helps in unifying and simplifying data for optimal use.

Besides increasing operational efficiency and mitigating risk, tangible business outcomes organizations can expect from the above approach include lower operational costs and higher sustainability through reduced carbon footprint and energy consumption. 

Getting the frameworks right
Building secure and sustainable AI frameworks and modernizing data infrastructure will allow organizations in the region and beyond to streamline their operations, improve efficiency, and meet the growing demands of AI-driven workloads.

By focusing on tangible strategies and investing in technologies that enable silo-free, intelligence-driven data infrastructure, businesses can take advantage of new growth opportunities while building resilience.