AI systems need fresh, high-quality context right now, not batches of data delivered hours later.
Delayed data is “a silent killer” for AI initiatives. In a recent Forbes article, CEO and Cofounder of Raven DB, Oren Eini described feeding AI models delayed data as “playing broken telephone with your most important business decisions.” The answer is to bring AI closer to real-time, operational data. Instead of leaving AI systems to deal with data scraps, it’s critical to fuel models with fresh data and apply its intelligence in real time.
The enterprises deploying successful AI initiatives are not deploying radically different AI models or agents. They’re building event-driven architectures, designed to stream context-rich, trustworthy, and compliant data into AI systems with sub-second latency. Real-time analytics is the technological pillar that makes this possible.
Delayed Reaction: The Challenges Calling for Real-Time Analytics
In order to reason, predict, and act effectively, intelligent systems need trusted, real-time data delivered in AI-ready formats.
Delayed Data: When insights arrive late, decisions are made on stale information, leading to missed opportunities, flawed predictions, or even harmful outcomes. In enterprise environments, delayed data means AI systems are always reacting to yesterday’s reality.
Ungoverned Data: If data feeding into AI systems fails to meet governance and compliance standards, organizations face exposure to fines, legal action, and reputational damage. Beyond penalties, ungoverned data erodes confidence in AI outputs, making stakeholders question the entire project.
Inconsistent Data Structures: Inconsistent data leaves AI systems to struggle to parse signals from noise, leading to inaccurate outputs, bias, or wasted computational effort. As a result, insights are less reliable, integration is slowed, and the potential value from AI initiatives is lost.
How Striim Protects, Enriches, and Transforms Data in Real Time
Striim’s platform transforms streaming pipelines into a real-time analytics and decisioning engine. This ensures that agentic AI has the context-rich, trustworthy, and compliant data it needs to reason, predict, and act effectively across critical business scenarios.
Armed with real-time Event-Driven Architectures powered by Striim, enterprises get:
- Advanced streaming analytics that protect, enrich, and enhance data
- Real-time activation & alerts when anomalies are detected
- Full, end-to-end observability of streaming pipelines
- In-stream transformation that processes data before it lands
Benefit From a Platform Purpose-Built for Real-Time
Enterprises should ditch outdated, batch-based systems that delay insights. With Striim, they can not only deliver sub-second insights but also feed MCP-ready, governed streams into AI systems.
Improve operational efficiency through faster actions
By streaming and analyzing data in real time, Striim eliminates the lag of batch processing and manual intervention. Enterprises can automate workflows, detect issues instantly, and act on live signals instead of waiting for static reports.
Gain a competitive edge via instant responses to market and customer shifts
Markets and customer behaviors change by the second. Striim’s sub-second pipelines feed AI and analytics with live intelligence, enabling enterprises to adjust pricing, inventory, and engagement strategies as events unfold.
Reduce risk with real-time anomaly detection and interventionStriim’s in-stream anomaly detection identifies irregular patterns—whether in transactions, operations, or data pipelines—before they become costly incidents. By embedding governance and AI-powered monitoring in motion, risks are flagged and mitigated immediately.
Enhance customer experiences with adaptive, AI-driven services
With live, context-rich data streams, Striim enables adaptive experiences, whether it’s powering personalized recommendations, preventing service disruptions, or syncing inventory in real time.
Enable continuous innovation through live insights
Static data slows innovation. Striim gives teams always-current insights to experiment, refine AI models, and launch new services with confidence, enabling you to accelerate the development cycle from idea to impact.
Real-Time Analytics in Action: How Virgin Media O2 Support Proactive Network Intelligence with Real-Time Data
Virgin Media O2, a telecommunications leader serving over 45 million customers, needed to strengthen its network intelligence capabilities to deliver reliable, high-quality service at scale. With millions of concurrent users, the company required a way to instantly detect performance issues, analyze time-based patterns, and ensure its data was clean, governed, and AI-ready.
The Striim Solution
With Striim, Virgin Media O2 can stream, enrich, and govern high-velocity data in real time, powering proactive insights across its network.
- Proactive network intelligence that detects performance issues instantly and analyzes customer and network behavior patterns
- A real-time analytics platform that streams operational data into BigQuery with sub-second latency for immediate insights
- In-flight governance that sends clean, enriched data to analytics tools and APIs to ensure intelligent systems can consume trusted, AI-ready datasets
The Results
- Faster detection and resolution of network performance issues
- Improved customer experience with proactive, context-aware service delivery
- Increased operational efficiency by eliminating manual monitoring delays
- Stronger compliance through governed, real-time data pipelines
- A future-ready foundation for AI-driven network optimization
Ready to take the next step, and explore real-time analytics with Striim? Try Striim for Free, or Request a Demo to learn more.
