Spencer Cook, Lead Solutions Architect at Databricks, joins to unpack how enterprises are moving beyond hype and building practical AI systems using vector search, RAG, and real-time data pipelines. He and John Kutay get into what it really takes to serve production LLMs safely, avoid hallucinations, and tie AI back to business outcomes—without losing sight of governance, latency, or customer experience.
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The General Medical Council (GMC) is the independent regulator of doctors in the United Kingdom, responsible for protecting patient safety and upholding standards in medical practice. Established in 1858, the GMC maintains the official register of medical practitioners, ensures the quality of medical education and training, and investigates complaints about doctors’ conduct and performance. Its regulatory duties span the full medical career lifecycle—from medical school accreditation to post-graduate training oversight and fitness to practise tribunals—making it a cornerstone of the UK’s healthcare system.
With over 1,700 employees supporting more than 300,000 registered doctors, the GMC depends on timely, accurate, and secure data to fulfill its mission. Its work involves sensitive and complex data, including personal identifiers, legal casework, and educational records. As the organization modernizes its infrastructure, the move toward real-time, cloud-based analytics is essential for faster reporting, enhanced transparency, and future-ready capabilities like AI-driven insights. This transformation enables GMC to deliver more responsive regulation and support high-quality care across the UK.
GMC’s strategic goal was to migrate to a modern, cloud-based analytics stack built around Azure and Power BI. But there was one major obstacle: their primary data source, Siebel CRM, wasn’t ready to move to the cloud.
The organization faced several limitations:
Following a thorough review of how the right data was critical in the right architecture, it was shared:
To solve this challenge, GMC needed a real-time integration layer that could stream on-prem data to Azure reliably. After evaluating several solutions—including Oracle GoldenGate and Qlik—they selected Striim for its:
GMC’s team worked with Striim to deploy a streaming solution that connected their Siebel source data to the cloud—while simultaneously scaling up their Azure environment. The implementation helped the team build out its new architecture while laying the groundwork for broader real-time data access.
Even before completing their full migration, GMC saw significant operational benefits:
✅ Cost Savings
By retiring Tableau (an estimated £90,000/year) and planning the decommissioning of Oracle analytics and Informatica, GMC reduced analytics costs while positioning the organization for scalable growth.
✅ Faster Back-End Operations
Previously, if an ETL failed, re-running it meant uploading over 150 GB of data—a process that could take hours and disrupt business operations. With Striim’s live streaming in place, data is always current, and ETLs can be triggered on demand.
✅ Minimal Disruption
Because Striim runs in parallel with existing ETLs, GMC was able to phase in their new system gradually, minimizing risk during the transition.
✅ Strategic Flexibility
Striim enabled decoupling from legacy infrastructure, empowering GMC to scale up Power BI adoption and build out its modern cloud analytics stack with confidence.
By connecting on-prem systems with Azure in real time, GMC is not only solving today’s data integration challenges but also laying the groundwork for tomorrow’s AI, analytics, and compliance initiatives.
Looking ahead, GMC’s analytics roadmap includes:
GMC’s transformation highlights the power of real-time data integration in modernizing legacy systems and enabling a cloud-first future. Striim delivered the scalability, compliance, and speed needed to help GMC accelerate its journey while keeping costs in check and teams empowered.
Want to see what Striim can do for your organization?
Today’s businesses generate and collect vast amounts of data from an ever-growing array of sources—transactional databases, customer relationship management (CRM) systems, website interactions, social media platforms, IoT devices, and more.
However, integrating and harmonizing these disparate data streams in real time presents a formidable challenge. The complexity arises from differences in data formats, structures, latency requirements, and the need for seamless orchestration between multiple systems. Without a unified approach, businesses struggle to gain a holistic view of customer behaviors, leading to missed opportunities, disjointed experiences, and inefficiencies in decision-making.
Real-time data streaming addresses these challenges head-on by serving as the backbone of real-time AI data pipelines. Its distributed, in-memory streaming architecture ingests, processes, and integrates unbounded and evolving data streams with unmatched efficiency and minimal latency. Striim seamlessly connects diverse data sources, applies transformation and enrichment in real time, and delivers unified, actionable insights across AI, BI, and operational platforms.
Specifically, Striim’s AI-ready architecture goes beyond traditional integration by enabling businesses to:
The integration of Generative AI (GenAI) into real-time data pipelines enables businesses to analyze and respond to customer behaviors dynamically. With GenAI, organizations can:
By processing diverse data sources in real time, businesses gain immediate insights into customer preferences, intent, and engagement. This enables:
GenAI allows businesses to craft highly customized experiences by dynamically analyzing individual customer data. With real-time AI-driven insights, organizations can:
Consumer expectations shift rapidly, and static models quickly become obsolete. Striim enables businesses to adapt their AI models dynamically by:
Businesses leveraging real-time data with GenAI achieve higher engagement levels by:
Striim’s platform is designed with advanced capabilities that bridge real-time data integration and AI-driven analytics. Key technical differentiators include:
Striim ingests data from various sources—transactional systems, IoT devices, clickstreams, CRM platforms—leveraging low-latency messaging frameworks like Apache Kafka and MQTT. The distributed in-memory architecture ensures high throughput and efficient handling of real-time workloads.
Striim natively supports GenAI models, enabling real-time execution of:
With built-in support for:
Striim ensures AI-powered insights reach the right touchpoints at the right time:
Organizations that harness real-time data and GenAI with Striim unlock transformative outcomes:
The future of customer engagement is real-time, AI-powered, and insight-driven. Businesses can no longer afford to operate on fragmented, delayed data streams. Striim unifies diverse data sources, integrates AI seamlessly, and delivers real-time intelligence that transforms customer interactions.
By merging operational and behavioral data streams with AI-enhanced analytics, Striim empowers enterprises to stay ahead of the curve—ensuring every customer experience is timely, relevant, and impactful.
Striim is the backbone of modern AI-driven enterprises, providing the real-time data infrastructure needed to drive intelligent automation, adaptive customer engagement, and sustained business growth.
Real-time data has become a non-negotiable foundation for powering machine learning (ML) and generative AI (GenAI). From delivering event-driven predictions to powering live recommendations and dynamic chatbot conversations, AI/ML initiatives depend on the continuous movement, transformation, and synchronization of diverse datasets across clouds, applications, and databases.
But complexity stands in the way: incompatible platforms, brittle pipelines, fragmented architectures, and the growing pressure of data privacy and compliance risks make it challenging for teams to deliver trusted, real-time data to models and applications.
In this webinar with BARC, a leading analyst firm for data & analytics and enterprise software, you’ll learn how to overcome these challenges and build the data backbone for AI/ML success.
Join us to:
Break down the key elements of modern data streaming architectures and their impact on AI/ML.
Define the must-have characteristics of a data streaming architecture.
Explore real-world use cases, including streaming ELT for experimentation, real-time ML, and retrieval-augmented generation (RAG) for GenAI.
Gain actionable guidance to build scalable, resilient streaming pipelines that drive continuous innovation and measurable value.
Executives and practitioners leading data and AI transformation — learn what it takes to stay competitive. Register today!
AI and analytics have the potential to transform decision-making, streamline operations, and drive innovation. But they’re only as good as the data they rely on. If the underlying data is incomplete, inconsistent, or delayed, even the most advanced AI models and business intelligence systems will produce unreliable insights.
Many organizations struggle with:
These issues don’t just affect technical teams—they impact every aspect of the business, from customer experience to operational efficiency. Without high-quality, available data, companies risk misinformed decisions, compliance violations, and missed opportunities.
To extract meaningful value from AI and analytics, organizations need data that is continuously updated, accurate, and accessible. Here’s why:
Many businesses are shifting toward real-time data pipelines to ensure their AI and analytics strategies are built on reliable information. Here’s how they are tackling these issues:
Rather than storing data in isolated systems, organizations are adopting real-time data integration strategies to unify structured and unstructured data across databases, applications, and cloud environments.
Modern data architectures incorporate automated validation, cleansing, and enrichment techniques to detect missing values, inconsistencies, and errors before they reach AI and analytics platforms.
To avoid delays, businesses are implementing streaming data platforms that allow information to be processed as soon as it is generated, rather than relying on batch updates.
With growing regulations around data privacy, organizations must enforce real-time lineage tracking, access controls, and encryption to ensure sensitive data remains secure.
AI models require ongoing updates to stay relevant. Leading companies are using continuous learning techniques to keep AI applications accurate by feeding them real-time, high-quality data.
Striim helps organizations solve these challenges by ensuring real-time, clean, and continuously available data for AI and analytics. With low-latency streaming, automated data validation, and AI-powered transformations, Striim enables businesses to:
By solving the data quality and availability problem, Striim helps businesses unlock AI’s full potential—ensuring that decisions are driven by accurate, real-time intelligence.
The success of AI and analytics depends on how well businesses manage data quality and availability. Companies that fail to address these challenges risk acting on faulty insights, missing market trends, and losing their competitive edge.
By investing in real-time, high-quality data pipelines, organizations can ensure that AI and analytics initiatives deliver accurate, timely, and actionable intelligence.
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Peter Laflin, Chief Data Officer at Morrisons, shares how his team turned customer confusion into a cutting-edge vector search experience—bridging physical retail with AI-powered search. He and John Kutay dive into the practical challenges of implementing LLMs and real-time data pipelines at scale, the importance of starting with actual customer problems, and why the best engineering feels a little lazy (on purpose). A real-world look at what happens when modern search meets supermarket shelves.
What’s New In Data is a data thought leadership series hosted by John Kutay who leads data and products at Striim. What’s New In Data hosts industry practitioners to discuss latest trends, common patterns for real world data patterns, and analytics success stories.
As data volumes surge and the need for fast, data-driven decisions intensifies, traditional data processing methods no longer suffice. This growing demand for real-time analytics, scalable infrastructures, and optimized algorithms is driven by the need to handle large volumes of high-velocity data without compromising performance or accuracy. To stay competitive, organizations must embrace technologies that enable them to process data in real time, empowering them to make intelligent, on-the-fly decisions.
With industries facing an increasing pace of change, businesses require the capability to quickly extract valuable insights from dynamic data streams. Real-time AI and machine learning (ML) models play a crucial role in ensuring both speed and precision, enabling businesses to navigate and respond to ever-changing conditions efficiently. These technologies must not only scale but also adapt to the complexity of high-velocity data.
Real-time analytics offer organizations the ability to enhance operational efficiency by making faster, more informed decisions. Below are key advantages of leveraging high-throughput data processing:
Real-Time Actionable Insights: By applying trained AI models to incoming data streams in real time, businesses can extract actionable insights immediately. This ensures that critical decisions—such as identifying new business opportunities or mitigating risks—are made quickly, reducing delays and increasing agility. Striim plays a key role in enabling businesses to extract these insights by seamlessly processing and integrating data in real time from various sources.
Improved Efficiency and Scalability: Real-time data processing platforms like Striim allow businesses to manage vast datasets without sacrificing performance. By using advanced algorithms and parallel processing techniques, Striim helps organizations scale their operations to accommodate increasing data volumes while maintaining low-latency performance. This scalability ensures that businesses can handle large, complex datasets efficiently, even as they grow.
Cost Savings Through Automation: High-throughput data processing allows organizations to automate decision-making tasks that would otherwise require manual intervention. This reduces reliance on human resources, minimizes errors, and lowers operational costs, enabling businesses to allocate resources more effectively. Striim’s platform supports this automation, ensuring that businesses can optimize their operations and reduce the need for manual data handling.
Enhanced Accuracy: Real-time processing utilizes sophisticated algorithms. These models improve the accuracy of insights derived from data streams, supporting more reliable, up-to-date decision-making and minimizing risks associated with outdated or incomplete data. With Striim’s advanced data integration capabilities, businesses can ensure that their decision-making is based on the most accurate and timely data available.
Seamless Integration for Instant Insight: To maximize the benefits of real-time analytics, organizations need platforms that can seamlessly integrate AI models into their data pipelines. Striim provides the architecture to apply trained models to incoming data as it flows through the system. By deploying lightweight inference agents within the streaming pipeline, Striim delivers real-time insights without delays, ensuring businesses can act on them instantly.
Flexibility Across Use Cases: Real-time data analytics can be applied across a variety of use cases, from predictive maintenance to anomaly detection, and customer behavior analysis. Whether businesses are looking to monitor equipment performance, detect fraud, or gain insights into customer trends, Striim’s platform provides the flexibility to implement AI models quickly and effectively, delivering insights tailored to specific business needs.
As industries continue to generate enormous volumes of data, the ability to process and manage this data at high speeds will be critical to success. Organizations that can leverage real-time analytics to extract insights from fast-moving data streams will be better equipped to make informed decisions in today’s dynamic landscape. Striim’s platform plays an integral role in enabling businesses to achieve this by delivering real-time data processing, scalable architectures, and seamless integration of advanced analytics models.
The future of high-velocity data demands agility, scalability, and precision—qualities that Striim delivers, helping businesses turn real-time insights into actionable outcomes with minimal delay.
Artificial intelligence (AI) and machine learning (ML) are transforming the way the world works by enabling smarter, faster, and more automated decision-making. However, one of the challenges that have emerged as AI systems evolve is the issue of AI/ML hallucinations—outputs generated by models that are plausible but incorrect, which can undermine the reliability of AI systems.
Addressing these hallucinations head-on is essential for ensuring that AI systems continue to provide accurate and actionable insights, especially in environments where real-time decisions are imperative to success.
As the volume of data continues to grow at an exponential rate, the need for scalable AI and ML solutions becomes even more significant. Real-time AI solutions are no longer a luxury but a necessity for businesses looking to stay ahead in a data-driven world. To combat hallucinations and ensure accurate decision-making, businesses will need to develop robust systems that include rigorous validation, enhanced interpretability, and continuous monitoring. These advancements ensure that the AI systems powering business operations remain reliable, trustworthy, and capable of making data-driven decisions in dynamic conditions.
First, let’s dive into the benefits associated with your business leveraging real-time AI.
By automating processes and improving resource allocation, companies can significantly reduce operational costs and enhance efficiency. Real-time insights allow businesses to quickly identify inefficiencies and take corrective actions, driving cost savings across the organization.
Striim’s real-time ML analytics streamline operations, enabling businesses to identify bottlenecks and optimize workflows. By acting on these insights promptly, businesses can enhance productivity and reduce delays, improving their overall operational efficiency.
Real-time AI enables businesses to stay ahead of the competition by providing the agility to capitalize on emerging opportunities and respond to market changes faster than competitors. By leveraging real-time insights, businesses can improve customer experiences, adjust pricing strategies, and optimize their supply chains on the fly. However, if your business isn’t able to manage hallucinations, it won’t gain a competitive advantage, but a setback.
With the help of real-time AI, your organization is able to react quickly to changing market conditions with up-to-the-moment insights from streaming data sources. Whether it’s personalizing customer experiences, adjusting pricing strategies, or optimizing operations, the ability to make decisions based on real-time insights provides businesses with a critical competitive advantage in today’s fast-paced digital economy.
Of course, these benefits are only feasible if your organization manages hallucinations successfully.
The good news is that you don’t have to do it alone. Here’s how Striim empowers your business to manage hallucationas and gain confidence in real time AI.
Striim powers AI analytics over inflight data, enabling precise anomaly detection and automated predictions. This ability allows businesses to detect and act on anomalies as they occur, helping to prevent costly disruptions. By integrating these insights into the decision-making process, businesses can mitigate the risks of hallucinations and other data inconsistencies, ensuring reliable AI outputs.
Continuous learning algorithms ensure that AI models evolve dynamically in response to changing data patterns. As new data streams in, these algorithms update model parameters in real time, ensuring that AI predictions stay relevant and accurate. With this adaptive approach, Striim helps maintain the accuracy and effectiveness of AI systems, reducing the likelihood of hallucinations while enhancing decision-making.
Striim’s processing engine is optimized for low-latency data processing, using techniques like in-memory computing, parallelization, and pipeline execution to maximize throughput and minimize delays. By providing near-instant access to insights, Striim enables businesses to make timely, data-driven decisions that account for the most current data—reducing the risk of acting on outdated or inaccurate information.
As AI systems continue to grow and evolve, the importance of managing hallucinations and maintaining the accuracy of models in real time environments will only increase. Striim’s advanced real-time data integration, low-latency processing, and continuous learning algorithms provide businesses with the tools they need to navigate this challenge. By ensuring that AI models remain adaptable and accurate in the face of evolving data, Striim is helping businesses not only mitigate the risks of AI hallucinations but also unlock the true potential of real-time AI decision-making.
By integrating these advanced technologies, organizations can make smarter, faster decisions that propel them forward, improving their bottom line while minimizing the risks associated with AI-based systems. Real-time data analytics, powered by Striim, is the key to navigating the future of AI in business and driving sustainable success.
Alok Pareek, Co-founder and EVP of Products at Striim, joins What’s New in Data to dive into the game-changing innovations in Striim’s latest release. We explore how real-time data streaming is transforming analytics, operations, and decision-making across industries. Alok breaks down the challenges of building reliable, low-latency data pipelines and shares how Striim’s newest advancements help businesses process and act on data faster than ever. From cloud adoption to AI-driven insights, we discuss what’s next for streaming-first architectures and why the shift to real-time data is more critical than ever.
Learn more about our latest release on Striim’s Release Highlight page here: https://www.striim.com/whats-new-in-striim/
What’s New In Data is a data thought leadership series hosted by John Kutay who leads data and products at Striim. What’s New In Data hosts industry practitioners to discuss latest trends, common patterns for real world data patterns, and analytics success stories.
