Global Data Lifecycle - Integration and Governance

Global Data Lifecycle - Integration and Governance

Connect with Db2, Informix, Netezza, open source, and other data experts to gain value from your data, share insights, and solve problems.

 View Only

How Streaming + Batch Powers Smarter, Faster Enterprise AI

By Chandni Sinha posted yesterday

  

Your AI is only as good as your data pipeline  

Enterprises today are under immense pressure to unlock the power of AI not just for innovation, but to drive intelligent, real-time decision-making across the business. Yet most aren’t ready.  

More than 80% of enterprise data goes unused in AI and analytics initiatives, according to IDC. And by 2026, Gartner predicts organizations will abandon 60% of AI projects unsupported by AI-ready data.   

Why the gap? Because while AI has evolved, many data pipelines haven’t. They’re brittle because they often break with schema changes. They are siloed as data is stored across hybrid environments. And they are slow in terms of the volume of records that can be processed per second. Enterprises often rely on fragmented ecosystems of batch ETL tools, traditional systems, and real-time ingestion platforms that don’t talk to each other. The result:  

  • Models trained on stale or incomplete data  

  • Inability to act in real-time  

  • Mounting costs from tool sprawl and frequent pipeline breakages 

This isn't just a technical challenge, it’s a strategic bottleneck.   

  

The Smarter Path: watsonx.data integration  

To scale AI across the enterprise, a new architectural pattern is emerging: hybrid data integration, where batch and streaming data work together seamlessly.  

  • Batch data offers depth and context. It provides rich historical datasets required for training accurate, explainable, and compliant models.   

  • Streaming data adds speed and situational awareness. It enables real-time inference, personalization, and dynamic responses to events as they unfold.  

The magic happens when these two modes are not separate and siloed with different teams manually stitching it all together, but intelligently unified. This is what powers enterprise-grade AI that’s not just smart but fast, adaptive, and continuously improving.  

The future isn’t batch or streaming. It’s both. Unified. Resilient. AI-ready.  

Without a hybrid approach that harnesses the best of batch and real-time integration, AI is either uninformed or outdated. The hybrid integration opportunity unlocks what siloed tools cannot:  

  • Real-time decisioning backed by historical intelligence: Get insights in seconds, not hours, with real-time pipeline triggers  

  • Operational agility: React instantly to customer behaviour, market signals, or system events  

  • Future-proof architecture: Your existing data pipelines will continue to work regardless of changes to underlying data storage architecture or integration styles. 

Most importantly, you gain trust in your data, in your AI, and in every decision made from both.  

This is exactly what IBM’s watsonx.data integration was built to deliver. IBM watsonx.data integration is designed for hybrid architectures, helping enterprises move from disjointed pipelines to a unified control plane for ingesting, preparing, and integrating both streaming and batch data.   

How Watsonx.Data Integration Transforms Data into AI-Driven Action 

Organizations across industries are reimagining how data powers their business by unifying batch and streaming pipelines with IBM watsonx.data integration. From combining historical transaction records with real-time digital interactions to deliver personalized experiences, to analysing live financial data alongside historical fraud patterns to trigger instant risk alerts or merging IoT sensor feeds with past maintenance logs to anticipate equipment failures, watsonx.data integration turns data into action at speed. These are only a few examples of how unified hybrid integration can transform operations, accelerate decision-making, and power enterprise AI. The potential extends far beyond. Wherever data is created and consumed, watsonx.data integration is ready to scale with it. 

 

 

Solving for Tool Sprawl, Cost, and Compliance  

One of the biggest challenges enterprises faces is tool sprawl. Over the years, data teams have accumulated dozens of siloed tools: for streaming integration, batch ETL/ELT, data observability, data replication, low-code pipeline building and more. 

This doesn’t scale. It introduces administrative complexity, data inconsistency, compliance risks, and soaring total cost of ownership.  

According to IDC, 50% of organizations use at least three separate tools for data integration. As organizations modernize their data environments and aim to cut costs in a challenging economic climate, consolidating tools to eliminate technical debt has become a critical priority. 

watsonx.data integration solves this by providing:  

  • Multiple pipeline authoring experiences: Multimodal authoring entry points for code, low-code, or SQL help reduce tool sprawl, increase productivity, and expand collaboration. 

  • Full spectrum of data integration capabilities: A single data integration control plane for real-time data streaming, bulk ETL/ELT, and replication underpinned by data observability. 

  • Composable, workload adaptability: A composable architecture enables the portability of workloads between execution engines, allowing for optimization across various workload characteristics. 

 

It eliminates the need for multiple tools and enables a more streamlined approach for reliable data delivery for AI and analytics. 

The Smartest AI Starts Where Streaming and Batch Converge  

  
In a world where every second counts, competitive advantage lies in how quickly you turn data into decisions. Enterprises that treat data as a strategic asset by breaking silos and modernizing their pipelines will lead in AI adoption and innovation.   

According to IDC, 79% of organizations experience benefits from enhanced decision-making capabilities through immediate insights. Companies leveraging real-time approaches can shorten decision cycles by an impressive 30% compared to traditional analytical methods. And because data has a short shelf life, timely processing is critical, 63% of use cases require data to be processed within minutes to be useful. 

Those who fail to modernize? They’ll continue to face lagging models, higher costs, and lower trust in AI systems.  

Watch a webinar to discover how you can integrate any data, using any integration technique – batch, real-time, replication - with a unified control plane in watsonx.data integration to scale reliable data delivery for AI, while scaling down the number of tools and pipeline debt. 

  

 

0 comments
0 views

Permalink

https://community.ibm.com/community/user/blogs/chandni-sinha/2025/07/16/how-streaming-batch-powers-smarter-faster-enterpri