Intelligent systems demand real-time inputs. Whether it’s agentic AI, capable of handling complex, multi-step processes, or smaller scale generative AI use cases, stale information puts AI on the back foot.
Perhaps Sridhar Ramaswamy, CEO of Snowflake and Google veteran, put it best: “Everybody knows there’s no AI without data, without the right data, without high quality data, without governed data.”
But bringing together different types of data—and preparing them for AI use cases—in real time is no small feat. Data isn’t necessarily in the right format for AI by default. Nor is it unified, integrated, and stored where it’s needed, in the cloud. Without a unified approach, insights are fragmented, decision-making is hindered, and opportunities for AI innovation are missed.
This article offers a practical framework to help you avoid these pitfalls by exploring the critical parts of a modern data strategy, common mistakes that hurt business results, and the case for adopting a unified, real-time integration platform to tie it all together.
A Real-World Example
Consider a manufacturing company that has data scattered across systems—ERP software, production line sensors, supply chain management tools, and customer order databases—and operating in silos.
When the COO asks for a report on how a supply chain delay is impacting production timelines and customer orders, the IT team has to scramble, manually consolidating data from multiple sources. By the time the report is ready, the disruption has already escalated, costing the company both revenue and customer trust.
What Is a Data Management Strategy?
You can think of data management as your organization’s central operating system for information. Your strategy will dictate how you collect, secure, and govern data, as well as explain why those actions matter. A well-thought-out strategy brings clarity to your approach to data, reduces risk, and transforms data from a resource into a powerful engine for growth.
It’s also important to distinguish between strategy (the rules and priorities) and execution (the tools and workflows). Strategy sets the direction; execution puts it into motion.
Key Components of a Modern Data Management Strategy
A comprehensive strategy covers the entire data lifecycle, from collection and integration to storage and access. It’s also an opportunity to bring together your teams, processes, and platforms. To build a strong foundation, focus on the following pillars:
Data Collection and Ingestion
Continuously collect data from every important source: databases, applications, and edge devices. Real-time ingestion eliminates delays and batch-processing bottlenecks.
Data Integration and Transformation
Create seamless, automated workflows that transform, enrich, and harmonize data at scale. Unify data flows to ensure higher quality and reduce manual errors.
Data Storage and Architecture
Build modular, scalable, and cost-effective architecture. Balance operational databases, data lakes, and analytical warehouses without vendor lock-in.
Data Governance and Security
Create trust with built-in security, access controls, encryption, real-time monitoring, and automated audit trails for regulatory compliance.
Data Quality and Lineage
Ensure complete, accurate data across the organization with in-flight quality measures and automated schema checks for clean, ready-to-use data.
Data Access, Sharing, and Analytics
Deliver timely, guardrailed access to data through APIs, real-time streams, and secure self-service portals while maintaining compliance and oversight.
Data Collection and Ingestion
To get a clear picture of your business, you need to continuously collect data from every important source: databases, applications, and even devices at the edge of your network. Real-time ingestion gets rid of delays and batch-processing bottlenecks, feeding your analytics, operations, and AI initiatives with live information. The result is simple: every team gets the latest data for better, faster decision-making.
Data Integration and Transformation
Raw data is almost always fragmented. Modern data integration is more than just building extract, transform, load (ETL) pipelines; it’s about creating seamless, automated workflows that transform, enrich, and harmonize data at scale. When you unify these data flows, you ensure higher quality, reduce manual errors, and help your data teams and business users move faster with less overhead.
Data Storage and Architecture
Your data architecture needs to be modular, scalable, and cost-effective. A smart strategy balances operational databases, data lakes, and analytical warehouses without locking you into a single cloud, environment, or vendor. This approach helps you maximize performance and cost efficiency while supporting innovation wherever your business needs it.
Data Governance and Security
Robust governance and built-in security create trust, improve operational efficiency, and help you ensure regulatory compliance. This demands access controls from day one, encryption in transit and at rest, real-time monitoring, and automated audit trails. The most resilient strategies operationalize governance instead of treating it as a bolt-on or an afterthought.
Data Quality and Lineage
Trust in your data hinges on its quality, and how you can demonstrate efforts that ensure complete, accurate data across the organization. With in-flight quality measures and automated schema checks, you can ensure data lands at its destination clean and ready for use.
Data Access, Sharing, and Analytics
Giving teams timely, guardrailed access to data is foundational for a data-driven organization. An enterprise-ready strategy aims to deliver data to front-line teams through APIs, real-time streams, and secure self-service portals—without losing control over compliance and oversight.
Dive into why real-time, high-quality data is essential for AI and analytics. This article from the Striim blog explains how tackling issues like data silos and delays with smart integration and fast processing can unlock better insights and decisions.
Tools and Technology to Support Your Data Management Strategy
Your technology should support your strategy, not dictate it. When you consider solutions for your organization, look for tools that reduce manual work and consolidate workflows, without requiring a heavy lift from engineers. Striim combines all of these capabilities into a single, unified, real-time solution.
Data Integration and Streaming Platforms
Old-school batch ETL is too slow and risky to support today’s needs. Striim uses real-time, log-based Change Data Capture (CDC) to sync data across your entire ecosystem the moment changes happen. This means your operational dashboards, AI models, and critical workflows run on live data, not yesterday’s information.
Data Storage and Architecture Tools
Enterprise environments need to be scalable and work well together. Platforms like Snowflake, BigQuery, and Redshift can scale on demand, while an intelligent architecture ensures data flows smoothly across on-premises, cloud, and hybrid systems. As a rule of thumb, look for solutions that support integration and cost visibility from the start.
Data Governance and Cataloging Tools
Governance is most effective when it’s automated and integrated directly into your data pipelines. This supports policy enforcement, real-time data lineage, and central oversight without creating roadblocks. Striim’s built-in observability makes compliance and auditing a seamless part of existing data workflows.
Data Quality and Observability Platforms
In an enterprise environment, you need to know what’s happening with your data at every step. Striim gives you real-time schema change detection, process monitoring, and anomaly alerts. This empowers your team to fix errors before they impact business users or your bottom line. Continuous observability is non-negotiable.
Business Intelligence and Analytics Tools
BI and analytics tools depend on fresh, trusted data. The right solutions integrate smoothly with your core data platform, making insights accessible to everyone from technical users to business leaders.
Discover how adopting a streaming-first approach to your data strategy can deliver real-time insights, improve decision-making, and drive business growth in this step-by-step article.
Best Practices for Building a Scalable Data Management Strategy
A strong data management strategy isn’t about buying more tools. It’s about tying together your data infrastructure, processes, and people with clear objectives in mind.
Start with a Clear Business Case
Tie every technology decision to an intended business outcome, whether it’s faster time-to-insight, revenue growth, cost savings, or risk reduction. A strategy built around a clear ROI will get the support it needs and prevent wasted effort.
Prioritize Data Governance Early
Build resilience by embedding governance—including access controls, lineage, and privacy—from the beginning. Investing in governance early on protects you from costly rework and potential reputational damage down the road.
Embrace Real-Time Where It Matters
In today’s business world, even a small delay can lead to missed opportunities. Use a real-time architecture wherever it creates value. Striim’s native streaming offers sub-second latency, so you can lead your market instead of following it.
Build for Cloud and Hybrid Architectures
Look for platforms that support hybrid and multi-cloud deployments, allowing you to unite different data sources under a single system of control. This minimizes migration risk and accelerates future innovation.
Foster a Data-Driven Culture
Data only drives success when people are empowered to use it. Promote data champions within your organization, invest in training and documentation, and celebrate success stories. Make data a part of everyone’s job, not just the purview of data teams.
How can a streaming data architecture help you address the deluge of data headed our way?
Alex Woodie, Editor-in-Chief of Datanami, and Steve Wilkes, founder and CTO of Striim, sat down for an in-depth discussion. Watch the webinar.
Bring Your Data Management Strategy to Life with Striim
Complexity holds back progress. The Striim platform is engineered to turn your ambitious real-time data goals into a daily reality.
Live data with sub-second latency: Striim’s log-based CDC and unified streaming power your dashboards, analytics, and AI with the freshest data available. No more lag, no more batch windows.
Unified, resilient infrastructure: Striim replaces a patchwork of separate tools with one complete platform for ingestion, transformation, quality, and delivery. This lowers risk, cuts costs, and helps you scale quickly.
Embedded security and governance: Built for compliance, Striim provides granular access controls, automated lineage, and continuous monitoring. Data trust is built-in, not bolted-on.
Cloud ready: Striim delivers the same reliability and simplicity no matter where your data lives, supporting on-prem, cloud, and hybrid architectures with flexibility and security.
Proven business impact: With a low total cost of ownership (TCO) and faster project delivery, Striim turns real-time data from a technical goal into a strategic advantage.
If you’re ready to unify your data landscape and unlock the full promise of an AI-driven business, you need a platform—and a strategy—that’s built for enterprises like yours.
Book a demo for a tailored look at how Striim can redefine your data management, or get started for free and see the impact of unified, real-time data integration for yourself.
Agentic intelligence has the potential to transform every industry. But only when connected to relevant context.
The major LLMs many of us are familiar with: ChatGPT, Claude, Perplexity, and Gemini, are only so useful in the enterprise context. In order to handle complex tasks within a large organization, AI systems need more than individual prompts. As one CEO put it: “the problem at the heart of many AI disappointments isn’t bad code. It’s context starvation”.
Agents need context. But there are two blockers standing in the way. First, brittle, batched-based data infrastructure that cannot deliver fresh, up-to-date context so AI can act in the moment. Second, a secure, compliant way to connect agents to context, without overwhelming production systems.
Due to the non-deterministic nature of AI agents, we cannot know for certain how many times they query a source database. Enterprises therefore need continuous, real-time, compliant zones where agents can safely retrieve the vital context they need to produce meaningful outcomes.
Equipping Agents: The Challenges Behind Agentic AI at Scale
For AI agents to produce meaningful outcomes based on relevant insights, they need real-time, governed context delivered in AI-ready formats, without overwhelming core production systems.
Stale or delayed context: If agents operate on stale, outdated information, they make flawed predictions, miss opportunities, and deliver unreliable outcomes. In enterprise environments, even small delays can lead to poor customer experiences, financial risk, or compliance failures.
Unsafe or non-compliant context: Feeding agents ungoverned data introduces significant exposure, such as violating GDPR, CCPA, or AI governance rules. Beyond legal risk, unsafe data erodes trust in agentic decisions, undermining the organization’s confidence in their AI systems.
Production system overload: Allowing agents to directly query live operational systems creates contention, latency spikes, and outages. This destabilizes mission-critical applications and hinders AI adoption, as teams hesitate to risk production performance.
How Striim Powers Agentic AI with Rich, Real-Time, Read-Only Context
Striim supplies agentic AI with live, governed, and read-only context, ensuring AI systems can reason and act without putting production environments at risk. By transforming operational data into secure, AI-ready context in sub-second timeframes, Striim enables enterprises to scale agentic AI safely and effectively.
With Striim’s real-time, MCP-ready operational data store, enterprises get:
MCP AgentLink, a solution that delivers sub-second, secure replication to feed AI agents live data without impacting production systems
Built-in AI and ML interoperability that support open data formats, enabling agentic systems to utilize real-time data
Governance agents: Sherlock and Sentinel, that automate masking and protect sensitive data in real time
Vector embedding agent: Euclid, that embeds intelligence directly into data streams in real time
Anomaly detection agent: Forseer, that detects and flags inconsistencies before they make an impact
Striim Co-Pilot: making it fast, easy, and safe to deploy robust, real-time pipelines
Scalable, event-driven architectures that keep agents continuously supplied with the most relevant context
Benefit from Architecture Built For Agentic AI
Enterprises can no longer afford to treat AI as an experiment. With AI-centric architecture, organizations can operationalize agentic systems safely and at scale. By embedding compliance, governance, and automation into the data layer, enterprises accelerate time-to-value while reducing risk and strengthening confidence in AI-driven outcomes.
Accelerate AI operationalization with trusted, compliant pipelines
Agentic AI relies on continuous, high-quality context. With governed pipelines delivering compliant, real-time data, enterprises can move from pilots to production quickly, ensuring AI agents act on the most relevant, trusted information.
What this means for you: Faster time-to-value and reduced friction when scaling AI across the enterprise.
Strengthen compliance with regulatory standards
Compliance should never be an afterthought. AI-ready architectures enforce governance in motion, ensuring sensitive data is masked, anonymized, and secured before it ever reaches an AI system.
What this means for you: Reduce exposure to regulatory penalties while confidently deploying AI across sensitive domains.
Build organization-wide trust in AI-driven outcomes
Meaningful outcomes from AI are only possible when built on a solid foundation of trust. By grounding agents in transparent, well-governed data pipelines, enterprises improve explainability and reliability of outputs, building confidence from executives to end-users.
What this means for you: Greater buy-in across teams and leadership for AI initiatives.
Reduced compliance costs by automating governance
Manual governance and auditing are expensive, slow, and error-prone. Automated compliance within the streaming architecture enforces policies at scale, eliminating overhead and reducing costly rework.
What this means for you: Lower operational costs and audit-ready AI pipelines without additional burden.
Accelerate ROI with production-ready AI deployment
The real returns from AI come when it’s embedded into daily decisioning and operations. With enterprise-ready data foundations, organizations can safely deploy agents that optimize processes, detect risks, and personalize services in real time.
What this means for you: AI moves from concept to measurable business impact in weeks, not months.
Agentic AI in Action: How UPS Protects Shipments and Drives AI-Powered Revenue Growth
United Parcel Service (UPS), a global leader in logistics and package delivery, faced increasing pressure to secure shipments and reduce fraudulent claims. Rising e-commerce volumes and package theft exposed operational vulnerabilities, while merchants and consumers demanded greater reliability and trust. UPS needed a way to analyze delivery risk in real time, strengthen fraud prevention, and ensure AI-driven logistics decisions were powered by accurate, governed data.
The Striim Solution
UPS Capital implemented Striim’s real-time data streaming into Google BigQuery and Vertex AI, powering its AI-Powered Delivery Defense™ solution. Striim enabled high-velocity, sub-second data ingestion, cleaning, enrichment, and vectorization in motion, making data instantly AI-ready for ML models and APIs.
AI-Powered Delivery Defense™: Streams data into BigQuery and Vertex AI for real-time risk scoring and address confidence.
Fraud Detection & Risk Management: Analyzes behavioral patterns to flag risky deliveries and reduce fraudulent claims.
Instant AI-Ready Data: Cleans, enriches, and vectorizes data in motion, ensuring UPS can run advanced ML models without latency.
Adaptive Defense Against Emerging Threats: Continuous vector generation strengthens defenses against evolving fraud and theft tactics.
The Results
Enhanced customer experience through reliable, more secure deliveries
Cost savings from a reduction in package theft and fraudulent claims
Proactive, AI-powered risk management through predictive analytics
Shipper and merchant protection with continuous monitoring and anomaly detection
Enterprise-grade AI enablement, through Striim’s scalable AI-ready data foundation
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.
What this means for you: Less time firefighting and more time driving high-value outcomes.
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.
What this means for you: Spot market opportunities and act on them before your competitors.
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.
What this means for you:Gain peace of mind knowing threats are intercepted before they impact the business.
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.
What this means for you: Happier, more loyal customers with greater lifetime value.
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.
What this means for you: Teams can confidently test, learn, and deliver new AI-driven capabilities without delay..
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
If your data infrastructure isn’t ready for AI, neither is your organization.
In fact, a recent report outlined that 95% of enterprise AI pilot projects are failing to deliver meaningful results. The issue is not the AI models. It comes down to “flawed enterprise integration”, in other words: the inability for enterprises to connect AI systems with the data they need to perform.
And not just any data. For enterprises to break into the elusive 5% of organizations succeeding with AI, they need unified, trusted data from all their critical sources. Data that’s transformed, enriched and delivered in real time.
Fractured systems: The Challenges Behind Data & Platform Modernization
Enterprise data is everywhere. It’s often scattered, siloed, and stuck in legacy systems. That’s why upgrading infrastructure towards a unified dataset is essential for enterprises that aspire to operationalize AI.
Data Silos: Siloed data isn’t just inefficient, it increases risk while eroding trust. In an enterprise environment, the stakes are too high to feed siloed, disconnected data to AI. To succeed, agentic systems need unified, well-governed data that the organization can rely on.
Data Fragmentation: Even when accessible, data is often fragmented across different formats and structures. If it’s not cohesive, consistent, and easily available, enterprise data will fail to provide meaningful context for agentic systems.
Legacy Systems: Rigid legacy systems can’t support the low-latency, high-volume data streams essential for real-time AI. Without fresh, real-time data, agentic AI risks missing new opportunities or worse: acting on false information with disastrous results.
How Striim Modernizes Data Platforms with Trusted, Real-Time Data
Striim’s platform lets enterprises transform disparate, disconnected environments into an integrated, low-latency architecture. With low-latency, schema-aware pipelines sending data from every critical source, AI can train, fine-tune, and reason over a consistent, governed dataset.
Armed with modern data platforms with Striim, enterprises get:
A single, consistent, governed dataset for AI training
Full interoperability across open data formats and diverse platforms
Real-time availability for AI and analytics
A scalable, future-proof data foundation, ready for AI
Benefit From a Modern, AI-Ready Data Foundation
Enterprise teams shouldn’t waste time fixing broken systems and wrestling with disparate data sets. With Striim, they can get rich, real-time data where it needs to be, and build a future-proof data foundation that’s always AI-ready.
Improved accuracy and effectiveness of AI models
By feeding AI systems with real-time, governed, and context-rich data, Striim ensures that models are always working with the freshest and most reliable inputs. This reduces data drift, improves prediction accuracy, and enables AI to deliver consistent, trustworthy outcomes across critical business scenarios.
What this means for you: Innovate faster with AI with faster-time-to value from AI initiatives.
Unlocked value from fragmented and legacy data Striim unifies siloed, outdated, and disparate systems into a single, AI-ready stream of governed data. This transformation allows enterprises to finally tap into valuable insights hidden in legacy platforms, enabling new analytics, automation, and AI-driven use cases that were previously out of reach.
What this means for you: Feed AI systems with a complete, unified data platform without leaving valuable data behind.
A solid foundation for new AI-driven initiatives With Striim’s intelligent streaming platform, organizations gain a future-proof data foundation that supports rapid experimentation and deployment of AI. By embedding governance and enrichment in motion, Striim equips teams to confidently build next-generation applications, from predictive analytics to agentic AI systems.
What this means for you: Gain confidence in a clean, consistent, AI-ready dataset.
Reduced compliance and operational risk with governed streams Data governance is embedded directly into the stream, ensuring sensitive fields are masked, validated, and compliant before they ever reach AI workloads. This lowers audit scope, reduces regulatory risk, and gives enterprises peace of mind that AI decisions are both safe and accountable.
What this means for you: Reduce the fear of regulatory risk and compliance breaches, with well-governed data.
Lowered operational cost by consolidating platforms and silos By replacing fragmented pipelines and multiple point tools with a single, enterprise-grade platform, Striim helps organizations cut complexity and reduce total cost of ownership. Teams spend less time maintaining brittle integrations and more time driving strategic AI initiatives, all while consolidating spend across systems and vendors.
What this means for you: Free up time for you and your team with reduced operational complexity and less data admin.
Data Modernization in Action: How Sky personalized the customer experience at scale with a unified, compliant dataset
Sky, one of Europe’s leading media and entertainment companies, needed to modernize their infrastructure to enhance the customer experience. They sought to streamline the onboarding process, optimize pricing, and tailor ad experiences for over 17 million customers.
The Striim Solution
With Striim, Sky can deliver real-time, well-governed pipelines into Kafka and unlock analytics in all their downstream systems.
Real-time personalization enabling tailored ads, dynamic pricing, and customer-specific offers
Accelerated onboarding made possible by rich customer profiles and history
Enforced opt-in/opt-out preferences across all systems for audit-ready compliance
Real-time pipelines sent to Kafka with analytics in BigQuery, Looker, and Tableau
The Results
Increased engagement with sub-second personalization
Higher customer lifetime value (CLV) through optimized pricing
Reduced time-to-value for new users
Improved customer loyalty with context-aware experiences
Lower risk of fines under GDPR, CCPA, HIPAA, and AI governance acts
If you’re relying on data that’s hours or even minutes old, you’re already at a disadvantage.
That’s why real-time Change Data Capture (CDC) platforms are gaining popularity. CDC solutions capture and stream changes from your source databases in real time, sending them to targets such as data warehouses and data lakes. This log-based, low-latency data streaming method avoids the overhead and delays of full data extractions, giving you faster analytics and helping you make decisions you can trust.
Traditional batch-based ETL (Extract, Transform, Load) processes can’t keep up. Batch jobs run on fixed schedules—often taking hours or even days to deliver data to its destination—leaving both you and your intelligent systems and AI applications a step behind. With CDC, pipelines stream updates in near real time to relational databases (like SQL Server or Oracle), data warehouses, data lakes, or other targets, so your organization’s leaders can react in the moments that matter most.
For these reasons, CDC tools have grown from a niche market technology to an essential solution. They’re used across industries and company sizes, from high-growth startups needing real-time analytics to large enterprises modernizing legacy systems. The right CDC strategy empowers you to keep up with exponential data growth, achieve sub-second latency, and modernize aging ETL architecture.
Which CDC platform is right for your enterprise? This guide compares leading CDC solutions—Striim, Confuent, Fivetran, Oracle GoldenGate, and Qlik Replicate—so you can evaluate which features, connector coverage, latency capabilities, and pricing will work best for your needs.
The Business Case for CDC
Businesses leveraging real-time operations experienced over 62% greater revenue growth and 97% higher profit margins compared to those operating at a slower pace, according to MIT/CISR research.
Revenue impact: Stale data holds enterprises like yours back—especially when it comes to leveraging advanced use cases such as personalization, fraud detection, and AI—stifling innovation and harming profitability. CDC changes the game by ensuring data relevance, allowing you to act on real-time insights and boost revenue.
Cost efficiency: CDC reduces the need for large-scale batch ETL jobs, cutting network bandwidth costs, minimizing computer usage, and lowering operational overhead for your data engineering teams.
Risk mitigation: Real-time CDC ensures business continuity by maintaining up-to-date backups, synchronizing multi-region deployment, and enabling rapid recovery in the event of full system failures.
What Is Change Data Capture (CDC)?
Change data capture (CDC) is a method for identifying and capturing changes—such as inserts, updates, and deletes—in your databases and replicating them downstream. Instead of relying on full reloads, CDC continuously streams only the new or modified data,
CDC tools capture changes in several ways. Query-based and trigger-based approaches exist, but they can be intrusive and place additional load on your source systems. Log-based CDC is the most robust and scalable method because it reads directly from database transaction logs, creating low latency, data freshness, and non-intrusiveness.
Adopting log-based CDC lets you synchronize data in near real time without impacting production workloads. This makes it possible to act on data the moment it’s created, powering operational dashboards, advanced analytics, machine learning models, customer-facing applications, and event-driven use cases. Keep reading to learn common CDC use cases and discover the key features to look for in a CDC tool.
Data Integration Glossary
Change data capture (CDC) identifies and streams data changes—such as inserts, updates, and deletes—from source systems in near real time. CDC enables continuous data synchronization for analytics, AI, and operational applications without full data reloads.
Exactly-once delivery guarantees each data change is replicated and processed only once, preventing duplicates or data loss. This is a vital feature for accurate CDC pipelines.
Event-driven architecture (EDA) is an architectural paradigm that enables intelligent systems to react to data change events captured by CDC, enabling loosely coupled, real-time, and scalable applications and analytics workflows.
Extract, transform, load (ETL) is a batch data process that extracts data from sources, transforms it for consistency and quality, and loads it into data warehouses. Unlike CDC’s real-time streaming, ETL often works on scheduled batches for business intelligence (BI) workloads.
Fault tolerance in CDC solutions ensures uninterrupted data replication despite hardware or network failures, using features like data buffering, retries, and failover to prevent data loss.
Kafka is an open source distributed streaming platform. It’s often used as a CDC target or messaging layer, providing scalable, fault-tolerant, real-time data pipelines for event-driven architectures.
Latency in CDC platforms is the delay or slowdown between a data change in the source system and its reflection in the target system. Sub-second latency is essential for real-time analytics and rapid decision-making.
Log-based capture monitors database transaction logs to detect data changes with minimal source impact. It is the preferred CDC method for real-time, scalable streaming because it doesn’t query tables directly and can track complex database changes, such as multi-table transactions and bulk updates.
Multi-region sync replicates CDC data across geographic regions or data centers, enabling global availability, disaster recovery, and low-latency access for distributed users.
Online transaction processing (OLTP) systems handle high volumes of fast, transactional data changes. CDC tools capture these changes in real time to keep analytics and operational systems synchronized.
Operational dashboards visualize real-time data and key metrics fed by CDC streams. They help teams monitor live business processes, detect anomalies, and make immediate data-driven decisions.
Schema evolution allows CDC systems to adapt automatically to changes in source data structure, such as newly added columns, keeping data flowing and preventing pipeline breaks.
Service level agreements (SLA) set performance commitments for CDC tools, including replication latency, uptime, and error rates. SLAs ensure that data synchronization meets business needs for reliability and timeliness in analytics and operational workflows.
Stream processing continuously ingests and analyzes CDC data in real time, supporting immediate insights, alerts, and operational decisions without waiting for batch jobs.
Streaming enrichment enhances raw CDC data in real time by adding context—such as lookup values, aggregations, or business rules—before delivering it to target systems. This reduces downstream processing and enables faster, more actionable insights from live data streams.
Trigger-based capture uses database triggers to record data changes as they happen. While precise, this CDC method can increase source system load and may not scale well in high-volume or latency-sensitive environments.
Why You Should Use CDC Tools
Legacy data architectures and siloed information can slow down your enterprise’s ability to use real-time analytics and leverage AI. Change data capture (CDC) tools break down these barriers by continuously streaming changes from source systems to cloud data warehouses (Redshift, Snowflake, BigQuery), data lakes, streaming platforms, and data lakehouses (Databricks).
Pain Points Addressed by CDC Tools
With a modern data architecture backed by CDC, you can solve these longstanding challenges.
Legacy Architectures Can’t Support Modern Data Demands. Traditional batch-based ETL pipelines, siloed systems, and cobbled-together point solutions (such as Debezium + Kafka + Flink) introduce complexity, delay innovation, and hinder AI adoption. CDC tools modernize data pipelines by giving your enterprise continuous, trusted, and enriched data.
AI Initiatives Are Stalled by Stale or Inaccessible Data. Current enterprise data infrastructure fails to deliver the velocity or reliability required for advanced use cases. Real-time CDC pipelines remove data silos and deliver continuous, fresh data, giving your AI models, generative AI applications, and real-time decisioning models enriched and trusted data with sub-second latency.
Data Teams Are Overburdened by Tool Sprawl and Maintenance. Managing and maintaining separate CDC, transformation, and delivery tools strains your engineering resources and overwhelms your teams. Tool sprawl also slows project timelines and increases total cost of ownership (TCO). Best-in-class change data capture platforms consolidate CDC with streaming, delivery, and observability, delivering faster time to value and reducing TCO.
Inconsistent governance increases risk. When sensitive data flows through pipelines without real-time direction, masking, or lineage, it creates audit gaps and non-compliance with frameworks like HIPAA, GDPR, and SOC2. CDC platforms provide integrated masking, lineage tracking, and anomaly detection, enriching your enterprise’s data compliance and governance strategies.
Business stakeholders lack timely insights. Missed Service Level Agreements (SLAs), failed ETLs, and long recovery windows create blind spots across your finance, operations, and customer experience teams. Modern data streaming tools provide real-time dashboards, replacing once-a-day refreshes with fresh updates.
Digital transformation efforts carry operational risk. Unreliable, poorly integrated batch tools hinder cloud migrations and platform re-architecture initiatives. Reliable, observable CDC tools enable zero-downtime cloud migrations and multi-cloud synchronization without disrupting your daily operations.
Data accessibility and freshness are compromised. Change data capture platforms keep your enterprise’s data fresh, accurate, and available, building trust in analytics and helping you accomplish aspirational mission-critical initiatives like fraud detection, and hyper-personalization.
Reducing Risk, Maintaining Compliance
Managing your organization’s risk profile in today’s fraught cybersecurity environment and keeping up with regulations are two challenges that keep IT teams up at night.
Understand why real-time data is an essential element for both.
Key Benefits of CDC Tools
As the engine behind modern streaming data pipelines, CDC platforms fundamentally shift your organization’s ability to put data to good use. Rather than simply moving data, CDC unifies it across your organization, creating real-time intelligence that drives faster decisions and impacts every part of the business.
Greater success with AI and analytics initiatives: AI models rely on the freshest possible data. The longer the delay or lag, the less relevant the contributions of an AI system. With best-in-class CDC platforms, enterprises can power real-time analytics and sophisticated, AI-driven applications from the same data stream, deploying LLMs that actually work.
Reduced complexity and lower TCO: Maintaining separate tools for CDC, stream processing, enrichment, and delivery adds cost and complexity. By consolidating these capabilities into a single platform, you can reduce engineering overhead, cut licensing costs, and ease operational burdens, freeing up your teams to focus on meaningful projects.
Improved governance and compliance posture: Enterprise-ready CDC solutions will support your organization’s governance requirements. This includes implementing access controls, maintaining detailed audit trails, and encrypting data both in transit and at rest. Platforms like Striim include Sentinel AI and Sherlock AI to spot and secure sensitive information as it moves, protecting it from unauthorized use. These built-in governance features also make it easier for your enterprise to pass audits for standards such as HIPAA, GDPR, and SOC 2.
Stronger business agility and scalability: CDC tools enable your teams to launch new data products, build AI pipelines, and deliver live operational insights quickly, without rebuilding infrastructure or compromising resilience. CDC provides the agility to scale data operations and keep up with the growth of your business.
Trusted, always-on data for leadership and frontline teams: Trusted, always-on data changes the way everyone works across your enterprise. Key stakeholders can monitor KPIs, track consumer behavior, assess operational risks in the moment, and make critical decisions with confidence.
The Foundation for AI
Is your data architecture limiting your ability to effectively implement generative AI? Most enterprises (74%) struggle to implement AI effectively because they lack real-time, trusted data. CDC changes the equation by fundamentally transforming how data flows through your business, giving you the foundation for AI.
Common Use Cases
CDC tools can power a wide range of operational and analytical use cases, from real-time analytics to application-level intelligence. By delivering a continuous stream of fresh data, CDC solutions give you new ways to move faster and gain deeper insights.
Streaming transactional data from OLTP to cloud data warehouses: With CDC, you can stream real-time transactional updates directly from online transaction processing (OLTP) systems, such as relational operational databases, into your cloud data warehouses. Log-based CDC preserves ACID transaction integrity while avoiding the performance impact of repeated full-table queries. This ensures your downstream analytics platforms and BI tools always work with the freshest possible data.
For example, global payments company Clover consolidated its fragmented infrastructure by streaming data from 23 MySQL databases into Snowflake in real time, reducing operational complexity and empowering developers to take on higher-value tasks.
Real-time fraud detection and personalization using CDC and streaming: When you need to react instantly, whether to catch fraud, personalize customer experiences, or right-size inventory, CDC combined with in-flight stream processing gives you an edge. You can merge transactional, behavioral, and third-party data in real time, apply continuous queries, and trigger actions as soon as anomalies appear.
In banking, this might mean automating fraud prevention by flagging suspicious transfers before they complete. In retail, it could mean achieving personalization at scale by adjusting offers based on a customer’s live browsing behavior.
Zero-downtime cloud migration or multi-region sync: Downtime during a migration breaks customer experiences, increases compliance risks, and can even cost revenue. With CDC, you can replicate on-prem databases to cloud targets without interrupting live applications. After the initial load, CDC keeps both on-prem and cloud-based systems in sync until cutover, ensuring no data is lost and no service is disrupted. This same principle applies when you need multi-region or multi-cloud synchronization. CDC keeps geographically distributed systems in lockstep to support global scalability and disaster recovery strategies.
Triggering workflows and alerts based on specific change events: CDC lets you turn raw change data into action. By defining rules or conditions on change streams, you can automatically send alerts, update downstream systems, or kick off remediation steps when specific changes occur. This event-driven approach underpins fraud detection, IoT monitoring, operational dashboards, and more, essentially turning your data pipeline into a live control system for your business.
Rethinking Customer Experiences
Real-time analytics are remaking the customer experience. Companies can now use data to transform the way they understand user preferences and deliver on those priorities.
Learn how some businesses are increasing first-call resolutions, reducing repeat calls, and boosting customer ratings.
Top Change Data Capture Tools Compared
CDC tools vary widely in architecture, capabilities and, naturally, maturity. To choose the right one, you need to understand the key features that set today’s most effective CDC solutions apart.
Striim
Key features
Best-in-class real-time CDC capabilities with sub-second replication, preserving data integrity and supporting high-throughput workloads
Built-in SQL-based stream processing for transforming, filtering, enriching, and joining data in motion
An all-in-one platform that eliminates tool sprawl, lowers TCO, and accelerates time to value
Best fit
Large, data-intensive enterprises in financial services, retail/CPG, healthcare/pharma, hospital systems, travel/transport/logistics, aviation, manufacturing/energy, telecommunications, technology, and media
Free trials available for Striim Developer (perfect for learning and small-scale use cases) and Striim Cloud (fully managed, horizontally scalable streaming)
Contact sales for pricing on Striim Platform (self-hosted deployments on your infrastructure)
Case studies
Discovery Health Reduces Data Processing Latency From 24 Hours to Seconds with Striim. Read more.
American Airlines Powers Global TechOps with a Real-Time Data Hub. Read more.
UPS Leverages Striim and Google BigQuery for AI-Secured Package Delivery. Read more.
Confluent
Key features
Broad CDC connector ecosystem, including log-based and query-based connectors (Debezium, JDBC, and more)
Publishes database changes into Apache Kafka event streams for downstream processing
Stream governance and tooling for secure, compliant, event-driven CDC pipelines
Best fit
Organizations that want to be based on Kafka
Pros
Real-time data propagation for analytics and automated workflows
Enterprise-grade governance and pipeline management
Supports databases, mainframes, and cloud deployments with rich connector choices
Cons
Costly pricing structure with usage-based charges that can stack up quickly
Requires deep Kafka expertise and complex setup
Operational overhead and a fragmented ecosystem of unnecessary add-ons
Users reported throughput issues with certain CDC connectors such as Oracle
Change data capture (CDC) works by continuously monitoring your databases for changes, capturing them instantly, and supplying them as event streams to other systems or platforms. Whenever one of your users acts, the database logs it as an INSERT, UPDATE, or DELETE event. A CDC platform connects directly to your database to identify these changes in real time.
You can detect changes in different ways, including by polling tables for timestamp modifications, triggering database events when updates occur, or reading directly from transaction logs.
Each approach comes with trade-offs in performance, delay, and complexity. Evaluating these differences is essential to selecting the most suitable CDC tool for your organization.
Alternative CDC Methods
Log-based CDC is the most reliable and scalable approach, but other methods exist for capturing database changes. Know the pros and cons of these alternative so you can decide what’s best for your business.
Query-Based CDC
Also known as polling-based CDC, this method involves repeatedly querying a source table to detect new or modified rows. It is typically done by looking at a timestamp or version number column that indicates when a row was last updated.
While simple to set up, query-based CDC is highly inefficient. It puts a constant, repetitive load on your source database and can easily miss changes that happen between polls. More importantly, it can’t capture DELETE operations, as the deleted row is no longer there to be queried. For these reasons, query-based CDC is rarely used for production-grade, real-time pipelines.
Trigger-Based CDC
This method uses database triggers—specialized procedures that automatically execute in response to an event—to capture changes. For each table being tracked, INSERT, UPDATE, and DELETE triggers are created. When a change occurs, the trigger fires and writes the change event into a separate “history” or “changelog” table. The CDC process then reads from this changelog table.
The main drawback of trigger-based CDC is performance overhead. Triggers add computational load directly to the database with every transaction, which can slow down your source applications. Triggers can also be complex to manage, especially when dealing with schema changes, and can create tight coupling between the application and the data capture logic. This makes them difficult to scale and maintain in high-volume environments.
Both query-based and trigger-based CDC can work in limited or small-scale use cases. But most enterprises rely on log-based CDC for its many benefits.
Log-Based Change Capture
Log-based CDC is the gold standard for modern data integration. This technique reads changes directly from your database’s native transaction log (e.g., the redo log in Oracle or the transaction log in SQL Server). Because every database transaction is written to this log to ensure durability and recovery, it serves as a complete, ordered, and reliable record of all changes.
The key advantage of log-based CDC is its non-intrusive nature. It puts almost no load on the source database because it doesn’t execute any queries against the production tables. It works by “tailing” the log file, similar to how the database itself replicates data. Log-based CDC is highly efficient and scalable, capable of capturing high volumes of data with sub-second latency. Some log-based CDC tools come with the ability to analyze different tables to ensure replication consistency.
This reliability and low-impact approach is why modern, enterprise-grade streaming platforms like Striim are built around a scalable, streaming-native, log-based CDC architecture.
Real-Time Data Delivery
Your CDC tool should move data instantly to downstream systems, whether it’s your analytics platform, operational dashboard, or event-driven applications. By streaming changes as they occur, you can power analytics, migration workflows, synchronization, and other downstream processes without waiting for batch schedules.
Broad Source and Target Support
Choose a platform that connects to all the places you need, including relational databases, NoSQL stores, cloud data warehouses, data lakes, messaging systems, and more. A platform with broad support makes it easy for you to plug CDC into your current tech stack, connect to new systems as your needs grow, and stay flexible for whatever comes next.
Schema Evolution Handling
Your data isn’t static, and your pipelines shouldn’t be, either. Columns get added, types change, tables get renamed. You need a CDC solution that adapts without breaking your data flows. Modern platforms detect schema changes, propagate them downstream, and notify you when schemas drift, keeping your pipelines safe and your data reliable.
Built-In Stream Processing
Many CDC tools just capture changes. Advanced platforms take it a step further with SQL-based stream processing that lets your users filter, transform, enrich, and join data in motion—before it ever reaches a warehouse, dashboard, or operational system. Think of it as a real-time data refinery, delivering analytics-ready streams that accelerate time to insight.
Fault Tolerance and Exactly-Once Delivery
You need your data to be reliable every time you query it. A strong CDC platform ensures exactly-once delivery, using checkpoints and automatic error recovery to prevent duplicates or missing updates. This kind of reliability is critical for finance, compliance, and other sensitive workloads.
Monitoring and Observability
The best CDC tools give you dashboards, logs, metrics, and alerts so you can track throughput, latency, schema changes, and errors. With full visibility, you can monitor pipeline health so you can troubleshoot faster, prevent issues, and stay ahead of problems.
Deployment Flexibility (Cloud, On-Prem, Hybrid)
Your CDC platform should work where you work, whether it’s on-premises, in the cloud, or across a hybrid setup. Seek platforms that can adapt to your infrastructure so you can scale, re-architect, or migrate without having to replace your platform later.
Change Data Capture Tools in Action
Track database updates, inserts, and deletes in real-time with Change Data Capture to power data replication and migration. Learn more about CDC tools and use cases.
How to Choose the Right CDC Tool for Your Needs
With so many options available, navigating the CDC vendor landscape can be challenging. Narrow the field and create a focused shortlist of viable vendors by looking closely at these three areas.
Evaluate Total Cost of Ownership (TCO)
Determining the TCO goes beyond licensing fees. You should also consider the engineering resources you’ll need to build and maintain CDC pipelines, the need for third-party tools like Kafka or stream processors, and the platform’s ability to scale up or across clouds without costly re-architecting.
Look for Key Features of Modern Platforms
Seek solutions that embrace the features that matter most to your enterprise, including these must-haves:
Log-based change capture for efficient, low-impact extraction of database changes
Real-time data delivery to keep analytics and applications continuously updated
Broad source and target support, enabling flexible integration across diverse environments
Schema evolution handling to adapt automatically as data structures change
Built-in stream processing for filtering, transformation, and enriching data in motion
Fault tolerance and exactly-once delivery, ensuring data integrity without duplicates or loss
Monitoring and observability to track pipeline health and resolve issues quickly
Deployment flexibility across cloud, on-premises, and multi-cloud environments
These features will help you choose a robust, scalable CDC platform that will generate meaningful ROI.
Ask Strategic Questions
Once you identify the best CDC solutions, it’s time to evaluate vendors. Focus on these critical errors to ensure the solution can meet your technical requirements and business goals.
Data source and target compatibility: Does the tool support log-based CDC for your specific database version? What about future migration targets?
Latency and throughput underload: Can the solution handle high-volume changes in near real time without data loss or degradation?
Streaming enrichment: Do you need to transform or filter data in flight?
Error handling and recovery: What happens when a target is unreachable? Can the CDC platform retry, checkpoint, and resume?
Operational visibility: How easy is it to monitor, alert, and audit pipeline performance?
Security and compliance: Is the CDC platform compliant with your governance model (SOC2, HIPAA, etc.)? Can it ensure data movement at scale?
Asking these questions up front will help you find CDC platforms that meet your infrastructure needs, creating a smoother implementation.
Striim: One Platform for CDC, Streaming, and Beyond
Leading enterprises need a unified CDC platform that combines real-time data capture with in-stream processing and reliable, at-scale delivery. Striim is the only platform providing this end-to-end functionality in a single, enterprise-grade solution. With Striim, your organization gets:
An all-in-one platform: Striim consolidates CDC, streaming, delivery, and observability into a single platform. You get faster time to value and lower TCO, while your engineers are freed from the rigors of having to maintain multiple, cobbled-together systems.
Log-based CDC with sub-second latency: Striim’s log-based CDC extracts changes directly from database transaction logs without impacting production systems, supporting high-throughput workloads, and delivering real-time analytics and cloud sync at scale.
Built-in stream processing: Unlike other CDC tools that just capture change data, Striim you’re your users transform, filter, enrich, and join data in motion using SQL-based processing.
Don’t settle for stale data and fractured data workflows. See how the world’s leading enterprises use Striim to power their business with real-time insights.
The race is on. Enterprises and companies of all sizes are running full speed towards the transformative benefits of AI.
According to McKinsey, AI received $124.3 billion in equity investment last year alone, and 92% of executives are planning to invest more over the next three years.
To get AI-ready, companies need a trusted, real-time data layer that’s accessible to AI systems. The first step is getting data where it needs to be for AI activation: out of legacy, on-premises systems and into scalable, secure, cloud storage.
Cloudy Skies: The Challenges Behind Migrating to AI-Ready Cloud Infrastructure
As we laid out in our overview of AI-readiness, cloud migrations are high-stakes operations with significant risks and challenges, especially for large enterprises migrating huge amounts of data.
Data Downtime: Missing or delayed data can mean critical failures, broken customer experiences, or incorrect decisions. Organizations need data to operate AI effectively and can’t afford going without data, even for a short while.
Keeping Data Consistent: Migration to the cloud is about data movement; but it’s also about delivering data in clean, consistent formats. During transition, it’s crucial that data is transferred in well-structured, interoperable formats that AI can read and act on.
Complex, Diverse Integrations: In the enterprise context, moving data is rarely a case of getting “from point A to point B”. Migrations often involve a mismash of sources and targets that must be navigated to ensure complete, secure integration. Any disruption in this process will delay AI adoption efforts.
How Striim Delivers Data Safely and Securely, With Zero Downtime
Combining best-in-class real-time CDC, schema evolution, and bi-directional sync, Striim ensures AI gets the data it needs, where it needs it, by replicating data instantly from legacy systems. Cloud Migrations with Striim results in:
Faster time-to-value for migration and modernization
A step closer to an AI-ready foundation with trusted cloud data.
Data integrity maintained, with continuous, consistent replication across hybrid and multi-cloud.
Zero downtime and data loss during cutovers.
Reduced silos by enabling unified access for AI and analytics.
Benefit From Stress-Free Migration
By partnering with Striim, enterprise teams can focus on what really matters. Rather than worrying about how to move their data, they can put their efforts towards using it: building AI capabilities and advanced analytics.
Lower migration and modernization riskStriim reduces the complexity and risk of cloud migration and platform modernization by providing real-time, governed data streams that ensure consistency, minimize downtime, and safeguard compliance throughout the process.
What this means for you: Less stress and complexity for engineering and IT teams.
Accelerated path towards innovation and AI adoptionBy delivering AI-ready pipelines with sub-second latency and built-in governance, Striim enables enterprises to rapidly operationalize AI initiatives, shortening the time from experimentation to production and accelerating innovation.
What this means for you: Innovate faster and free up your time from manual synchronization tasks.
New revenue streams via AI-driven productsStriim powers new business opportunities by feeding AI systems with trusted, real-time data, enabling enterprises to launch AI-driven products and services that create additional revenue streams and competitive differentiation.
What this means for you: Gain buy-in from business stakeholders and expand your operations with proven ROI.
Strengthened compliance with governed dataWith in-stream governance, continuous validation, and secure data zones, Striim ensures that enterprises maintain compliance with GDPR, HIPAA, and emerging AI regulations—protecting sensitive data while enabling confident AI adoption.
What this means for you: Gain confidence in your governed data as a trusted foundation for AI initiatives.
Improved competitive positioning with faster AI deployment cyclesStriim’s real-time intelligence platform allows organizations to deliver AI use cases into production faster, giving them an advantage in rapidly evolving markets where speed, accuracy, and compliance directly translate into stronger competitive positioning.
What this means for you: Build momentum towards agentic AI and operationalize your AI projects quickly.
Cloud Migration in Action: How Macy’s modernized its data infrastructure to deliver seamless, real-time inventory experiences
Like many retailers, Macy’s came up against legacy systems that failed to deliver data at the pace they needed to deliver for their customers. Their data was scattered across DB2 mainframes and Oracle databases, siloed and inconsistent. That meant higher costs, slower app development, and frustrating inventory mismatches between online and in-store shopping.
The Striim Solution
Macy’s leveraged Striim’s real-time data integration capabilities to bridge the gap between legacy systems and the cloud. By continuously replicating data from mainframe and Oracle into Google Cloud services like Cloud Spanner, Pub/Sub, and BigQuery, Macy’s gained:
A single, trusted view of data across all channels.
High availability with built-in resilience and disaster recovery.
Scalability on demand to handle holiday traffic spikes without missing a beat.
The Results
Lower costs by moving off expensive mainframes.
Faster operations with real-time inventory and order management.
Better customer experiences online and in stores.
Accelerated innovation with a unified cloud data foundation.
A stronger digital backbone for AI, analytics, and future transformation.
“Striim helped us modernize our platforms and achieve real-time visibility, syncing our inventory data to prevent out-of-stock or surplus situations during peak holidays like Black Friday and Cyber Monday.” — Neel Chinta, IT Manager at Macy’s
Artificial Intelligence (AI) has shifted from hype to mandate.
In 2023, enterprises were experimenting with pilots. By 2024, AI spending had surged sixfold to $13.8 billion. In 2025, AI is no longer optional—it’s a board-level directive. Yet despite the urgency, 74% of companies still struggle to achieve and scale value from AI. Most face the same blockers: fragmented data across legacy and cloud systems, stale insights arriving hours too late, and a lack of governed, trusted data streams that AI can safely use in real time.
This is where Striim comes in.
Striim powers real-time intelligence for enterprise AI, providing the intelligent data infrastructure and event-driven streaming needed to operationalize AI at scale. Unlike batch ETL tools, open-source DIY stacks, or ingestion-only SaaS vendors, Striim delivers sub-second, governed data streams that are AI-ready from day one.
And crucially: Striim’s process is not just part of the AI journey—it is the AI journey. We meet enterprises where they are, guiding them through the four stages to operationalize AI:
Let’s walk through each stage and see how industry leaders are already using Striim to move from AI ambition to execution.
Stage 1: Cloud Migration & Adoption
For agentic AI to deliver to its full potential, it needs to live where innovation happens: the cloud. But moving massive volumes of critical data from legacy, on-premise systems is a high-stakes operation where downtime isn’t an option and data integrity is crucial.
The Challenges of Moving to the Cloud
Data Downtime: Enterprises cannot risk downtime, where even minutes of missing data could break AI responses and lead to poor outcomes for customers, partners, and the bottom line.
Data Inconsistency: Nor can enterprises afford data inconsistency during cutovers. Data cleaning or reformatting on arrival can be costly, inefficient and disruptive to AI systems.
Complex Integrations: Stitching together legacy systems, cloud platforms, and modern AI applications often requires brittle, custom-built pipelines that can’t support AI at scale.
How Striim Delivers Best-In-Class Cloud Migration
With industry-leading change data capture (CDC), in-stream transformations, and sub-second latency, Striim is best-in-class when it comes to getting enterprise data from legacy systems into AI-ready cloud environments.
Striim’s fast, low-risk cloud migration lets enterprises focus on what they do best: innovating for their customers and delivering value.
Migrating to the Cloud with Striim Gives You:
Lower migration and modernization risk through resilience and governance.
Faster innovation and AI adoption with real-time, cloud-ready data.
New revenue streams via AI-driven products.
Strengthened compliance with governed data.
Enhanced competitive edge with faster AI deployment cycles.
Curious to see a real-world example of cloud migration with Striim?
With data now in the cloud, the next critical step is modernizing the underlying platform to make that data useful for AI. The goal is to create a unified architecture, like a data lakehouse, that acts as a single source of truth.
The Challenges of Fragmented, Legacy Systems
Data Silos: For enterprises, data is scattered across disconnected systems and siloed teams. This holds companies back from getting the unified view required for advanced analytics and AI.
Data Fragmentation: Even when accessible, data is often fragmented across different formats and structures.
Legacy Systems: Rigid legacy systems can’t support the low-latency, high-volume data streams essential for real-time AI and analytics, creating a bottleneck for innovation.
How Striim Delivers a Modern, AI-Ready Data Foundation
With continuous ingestion from every source, automated schema handling, and in-stream transformations, Striim ensures data is always AI-ready. The platform’s elastic scaling and interoperability with open data formats provide a truly future-proof data foundation.
With Striim, enterprises can stop wrestling with fragmented data and start building next-generation AI applications.
Modernizing with Striim Brings:
Improved accuracy and effectiveness of AI models.
Unlocked value from fragmented and legacy data.
A solid foundation for new AI-driven initiatives.
Reduced compliance and operational risk with governed streams.
Lowered operational cost by consolidating platforms and silos.
Want to learn more about a real modernization success with Striim?
AI and agentic systems need fresh, real-time data. By the time information arrives in hourly or daily batches, it’s already stale, and the window of opportunity for your AI to act has closed.
The Challenges of Stale Data
Delayed Insights: Traditional analytics rely on batch processing, meaning insights are generated from data that is hours, or even days, old. This prevents AI models from acting on what is happening in the business right now.
Missed Opportunities: The lag between when an event occurs and when it is analyzed results in missed opportunities. Businesses cannot instantly respond to changes in customer behavior, market shifts, or operational issues, limiting their agility.
Reactive Decision-Making: Batch analytics forces organizations into a reactive posture, where they can only look back at what has already occurred. This limits the ability of AI to be truly predictive and respond to live events as they unfold.
How Striim Delivers Real-Time Analytics
With ultra-low latency in-stream processing, advanced streaming analytics, and built-in anomaly detection, Striim delivers sub-second insights directly from the data stream. The platform provides full pipeline observability and feeds context-rich, governed streams into AI systems for instant action.
With Striim, enterprises can stop making decisions based on stale data and start acting on live intelligence.
Analytics with Striim Delivers:
Improved operational efficiency through faster actions.
Competitive advantage via instant responses to market and customer shifts.
Reduced risk with real-time anomaly detection and intervention.
Enhanced customer experiences with adaptive, AI-driven services.
Continuous innovation through live insights.
Curious to learn what Analytics with Striim looks like in action?
AI and agentic systems have the potential to transform virtually every industry. But to be in a position to benefit from AI, enterprises need a governed, trusted, real-time data foundation, as well as the means to make this data available to agents in a safe, non-disruptive environment.
The Challenges of Running AI on a Shaky Data Foundation
Production Data Risk: Granting AI agents direct access to live production databases and systems creates significant security and operational risks.
Lack of Trust & Verifiability: Without a governed, verifiable, and continuously validated data source, enterprises cannot trust AI agents to make autonomous decisions.
Data Governance & Compliance: Deploying autonomous agents that interact with sensitive enterprise data creates major governance and compliance hurdles. It becomes incredibly complex to ensure adherence to regulations like GDPR, HIPAA, and the EU AI Act when agents have direct access to production data.
How Striim Enables Safe, Scalable, Intelligent AI
Striim’s platform was built to solve the core challenge of trust and safety in agentic AI.
Striim embeds a suite of AI agents directly into the data stream to make data safe, intelligent, and AI-ready. Governance agents like Sherlock AI & Sentinel AI automatically discover and mask sensitive data, Euclid prepares data for RAG architectures by transforming it into vector embeddings, and Foreseer detects and predicts anomalies directly in the data stream.
With MCP AgentLink, continuous, real-time, cleansed, and protected data replicas give agents access to fresh, accurate data without exposing production systems. This means enterprises can leverage MCP-ready, event-driven architectures and take full advantage of autonomous, agentic systems.
With Striim, enterprises can move from AI ambition to execution, deploying agents with confidence. They have the power to scale intelligent operations safely, knowing that their data is governed, their production systems are protected, and their AI-driven outcomes are built on a foundation of trust.
Agentic AI with Striim Delivers:
Faster AI operationalization with trusted, compliant pipelines.
Strengthened compliance with GDPR, HIPAA, and the EU AI Act.
Enterprise-wide trust in AI-driven outcomes.
Reduced compliance costs by automating data governance.
Accelerated ROI with production-grade, scalable AI deployments.
Take the next step towards AI readiness, with Striim
The four stages—Cloud Migration, Data Modernization, Analytics, and Agentic AI—represent critical steps on this path. Striim provides the unified platform to navigate each stage, transforming fragmented, risky data operations into a secure, real-time engine for innovation.
The age of AI is not just coming; it’s already here. With the right data infrastructure, your enterprise won’t just be ready for it—you’ll be leading the charge.
The pressure to deliver value from data is on. Across every industry, the volume of data is exploding while the window for making critical decisions is shrinking. This pressure, intensified by the rise of artificial intelligence, has catalyzed business leaders to rethink their data strategy. Increasingly, they’re seeing legacy data architectures, once a source of strength, have become a significant obstacle to growth.
The traditional approach—relying on siloed systems and periodic, batch processing for business intelligence—no longer suffices. An AI model that needs to detect fraud or optimize a customer experience in milliseconds can’t wait for a weekly report. This gap between the potential use of data and outdated data infrastructure risks putting the business at a competitive disadvantage, slowing down innovation and hindering AI-readiness.
That’s why having a coherent “data strategy” has become critical. But the term is often misunderstood. It’s not just a technical roadmap or an investment in new dashboards; it’s a blueprint that aligns your data initiatives with your core business goals. This article will break down what a modern data strategy entails, helping you build a practical plan for a faster, more intelligent future.
What is a Data Strategy?
A data strategy is a cohesive plan that defines how you will capture, store, manage, share, and use your data to achieve your business objectives.
It’s not just a technical document or a roadmap written by your CDO. A strong data strategy connects your data-related activities directly to measurable outcomes, like increasing revenue, improving operational efficiency, or creating better customer experiences.
Historically, data was used for periodic business intelligence—essentially a rear-view mirror look at what had already happened. Now, the focus has shifted to the future: a continuous flow of data insights that enables agile, forward-looking decision-making. In this environment, a robust data strategy has become essential. Without it, you simply cannot implement advanced, real-time data use cases like personalization or predictive analytics.
However, creating and executing a successful data strategy is fraught with challenges. Many companies struggle with:
Data silos: Information trapped in disconnected systems across different departments.
Outdated data infrastructure: Legacy, batch-based infrastructure that introduces costly delays.
Data volume and diversity: The sheer scale and variety of data from countless sources, from IoT sensors to customer applications.
Why You Need a Data Strategy (Even If You Think You Have One)
You might think you already have a data strategy. You’ve invested in dashboards, built reporting tools, or set up data pipelines. But without a central strategy, these efforts are at risk of becoming fragmented, reactive, or built on outdated assumptions.
Today’s data landscape has fundamentally changed. The rise of AI, exploding data volumes, and the demand for real-time responsiveness require a more integrated, forward-looking approach.
A modern data strategy brings clarity to your vision for data in a few key ways:
Faster, more confident decision-making by dramatically reducing data latency.
A unified view of the business that breaks down silos and creates a single source of truth.
AI and machine learning readiness powered by clean, timely, and trustworthy data.
Streamlined compliance and security with governance embedded directly into data flows.
Improved customer experiences through real-time personalization and responsiveness.
Without a strong strategy, you’ll run the risk of slow insights, duplicated efforts, and shadow IT processes. More importantly, you miss critical opportunities that depend on real-time action.
What to Build: The Key Components of a Data Strategy
While every company’s data strategy will look different, the most effective plans share common traits. Think of these as the essential pillars that provide the structure for execution and growth.
Data Architecture and Infrastructure: This is the foundation of your strategy. It defines the systems, tools, and technologies you will use to store, move, and process data. This includes your databases, data warehouses, data lakes, and the pipelines that connect them.
Data Governance and Security: These are the policies, rules, and standards that ensure your data is accurate, consistent, and secure. It answers critical questions: Who owns the data? Who can access it? How is it protected?
Data Integration and Interoperability: This component focuses on breaking down silos. It outlines how you will connect disparate data sources—from legacy systems to modern cloud apps—to create a unified view and enable seamless data flow.
Analytics and Insight Delivery: Data is only valuable if it leads to action. This part of your strategy defines how you will analyze data and deliver data insights to decision-makers, whether through dashboards, reports, or directly into AI-powered applications.
People and Process Enablement: Technology alone isn’t enough. This component addresses the human side of your data strategy, including upskilling your teams, fostering a thriving data culture, and defining the processes for data management.
Performance and Success Metrics: To ensure your strategy is delivering value, you must define how you will measure success. This involves setting clear KPIs that align with your business objectives, such as reducing data latency, improving decision speed, or increasing revenue from data-driven products.
How to Build it: The Core Pillars of a Future-Ready Data Strategy
The components represent what you need to build, but the pillars below illustrate how you need to think. They are the principles that ensure your data strategy is not only relevant today but resilient and adaptable for the future.
Strategic Alignment: Drive Tangible Business Value
Think of this pillar like a “so what” test for your data. Your data initiatives should tie directly to business outcomes. Instead of collecting data for its own sake, every project should answer the question: “How will this help us drive revenue, reduce costs, or improve our customer experience?” This alignment ensures that your investments in data return measurable returns.
Unified Data Ecosystems: Break Down Data Silos
A fragmented data landscape leads to a fragmented view of your business. The goal is to create a unified ecosystem where data flows seamlessly between systems. This doesn’t necessarily mean storing everything in one place, but it does require a real-time integration layer that connects your databases, cloud applications, and analytics tools into a cohesive whole.
AI and ML Readiness: Fuel Intelligent Operations with High-Quality Data
AI and machine learning models are only as strong as the data they’re fed. A future-ready strategy prioritizes the delivery of clean, timely, and well-structured data to power these intelligent systems. This means moving beyond slow, batch-based processes and architecting for data quality, ensuring a continuous flow of reliable data that can fuel real-time use cases.
Robust Governance and Trust: Balance Innovation with Security
Data governance isn’t a roadblock; it’s an enabler of trust. A modern approach embeds security, compliance, and ethical considerations directly into your data pipelines. By automating data governance, you can empower your teams to innovate with confidence, knowing that robust guardrails are in place to protect sensitive information and ensure regulatory compliance.
Data Culture and Literacy: Empower All Teams with Accessible Data
The most powerful data strategy is one that is embraced by all business units, not just the data team. This requires a cultural shift toward democratizing data, making it accessible and understandable for employees across all functions. Investing in data literacy programs and self-service analytics tools empowers your entire organization to make smarter, data-informed decisions.
How to Activate Your Data Strategy (Tips and Best Practices)
Creating the data strategy is the (relatively) easy part. The real work, and subsequent value, comes when you put it into practice. But activating your data strategy is no easy feat. Companies often get stalled at this stage by data access delays, persistent silos, and difficulty getting buy-in from stakeholders.
Here are some best practices to help you move from blueprint to real-world impact.
Break Down Data Silos with Real-Time Integration
Integration isn’t just about connecting systems—it’s about letting them communicate continuously. Use real-time data integration to ensure that when data is updated in one system (like a CRM), it’s instantly available and reflected in others (like your analytics platform or marketing automation tool). This creates a single, consistent view of your operations.
Architect for Continuous Data Flow and Scalability
Remember: Your data strategy isn’t for now, it’s for (at least) five years from now. Instead of relying on brittle, point-to-point connections that break under pressure, look to build scalable pipelines that can handle growing data volumes and support new use cases without constant re-engineering. Think of it as building a connected data superhighway, not a series of country roads.
Prioritize Seamless Connectivity Across Systems
Your data strategy should make it easy to connect new tools and data sources. By using a flexible integration platform with a wide range of pre-built connectors, you can reduce the timelines and effort involved in bringing new data online, allowing your teams to focus on building a strategic asset, not on building custom code.
Define KPIs That Reflect Real-Time Value
Measure what matters. While historical data analysis is important, focus on analytics that track real-time performance, such as customer engagement in the last hour, current inventory levels, or the immediate success of a marketing campaign. This shifts the focus from “what happened?” to “what is happening right now?” to influence current business decisions.
Apply Real-Time Data to Drive Tangible Business Outcomes
The ultimate goal is to use fresh data to make an impact, so your data strategy reflects your wider business strategy. You can start small, perhaps with just one high-value use case or business process, such as dynamic pricing in e-commerce, fraud detection in financial services, or predictive maintenance in manufacturing. A successful pilot project can demonstrate the power of real-time data and build momentum for broader adoption across the organization.
How Continuous Data Intelligence is Reshaping Strategic Possibilities
A strong data strategy doesn’t just improve current processes. It unlocks entirely new strategic possibilities. When you move from batch-based data collection to continuous, real-time intelligence, you fundamentally change how your business can innovate, and what you can deliver for your customers.
Immediate data availability transforms raw data into actionable, AI-ready insights the moment it’s created. This is the engine behind the next generation of intelligent applications. Consider its potential impact across different industries:
Dynamic Pricing in E-commerce: Instead of setting prices based on historical sales data, you can adjust them in real time based on current demand, competitor pricing, and even local weather patterns, maximizing revenue and inventory turnover.
Fraud Detection in Financial Services: By analyzing transaction data as it happens, you can identify and block fraudulent activity in milliseconds, protecting your customers and your bottom line before the damage is done.
Predictive Maintenance in Manufacturing: IoT sensors on machinery can stream operational data continuously. By analyzing this data in real time, you can predict equipment failures before they occur, scheduling maintenance proactively to avoid costly downtime.
Build Smarter, Faster, Real-Time Data Strategies with Striim
Activating a modern data strategy requires a platform built for real-time intelligence at scale. Striim helps leading organizations turn their strategic vision into an operational reality.
With Striim, you can:
Process data continuously and in-flight to reduce latency and power instant insights.
Integrate data seamlessly with 100+ out-of-the-box connectors for clouds, databases, applications, and more.
Build flexible, low-latency pipelines with streaming SQL for powerful and resilient data transformation.
Scale with confidence on an enterprise-grade, distributed architecture designed for high availability.
Maintain full control of your data with no vendor lock-in and complete cloud optionality.
Ready to put your data strategy in motion? Book a demo with our team or start your free trial today.
Jonathan Shough, CIO of Parexel, joins us to talk about leading data modernization in one of the world’s most regulated industries. He shares how compliance can be reframed as an enabler, not a blocker—and why it’s critical to deliver value to patients, not just platforms. We get into Parexel’s pragmatic approach to AI adoption, the role of human interaction in digital transformation, and what it really means to modernize data infrastructure without breaking what works. If you’re balancing transformation with trust—or just trying to give your teams back their Fridays—this one’s for you.
