Errol Rodericks, EMEA and LATAM product and solutions marketing director of Denodo, explains why real-time, connected data is the answer for the best possible experiences and outcomes for patients.
This year marks a turning point in the healthcare sector as artificial intelligence (AI) moves from experimentation to widespread adoption. Whether used to support earlier diagnosis through predictive models or to ease administrative workloads for clinicians, AI will transform how care is delivered and managed. By 2034, the UK AI healthcare market size is projected to reach £2.9 billion – illustrating the key role AI will play in healthcare in the future.
Last year, the UK government conducted the world’s largest AI trial in healthcare, demonstrating how AI tools like Microsoft 365 Copilot could free up hundreds of thousands of NHS staff hours every month, cut operational costs and help raise the standard of patient care. The Medicines and Healthcare products Regulatory Agency has also issued a formal call for evidence regarding AI oversight, signalling that broad adoption and increased regulatory scrutiny are expected.
These initiatives support and are aligned with the government’s 10 Year Health Plan that aims to establish the NHS as “the most AI‑enabled health care system” globally. For healthcare providers, whether AI has a role to play is no longer the question – rather, it is whether their data infrastructure has the capability to support the scale, quality and speed that AI demands.
The growing challenge of fragmented healthcare data
Healthcare leaders still struggle to manage the vast data requirements of AI. The greatest barrier to adoption is no longer the absence of data but the realities of managing it effectively. Patient information is still siloed across numerous paper records, electronic health systems, imaging archives, research environments and operational platforms.
As data must be ingested and refined before being used, traditional data management systems slow down decision‑making and reduce the effectiveness of AI models. Modern data architecture, like lakehouses, also lacks a universal semantic layer, consistent governance across distributed data and true personalised self‑service access for the clinicians and analysts who rely on timely information. Logical data management overcomes these limitations by virtually connecting data at its source – enabling governed, real‑time access without any need for replication.
Healthcare organisations seeking continuous, real‑time intelligence rather than static views of data can look to logical data management platforms as a solution. Instead of moving or duplicating data, a logical data layer uses data virtualisation to create a unified, dynamic view that spans all relevant systems. Data remains in its original location, and queries are executed in real-time through automated, optimised processes.
This approach removes the delays and risks associated with replicating sensitive healthcare information. It ensures that clinicians, analysts and AI applications can work with complete and up‑to‑date information without navigating layers of technical complexity.
The operational efficiencies are also evident. Rather than spending months building data pipelines for each new AI or analytics initiative, organisations can quickly create trusted, governed data sets ready for use. Policies and definitions are applied consistently across all use cases, which improves the reliability of insights. Most importantly, AI tools can operate using the most current clinical and operational information, enabling decisions that accurately reflect real‑world conditions.
As expectations for responsible AI use grow, regulatory bodies such as the MHRA are placing more emphasis on transparency, oversight and the protection of patient data. Logical data management supports these requirements by integrating governance directly into the data access layer. Organisations can easily trace where data comes from, how it is used and who can access it – all while maintaining strict security and confidentiality.
AI readiness should focus on enabling better outcomes for patients, supporting clinicians with improved information and strengthening the overall performance of healthcare organisations. A resilient foundation ensures that business‑ready data is always available and future innovation can be scaled without constant re‑engineering. Logical data management provides this stability and flexibility, enabling organisations to maximise the value of their existing data assets.
Errol Rodericks, EMEA and LATAM product and solutions marketing director of Denodo.
Looking ahead to tomorrow’s AI landscape
A leading public health organisation in Canada demonstrates the impact of this approach. By implementing a logical data management layer across its ecosystem, the organisation was able to unify information held within its enterprise data warehouse and a newly deployed Cerner patient system without copying it into new environments. This resulted in significantly faster system updates, with turnaround times reduced by 30% to 50%. Furthermore, with strengthened governance, near‑real‑time reporting became possible, and teams were able to adapt quickly to changing clinical and operational demands.
As AI adoption accelerates, the healthcare organisations that can reliably access real‑time, trusted data will be positioned to lead. They will be more resilient in responding to regulatory developments, faster in deploying new AI‑enabled capabilities and more effective in delivering high‑quality care at scale.
Logical data management converts fragmented data estates into strategic assets. This positions healthcare providers to be truly AI‑ready and empowers them to deliver the best possible experiences and outcomes for patients.
Comment: The foundation for AI-ready healthcare
Table of Contents
Errol Rodericks, EMEA and LATAM product and solutions marketing director of Denodo, explains why real-time, connected data is the answer for the best possible experiences and outcomes for patients.
This year marks a turning point in the healthcare sector as artificial intelligence (AI) moves from experimentation to widespread adoption. Whether used to support earlier diagnosis through predictive models or to ease administrative workloads for clinicians, AI will transform how care is delivered and managed. By 2034, the UK AI healthcare market size is projected to reach £2.9 billion – illustrating the key role AI will play in healthcare in the future.
Last year, the UK government conducted the world’s largest AI trial in healthcare, demonstrating how AI tools like Microsoft 365 Copilot could free up hundreds of thousands of NHS staff hours every month, cut operational costs and help raise the standard of patient care. The Medicines and Healthcare products Regulatory Agency has also issued a formal call for evidence regarding AI oversight, signalling that broad adoption and increased regulatory scrutiny are expected.
These initiatives support and are aligned with the government’s 10 Year Health Plan that aims to establish the NHS as “the most AI‑enabled health care system” globally. For healthcare providers, whether AI has a role to play is no longer the question – rather, it is whether their data infrastructure has the capability to support the scale, quality and speed that AI demands.
The growing challenge of fragmented healthcare data
Healthcare leaders still struggle to manage the vast data requirements of AI. The greatest barrier to adoption is no longer the absence of data but the realities of managing it effectively. Patient information is still siloed across numerous paper records, electronic health systems, imaging archives, research environments and operational platforms.
As data must be ingested and refined before being used, traditional data management systems slow down decision‑making and reduce the effectiveness of AI models. Modern data architecture, like lakehouses, also lacks a universal semantic layer, consistent governance across distributed data and true personalised self‑service access for the clinicians and analysts who rely on timely information. Logical data management overcomes these limitations by virtually connecting data at its source – enabling governed, real‑time access without any need for replication.
Healthcare organisations seeking continuous, real‑time intelligence rather than static views of data can look to logical data management platforms as a solution. Instead of moving or duplicating data, a logical data layer uses data virtualisation to create a unified, dynamic view that spans all relevant systems. Data remains in its original location, and queries are executed in real-time through automated, optimised processes.
This approach removes the delays and risks associated with replicating sensitive healthcare information. It ensures that clinicians, analysts and AI applications can work with complete and up‑to‑date information without navigating layers of technical complexity.
The operational efficiencies are also evident. Rather than spending months building data pipelines for each new AI or analytics initiative, organisations can quickly create trusted, governed data sets ready for use. Policies and definitions are applied consistently across all use cases, which improves the reliability of insights. Most importantly, AI tools can operate using the most current clinical and operational information, enabling decisions that accurately reflect real‑world conditions.
As expectations for responsible AI use grow, regulatory bodies such as the MHRA are placing more emphasis on transparency, oversight and the protection of patient data. Logical data management supports these requirements by integrating governance directly into the data access layer. Organisations can easily trace where data comes from, how it is used and who can access it – all while maintaining strict security and confidentiality.
AI readiness should focus on enabling better outcomes for patients, supporting clinicians with improved information and strengthening the overall performance of healthcare organisations. A resilient foundation ensures that business‑ready data is always available and future innovation can be scaled without constant re‑engineering. Logical data management provides this stability and flexibility, enabling organisations to maximise the value of their existing data assets.
Looking ahead to tomorrow’s AI landscape
A leading public health organisation in Canada demonstrates the impact of this approach. By implementing a logical data management layer across its ecosystem, the organisation was able to unify information held within its enterprise data warehouse and a newly deployed Cerner patient system without copying it into new environments. This resulted in significantly faster system updates, with turnaround times reduced by 30% to 50%. Furthermore, with strengthened governance, near‑real‑time reporting became possible, and teams were able to adapt quickly to changing clinical and operational demands.
As AI adoption accelerates, the healthcare organisations that can reliably access real‑time, trusted data will be positioned to lead. They will be more resilient in responding to regulatory developments, faster in deploying new AI‑enabled capabilities and more effective in delivering high‑quality care at scale.
Logical data management converts fragmented data estates into strategic assets. This positions healthcare providers to be truly AI‑ready and empowers them to deliver the best possible experiences and outcomes for patients.
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