For decades, a fundamental divide has shaped enterprise data strategy: the absolute separation between operational and analytical systems. On one side stood the digital engine of the company: the online transaction processing (OLTP) systems that manage inventory in real-time. On the other, the strategic brains: the online analytical processing (OLAP) platforms that sift through historical data to support planning and strategy. This divide, traditionally bridged by batch extract, transform, load (ETL), forced leaders to make decisions based on yesterday’s insights.

The availability of AI that can provide strategic insights in real-time is tearing down this wall. Forward-thinking organizations are building unified platforms that combine operational and analytical capabilities. This convergence enables real-time analysis of live data streams, allowing companies to replace reactive reporting with proactive decision-making that delivers immediate business value.

From reactive to proactive: Real-time value in action

The convergence of operational and analytical data transforms business decision-making from reactive to in-the-moment thinking. Instead of asking “what happened,” organizations can focus on “what’s happening now?” and “what can I influence next?”

For example:

• analyzed real-time search data to discover that customers were searching for a “midi parka,” a term absent from their product descriptions. By quickly renaming a product to match this trend, they saw a three-fold increase in conversions overnight.
• In logistics, is analyzing billions of daily data points from 4.6 million connected vehicles for real-time fleet optimization and driver safety.
• In financial services, built a fraud detection platform that achieves 90% accuracy by analyzing transactions as they happen, not after the fact.

The technology making convergence a reality

The shift from siloed, lagging data to real-time, actionable intelligence is made possible by a new generation of cloud-native technologies. Together, they create a powerful data flywheel: a continuous loop where live operational data is analyzed for insights, which are then pushed back into business systems to guide action and improve operations. This self-reinforcing cycle is built on four key technologies:

Data federation: Federation allows an analytical platform to query data directly from operational databases, without moving or copying it. This zero-copy approach allows analysts to combine real-time transactional data with historical data to get a complete, up-to-the-second view of a business operations.

Real-time data streaming: Technologies like change data capture (CDC) can stream updates from operational systems to analytical platforms as they happen, without impacting performance. This ensures that analytical tools are always working with the freshest available data.

Unified storage layer: A modern data lakehouse stores information in open formats accessible to both analytical engines and transactional databases. This eliminates data duplication and allows a single dataset to support everything from advanced analytics and BI dashboards to operational decision-making.

Reverse ETL: Reverse ETL sends insights, such as customer scores or product recommendations, back into business systems like CRMs and marketing platforms. This puts analytics directly into the hands of frontline teams to drive action in real time.

The ultimate catalyst: Giving AI a memory

Converged operational and analytic data systems lay the groundwork for real-time intelligence, but the next wave of business impact will come from autonomous agents that can make and act on decisions – not just support them. However, today’s large language models have a fundamental limitation: They lack business context and are, simply put, forgetful. Without an external brain, every interaction starts from a blank slate.

This is where connecting agents with data across analytical and operational platforms becomes critical. To build truly useful agents, we must give them two types of memory:

1. Semantic memory: This is the agent’s deep, contextual library of knowledge about your business, products, and industry. To improve AI accuracy and reduce hallucinations, modern data platforms now support retrieval-augmented generation (RAG), a technique that lets AI models ground responses in real business data, not just their generic training patterns. This capability relies on vector embeddings and vector search, which finds relevant content by comparing the meaning of queries and data, rather than by exact keywords. By using this approach, AI systems can retrieve the right information from enterprise data platforms, multimodal datasets (e.g., documents), knowledge bases, or even live operational data.

2. Transactional memory: For personalization and reliability, agents need to remember specific interactions and maintain state. This includes both episodic memory (a log of conversations and user preferences, so the agent can carry on conversations that feel continuous, not like a reset each time) and state management (tracking progress through complex tasks). If interrupted, an agent uses this stateful memory to pick up where it left off.

Supporting this memory architecture requires a new generation of data infrastructure: systems that handle both structured and unstructured data, offer strong consistency, and persist state reliably. Without this foundation, AI agents will remain clever but forgetful, unable to reason or adapt in meaningful ways.

The CIO’s new playbook: Architecting the intelligent enterprise

For CIOs, this convergence means a fundamental shift from managing siloed systems to architecting a unified enterprise platform where a real-time data flywheel can spin. This requires building a resilient data foundation that can deliver immediate business value today while also supporting the semantic and transactional memory that tomorrow’s autonomous AI agents require. By solving AI’s inherent “memory problem,” this approach paves the way for truly intelligent systems that can reason, plan, and act with full business context, driving unprecedented innovation.

At Google Cloud, we’ve seen these patterns emerge across industries, from retail to travel to finance. Our platform is designed to support this shift: open by design but also unified and built for scale. It is engineered to converge operational and analytical data so organizations can move from insight to action, without delay.

Learn more by the data leaders’ best practice guide for data and AI.