How data for AI is changing the modern data platform
Data platforms must evolve to meet AI requirements, with a greater focus on real-time integration, unified governance, infrastructure flexibility and data quality.
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Published: 15 Jul 2025
Traditional data platforms supported enterprise decision-making by managing, processing and analyzing data to drive better business outcomes. Companies used to rely on these platforms to centralize data, ensure consistency and enable teams to uncover insights through analytics, reporting and business intelligence (BI) tools.
At its core, a data platform is built to ingest structured data from systems such as ERP and CRM, store it efficiently and serve it to analysts and decision-makers. The result is smarter decisions, improved operational efficiencies, and better customer experiences.
But the game has changed.
The explosive growth of AI and the desire to use agentic AI are redefining the demands on data platforms. Today, organizations are focused on data for AI -- a shift that's driving a fundamental change in modern data architecture.
AI requires massive amounts of high-quality, trusted data -- structured and unstructured -- from a wide range of sources. This includes transactional databases, sensor feeds, images, audio, video and social media posts. Organizations must process and analyze data at scale, often in real time, on-premises, in the cloud or multiple clouds and hybrid environments.
Evolving platform requirements for AI readiness
This new need for massive amounts of data has led to a new set of requirements for data platforms:
Focus on data quality. AI models are only as good as the data they are trained on. Ensuring accuracy, consistency, completeness and timeliness of data is now a top priority. Poor data quality leads to biased models, flawed predictions and bad outcomes.
Unified data governance and security. As data moves across cloud and on-premises environments, organizations must enforce consistent governance, ensure compliance with regulations such as GDPR and HIPAA, and safeguard sensitive information wherever it resides, including AI responses and actions.
Scalable data integration. The need to harness structured and unstructured data from virtually anywhere -- enterprise systems, data lakes, cloud warehouses and streaming platforms --demands powerful integration capabilities.
Modern infrastructure flexibility. Organizations are embracing multi-cloud and hybrid models for cost efficiency, resilience and agility. Data platforms must support this flexibility without compromising performance or control.
A new data paradigm. All data across an organization can be valuable for AI training, making it essential to prepare data for AI use from inception to outcomes. This shift is a fundamental new way of thinking and requires a cultural change in how employees and partners treat raw and curated data.
How technology vendors are enabling AI
Technology vendors have recognized this shift and are innovating rapidly. Cloudera, for example, has its hybrid platform and has evolved with new capabilities, including Private AI, offering governance and AI-ready capabilities. AWS, Google Cloud and Oracle offer cloud-native AI and machine learning services tightly integrated with their data platforms, enabling end-to-end pipelines from ingestion to model training. Oracle's Database 23ai is now available on AWS, Google and Microsoft Azure. Google BigQuery's effort to give structure to all unstructured data is just one big move to prepare data for AI.
The rise of AI has redefined what a data platform must do.
Vendors are increasingly developing platforms that bring together data and AI workloads. Databricks, for example, supports this convergence through its Lakehouse architecture and Unity Catalog, which are designed to combine the reliability of data warehouses with the flexibility of data lakes -- all under a governance framework. Snowflake is also expanding its AI integration capabilities and ecosystem, with offerings that aim to support the operationalization of data for AI use cases.
Qlik and Quest also provide tools for data integration, quality and transformation, allowing for data from disparate sources to be unified and trusted for AI applications.
The rise of AI has redefined what a data platform must do. It's no longer limited to supporting BI dashboards or historical analytics. Now, it must enable a future where AI-driven insights are embedded into every business process, decision and customer interaction. To get there, organizations need data platforms that are agile, scalable and AI-ready.
In this new era, the industry leaders will be those who master data for AI and transform their platforms to realize their full potential.
Stephen Catanzano is a senior analyst at Enterprise Strategy Group, now part of Omdia, where he covers data management and analytics.
Enterprise Strategy Group is part of Omdia. Its analysts have business relationships with technology vendors.
