Striim https://www.striim.com/ Wed, 08 Jan 2025 22:41:04 +0000 en-US hourly 1 https://wordpress.org/?v=6.6.1 https://media.striim.com/wp-content/uploads/2020/09/20152954/mstile-150x150-1-120x120.png Striim https://www.striim.com/ 32 32 A Comprehensive Guide to Operational Analytics https://www.striim.com/blog/comprehensive-guide-operational-analytics/ https://www.striim.com/blog/comprehensive-guide-operational-analytics/#respond Wed, 08 Jan 2025 16:55:11 +0000 https://www.striim.com/?p=44208 Recent studies highlight the critical role of data in business success and the challenges organizations face in leveraging it effectively. A 2023 Salesforce study revealed that 80% of business leaders consider data essential for decision-making. However, a Seagate report found that 68% of available enterprise data goes unleveraged, signaling significant untapped potential for operational analytics to transform raw data into actionable insights.

Operational analytics unlocks valuable insights by embedding directly into core business functions. It leverages automation to streamline processes and reduce reliance on data specialists. Here’s why operational analytics is key to improving your organizational efficiency — and how to begin. 

What Is Operational Analytics?

Operational analytics, a subset of business analytics, focuses on improving and optimizing daily operations within an organization. While business intelligence (BI) typically centers on the “big picture” — such as long-term trends, strategic planning, and organizational goals — operational analytics is about the “small picture.” It hones in on the granular, day-to-day decisions that collectively drive efficiency and effectiveness in real-time environments.

For example, consider a hospital seeking to streamline operations. To do so, the team would answer questions including: 

  • How many nurses are required per shift?
  • How long should it take to transfer a patient to the ICU?
  • How can patient wait times be reduced?

Operational analytics can answer these questions by providing actionable insights that drive efficiency and throughput. By analyzing real-time data, it helps organizations make data-informed decisions that directly improve daily workflows and overall performance.

Here’s another example: A customer service team can monitor ticket volumes in real-time, allowing them to prioritize responses without switching between tools. Similarly, a logistics coordinator can dynamically adjust delivery routes based on current traffic or weather conditions, ensuring smoother operations and greater agility. These examples illustrate how operational analytics seamlessly integrates into everyday processes, enabling teams to respond quickly and effectively to changing circumstances.

Operational analytics also excels in providing real-time feedback loops that BI does not typically offer. Where BI might analyze the success of a marketing campaign after its conclusion, operational analytics can inform ongoing campaigns by highlighting immediate trends, such as engagement spikes or content underperformance, enabling in-the-moment adjustments.

How Are Models Developed in Operational Analytics?

Analytic models are the backbone of operational analytics, helping organizations understand data, generate predictions, and make informed business decisions. There are three primary approaches to building models in operational analytics:

1. Model Development by Analytic Professionals

Analytic specialists, such as data scientists or statisticians, frequently lead the development of sophisticated models. They utilize advanced techniques including cluster analysis, cohort analysis, and regression analysis to uncover patterns and insights.

Models developed by these professionals generally follow one of the following approaches: 

  • Specialized Modeling Tools: Tools designed for tasks like data access, cleaning, aggregation, and analysis.
  • Scripting Languages: Languages like Python and R that provide robust libraries for statistical and quantitative analysis.

As a result, this approach delivers highly customized and precise models but requires significant expertise in both statistics and programming.

2. Model Development by Business Analysts

For organizations with limited needs, hiring an analytic specialist may not be feasible. Instead, these teams can leverage business analysts who bring a combination of business understanding and familiarity with data.

Typically a business analyst: 

  • Understands operational workflows and data collection processes.
  • Leverages BI tools for reporting and basic analytics.

While they may lack the technical depth of data scientists, business analysts use tools like Power BI and Tableau, which provide built-in functionalities and automation for model building. These tools allow them to extract and analyze data without the need for advanced programming knowledge, striking a balance between ease of use and analytical capability. Therefore, this is a great option for businesses that don’t have the resources to tap analytic professionals. 

3. Automated Model Development

Automated model development leverages software to build models with minimal human intervention. This approach involves:

  • Defining decision constraints and objectives.
  • Using the software to experiment with different approaches for various customer scenarios.
  • Allowing the software to learn from results over time to refine and optimize the model.

Through experimentation, the software identifies the most effective strategies, ultimately creating a model that adapts to customer preferences and operational needs. This method is particularly valuable for scaling analytics and reducing reliance on specialized skills.

How Can Your Team Implement Operational Analytics? 

Implementing operational analytics requires leveraging the right technologies, processes, and collaboration to ensure real-time, actionable insights drive efficiency and decision-making. Here’s how to do so effectively: 

1. Acquire the Necessary Tools

The foundation of operational analytics lies in having the right tools to handle diverse data sources and deliver real-time insights. Key components include:

  • ETL Tools: To extract, transform, and load data from systems such as enterprise resource planning (ERP) software, customer relationship management (CRM) platforms, and other operational systems.
  • BI Platforms: For data visualization and reporting.
  • Data Repositories: Data lakes or warehouses to store and manage vast datasets.
  • Specialized Tools for Data Modeling: These tools help create and refine analytics models to fit operational needs.

Real-time data processing capabilities are crucial for operational analytics, as they enable organizations to respond to changes immediately, improving agility and effectiveness.

2. Leverage In-Memory Technologies

Traditional BI tasks often rely on disk-stored database tables, which can cause latency. With the reduced cost of memory, in-memory technologies provide a faster alternative.

  • How It Works: By loading data into a large memory pool, organizations can run entire algorithms directly within memory, reducing latency and accelerating insights.
  • Use Cases: Financial institutions, for instance, use in-memory technologies to update risk models daily for thousands of securities and scenarios, enabling rapid investment and hedging decisions.

In-memory technologies are particularly valuable for real-time operational analytics, where speed and performance are critical to decision-making.

3. Implement Decision Services

Decision services are callable services designed to automate decision-making by combining predictive analytics, optimization technologies, and business rules.

  • Key Benefits: They isolate business logic from processes, enabling reuse across multiple applications and improving efficiency.
  • Example: An insurance company can use decision services to help customers determine the validity of a claim before filing.

To ensure effective implementation, decision services must access all existing data infrastructure components, such as data warehouses, BI tools, and real-time data pipelines. This access ensures decisions are based on the most current and relevant information.

4. Foster Unified Data Definitions Across Teams

A shared understanding of data is essential to avoid delays and inconsistencies in operational analytics implementation. Ensure alignment across your analytics, IT, and business teams by:

  • Standardizing Data Definitions: Consistency in modeling, testing, and reporting processes ensures smooth collaboration.
  • Prioritizing Real-Time Alignment: Unified data definitions help ensure that real-time insights are actionable and reliable for all stakeholders.

Which Industries Benefit from Operational Analytics?

Operational analytics has the potential to transform a variety of industries by enabling real-time insights, improving efficiency, and enhancing decision-making.

Sales

Many organizations focus on collecting new data but overlook the untapped potential in their existing sales tools like Intercom, Salesforce, and HubSpot. This lack of insight can hinder their ability to optimize sales strategies.

Operational analytics helps businesses better utilize their existing data by creating seamless data flows within operational systems. With more contextual data:

  • Sales representatives can improve lead scoring, targeting prospects with greater accuracy to boost conversions.
  • Real-time, enriched data enables segmentation of customers into distinct categories, allowing tailored messaging that addresses specific pain points.

These improvements empower sales teams to act on high-quality data, driving better outcomes.

Industrial Production

Operational analytics supports predictive maintenance, enabling businesses to detect potential machine failures before they occur. Here’s how it works:

  1. Identify machines that frequently disrupt production.
  2. Analyze machine history and failure patterns (e.g., overheating motors).
  3. Develop a model to predict failure probabilities.
  4. Feed sensor data, such as temperature and vibration metrics, into the model.

Over time, the model learns from historical and real-time data to provide accurate failure estimates. Benefits include:

  • Advanced planning of maintenance schedules to minimize downtime.
  • Improved inventory management by identifying spare parts needed in advance.

These predictive capabilities enhance operational efficiency and reduce costly disruptions.

Supply Chain

Operational analytics can optimize supply chain processes by extracting insights from the vast data generated across procurement, processing, and distribution.

For example, a point-of-sale (PoS) terminal connected to a demand signal repository can use operational analytics to:

  • Enable real-time ETL processes, sending live data to a central repository.
  • Anticipate consumer demand with greater precision.

Additionally, prescriptive analytics within operational analytics helps manufacturers evaluate their supply chain partners. For instance:

  • Identify suppliers with recurring delays due to diminished capacity or economic instability.
  • Use this insight to address performance issues with suppliers or explore alternative partnerships.

By uncovering inefficiencies and enabling proactive decision-making, operational analytics strengthens supply chain reliability and responsiveness.

Real-World Operational Analytics Example 

Morrisons, one of the UK’s largest supermarket chains, demonstrates how operational analytics can drive real-time decision-making and operational efficiency. By leveraging Striim’s real-time data integration platform, Morrisons modernized its data infrastructure to seamlessly connect systems such as its Retail Management System (RMS) and Warehouse Management System (WMS). This integration enabled Morrisons to ingest and analyze critical datasets in Google Cloud’s BigQuery, providing immediate insights into stock levels and operational performance.

For example, operational analytics allowed Morrisons to implement “live-pick” replenishment processes, ensuring on-shelf availability while reducing waste and inefficiencies. Real-time visibility into KPIs such as inventory levels, shrinkage, and availability empowered their teams—from senior leadership to store staff—to make informed decisions instantly. By embedding analytics into daily workflows, Morrisons created a data-driven culture that improved customer satisfaction and operational agility. This transformation highlights the power of operational analytics to optimize processes and enhance outcomes in the retail industry.

What Tools Are Available for Operational Analytics?

One common challenge with operational analytics is ensuring that tools can sync and share data seamlessly. Reliable data flow between applications is essential, yet many software platforms only move data into a data warehouse, leaving the task of operationalizing that data unresolved. Data latency further complicates the issue, making it difficult to display up-to-date insights on dashboards. This is where Striim stands out, offering powerful capabilities to address these challenges.

Striim provides real-time integrations for virtually any type of data pipeline. Whether you need to move data into or out of a data warehouse, Striim ensures that operational systems receive data quickly and reliably. Additionally, Striim allows users to build customized dashboards for operational analytics, providing actionable insights in real time.

Gaining Employee Buy-In for Operational Analytics

Adopting operational analytics often involves organizational changes that may challenge existing workflows. Decisions based on analytics can shift responsibilities, empowering junior staff to make decisions they previously deferred to senior colleagues. This shift can create unease among employees used to manual decision-making processes.

To ensure a smooth transition, organizations should involve employees from the start, demonstrating how operational analytics can enhance their work. Automation can free up time for more meaningful tasks, improving overall productivity and reducing repetitive workloads. By gaining employee confidence and highlighting the benefits, organizations can foster acceptance and ensure a successful rollout of operational analytics.

Improve Efficiency with Operational Analytics 

Operational analytics empowers organizations to optimize daily operations by transforming raw data into actionable, real-time insights. Unlike traditional business intelligence, which focuses on long-term strategies, operational analytics hones in on immediate, tactical decisions, enhancing efficiency and agility. 

Industries like retail, industrial production, and supply chain management use operational analytics to drive predictive maintenance, improve customer segmentation, and ensure real-time decision-making. Tools like Striim facilitate seamless data integration, real-time dashboards, and reduced latency, enabling businesses to respond proactively to operational challenges. By aligning technology, processes, and employee buy-in, operational analytics fosters a data-driven culture that enhances performance and drives success.

Ready to unlock the power of real-time insights with Striim? Get a demo today.

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Maximizing Fuel Efficiency with Real-Time Data: A New Era in Airline Operations https://www.striim.com/blog/maximizing-fuel-efficiency-airline-operations/ https://www.striim.com/blog/maximizing-fuel-efficiency-airline-operations/#respond Wed, 18 Dec 2024 12:09:41 +0000 https://www.striim.com/?p=82695 In 2024, the global airline industry is projected to spend $291 billion on fuel, making it one of the most significant expenses for airlines. Inefficient fuel management not only drives up operational costs but also hampers environmental targets. 

However, optimizing fuel usage is complex, often hindered by limited real-time monitoring, which can lead to unnecessary waste due to inefficient routes, weather adjustments, excess weight, and outdated practices. Now, real-time data is empowering airlines to address these challenges directly, unlocking impressive gains in both efficiency and sustainability.

Elevating Fuel Efficiency with Real-Time Data

For airlines, fuel efficiency isn’t just about cutting costs—it’s a pivotal factor in reducing environmental impact and maintaining competitive operations. Real-time data integration shifts the industry from reactive to proactive, enabling airlines to make precise adjustments that enable performance across every flight. Despite advancements, fuel inefficiency persists due to operational and logistical hurdles:

  • Limited Access to Live Data: Reactive decision-making often results from a lack of real-time visibility into key metrics.
  • Suboptimal Flight Paths: Without dynamic integration of weather and air traffic data, inefficient routing becomes inevitable.
  • Excessive Weight Management: Ineffective load balancing and outdated cargo handling unnecessarily increase fuel burn.

Addressing these issues requires a comprehensive approach, where real-time insights translate directly into action, driving operational efficiency and sustainability.

Real-Time Data Applications Driving Fuel Optimization

By tapping into real-time data, airlines are able to optimize fuel usage, reducing costs. This is made possible due to: 

1. Live Fuel Consumption Tracking

Real-time monitoring enables airlines to actively track fuel use and adjust operations dynamically. This leads to optimized routing, reduced fuel burn, and on-time arrivals—key factors in lowering costs and enhancing operational precision.

2. Route Optimization with Real-Time Insights

By incorporating live weather data and traffic conditions into flight planning, airlines can proactively adjust paths to avoid adverse conditions and capitalize on fuel-efficient routes. This minimizes unnecessary fuel consumption and most importantly, improves safety. 

3. Weight and Balance Optimization

Real-time analysis of passenger and cargo loads helps reduce excess weight, ensuring more efficient fuel burn. This practice not only cuts costs but also enhances the aircraft’s performance and range.

4. Anomaly Detection for Reliability

Real-time data analytics are essential for airlines to maintain operational efficiency and safety. By continuously monitoring aircraft performance metrics, airlines can promptly detect anomalies such as fuel leaks or irregular engine behavior, allowing for immediate corrective actions that prevent larger disruptions and ensure aircraft reliability.

5. Compliance with Fuel Efficiency Standards

Airlines must adhere to stringent fuel management regulations. Real-time insights simplify this process by providing detailed metrics, ensuring compliance while reducing overall consumption and waste.

Unlocking Operational Efficiency and Sustainability with Striim

Striim enhances these capabilities by providing a platform that integrates and processes real-time data from various sources. This enables airlines to implement predictive maintenance strategies, identifying potential issues before they escalate and optimizing overall performance.

Beyond maintenance, Striim’s real-time data processing also supports key initiatives like fuel optimization through live fuel tracking, route optimization using real-time insights, and weight and balance analysis, helping airlines reduce costs, improve safety, and enhance operational precision.

For instance, Striim’s real-time data integration has been instrumental in American Airlines’ operations, allowing them to monitor aircraft telemetry and proactively manage maintenance needs, while simultaneously ensuring compliance with fuel efficiency standards and delivering superior performance.

In an industry where margins are tight, real-time data is a powerful enabler of efficiency and sustainability. With Striim, airlines can evolve beyond reactive decision-making and embrace a proactive approach to fuel management, ensuring long-term success in a competitive and environmentally conscious landscape

How Striim Equips Airlines with Real-Time Insights for Efficiency and Sustainability

Striim’s platform provides airlines with advanced capabilities to achieve operational excellence and sustainability thanks to: 

  • Comprehensive Data Integration: Striim aggregates data from diverse sources—including weather systems, air traffic control, and aircraft sensors—into a unified, real-time view of operations. This centralized approach empowers teams with immediate insights across all facets of aviation operations.
  • Predictive Analytics with Machine Learning: Machine learning-driven insights help airlines forecast inefficiencies, such as engine anomalies or suboptimal routing, before they escalate. This ensures not only operational reliability but also cost-effective and environmentally friendly decision-making.
  • Robust Security: Advanced security measures protect sensitive operational data, ensuring compliance with industry standards while enabling seamless, secure data sharing across teams.

These capabilities empower airlines to proactively address inefficiencies across operations—from predictive maintenance to real-time delay management—while enhancing the passenger experience and advancing sustainable practices. Striim enables airlines to harness the full potential of real-time data, driving both operational excellence and a sustainable future in aviation.

Ready to experience the difference Striim can make? Get a demo today

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Striim & Morrisons: Customer Testimonial https://www.striim.com/blog/striim-morrisons-customer-testimonial/ https://www.striim.com/blog/striim-morrisons-customer-testimonial/#respond Mon, 25 Nov 2024 17:58:28 +0000 https://www.striim.com/?p=82002
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Real-Time Data: An Overview https://www.striim.com/blog/brief-overview-real-time-data/ https://www.striim.com/blog/brief-overview-real-time-data/#respond Fri, 22 Nov 2024 17:01:29 +0000 https://www.striim.com/?p=51792 Before real-time data, businesses had to rely on batch processing to collect and analyze large sets of data in periodic intervals. While batch processing was effective for historical analysis, it lacked the immediacy necessary for fast, actionable insights. With the rise of real-time data, businesses can now process information continuously as it arrives, enabling real-time decision-making. Additionally, it allows companies to respond to events instantly, improving operational efficiency and customer experiences.

According to McKinsey, high-performing companies are 70% more likely to have data broadly accessible to employees, enabling faster decision-making compared to their counterparts. Additionally, 92% of business leaders report plans to increase investment in real-time data analytics in the near future.

 As businesses continue to evolve and demand faster, more dynamic insights, the shift towards real-time data becomes not just an advantage but a necessity. To understand the impact and mechanics of real-time data, we’ll walk you through how it fundamentally differs from traditional methods. 

What is real-time data?

Real-time data refers to information that is captured, processed, and delivered to end users with minimal latency. Another signature of real-time data is that you can collect from a wide range of sources, such as cameras, social media feeds, sensors, operational systems (e.g., ERP systems), and databases. According to Dmitriy Rudakov, Director of Solution Architecture at Striim, the difference between real-time data is “its latency and how soon the user receives the data. So in some cases it’s milliseconds and in some cases it’s hours, but the main idea is that events arrive as soon as network and source speed allows. There’s typically a defined service level agreement (SLA).” 

This type of data provides a current view of events as they happen, enabling businesses to make informed decisions in the moment. Whereas with batch processing data is collected and analyzed at set intervals, real-time data is continuously streamed and updated, ensuring that the latest insights are always available.  

This immediacy of real-time data is crucial for a variety of applications, from monitoring stock market changes to tracking customer behavior on e-commerce platforms. For instance, in industries like finance and healthcare, real-time data enables immediate responses to potential issues, such as fraud detection or patient monitoring. In retail and e-commerce, it allows for dynamic pricing adjustments, personalized customer experiences, and real-time inventory management. 

By leveraging real-time data, businesses can not only react to events but also predict trends and opportunities, allowing them to stay ahead in fast-paced environments. Stream processing systems make this possible by processing data continuously as it is generated, rather than waiting for batches of information. This capability gives organizations a significant edge in improving operational efficiency, enhancing customer experiences, and making proactive decisions.

Real-time data can be categorized into the following types:

  • Event data: This refers to data generated in response to specific, well-defined events or conditions within a system. For example, when a user makes a purchase on an e-commerce platform, event data is generated to capture the details of that transaction.
  • Stream data: This type of data is continuously produced in large volumes, without any defined beginning or end. It includes real-time data flows from sensors, social media feeds, or network traffic. 

What are Examples of Real-Time Data? 

Examples of real-time data in an organization include system malfunction alerts, tracking the locations of assets or moving objects, and telemetry readings. This data can be sourced from both applications and sensors.

By using real-time data from applications (e.g., user interactions, page views, auction bids) and sensors (e.g., equipment temperature, system overload warnings), organizations can make faster decisions and maintain operational efficiency.

Let’s walk through some real-time data examples to demonstrate why it’s impactful. 

American Airlines exemplifies the power of real-time data by using it to streamline their flight operations. Managing over 5,800 daily flights, they rely on a sophisticated real-time data hub that integrates Striim, MongoDB, Azure, and Databricks. This hub captures and processes operational data, such as aircraft telemetry and maintenance needs, enabling the TechOps team to monitor and react to potential issues immediately. By leveraging this real-time data infrastructure, American Airlines ensures safe, efficient operations and an enhanced customer experience across their global network.

In the retail sector, legendary brand Macy’s faced significant challenges due to fragmented data across systems like mainframe and Oracle databases, leading to inconsistencies, high costs, and slow application development. Striim provided a solution by replicating real-time data to Google Cloud services like Cloud Spanner and BigQuery, creating a unified source of truth. This allowed Macy’s to streamline inventory management, reduce costs, ensure consistent customer experiences across channels, and accelerate time to market, supporting their overall digital transformation goals.

Historical vs Real-Time Data: What’s the Difference? 

Historical data plays a crucial role in batch processing, as it is collected, processed, and stored over time in an organization’s data repository. Typically analyzed in large volumes, historical data helps identify trends and insights, but it is not available for immediate decision-making. Unlike real-time data, which is continuously generated and offers up-to-date insights, historical data is bounded by a specific timeframe.

Say you want to plan your route to get home from the airport. While historical data can analyze past traffic patterns for route planning, it cannot adapt to sudden changes like a closed lane, highlighting the limitations of relying solely on historical information. In contrast, applications fueled by real-time data, such as Google Maps, leverage current data to provide instant updates and optimize travel routes efficiently. 

What are the Benefits of Real-Time Data? 

Real-time data allows companies to adopt a proactive approach. Access to real-time data means they can make decisions based on the latest data to address their inefficiencies and empower their end-users to be well informed. 

Successfully Fuel Your Artificial Intelligence Initiatives 

The future of AI is real-time data. Real-time data is essential for advancing AI initiatives because it enables instant processing and continuous learning. 

Unlike traditional batch processing, which delays data analysis, real-time data provides immediate access to current information, allowing AI systems to make data-fueled decisions and adapt quickly. This is because, with real-time data, your models are trained on the latest, most up-to-date information. 

Enhance Customer Experience

To enhance customer experience, leveraging real-time data is crucial, especially when it comes to delivery tracking. A study conducted by Descartes Systems Group reveals that 90% of consumers expect real-time tracking updates. 

By utilizing real-time data, businesses can address customer inquiries promptly and provide insights from drivers, deliveries, and operations. This transparency allows customers to track their orders from confirmation to delivery, ensuring they are informed throughout the process. Additionally, features such as instant communication with delivery drivers can improve convenience by accommodating customers’ preferred delivery schedules.

For instance, UPS Capital harnessed Striim’s real-time data streaming technology with Google BigQuery to enhance both delivery security and customer satisfaction. By streaming data instantly from multiple sources, Striim enabled immediate risk assessments and proactive decision-making. Integrated with BigQuery’s analytics and machine learning, this real-time data predicted delivery risks and optimized logistics strategies. The DeliveryDefense™ Address Confidence system, fueled by real-time insights, assigned confidence scores to delivery locations, significantly improving predictive accuracy. These real-time capabilities not only enhanced security but also increased customer satisfaction by ensuring timely deliveries, providing transparency throughout the process, and safeguarding customers’ packages. 

Eliminate silos

When relying on historical data, teams often encounter information silos, where departments operate independently, unaware of each other’s current operations. Without real-time data, this disconnect can lead to inaccurate decision-making. 

For instance, a customer service representative might inform a customer that their delivery will arrive on time, based on outdated information. However, if the product went out of stock just a few hours earlier, the customer may experience delays and frustration, leading to a poor customer experience. By contrast, real-time data allows for up-to-the-minute accuracy, enabling more precise answers and ultimately enhancing customer satisfaction.

Reduce downtime

Real-time data empowers businesses to take a proactive approach in managing assets by enabling them to predict, prevent, and address failures before they escalate. By continuously streaming data from various systems, companies can monitor machinery, infrastructure, and service levels in real time, allowing early detection of issues like equipment failure or service spikes. This ensures swift action, preventing minor inefficiencies from becoming larger, costlier problems, ultimately reducing downtime and optimizing performance.

Circling back to American Airlines, it used Striim’s real-time data streaming to optimize flight operations by tracking data from multiple sources such as aircraft sensors and weather conditions. This enabled the airline to anticipate potential disruptions and proactively adjust flight routes or schedules, reducing delays and improving customer satisfaction .

Use Striim to power your real-time data architecture 

Real-time data is no longer optional—it’s essential for modern businesses looking to stay competitive. Gone are the days when real-time systems were considered too expensive for widespread adoption. With advancements in memory, CPUs, and cloud technologies, real-time data processing is now both affordable and scalable, making it a must-have for organizations of all sizes. “Striim’s data collectors such as CDC readers due to its design allow real time data delivery without impacting the source systems,” shares Dmitriy Rudakov. 

A real-time data integration platform like Striim can help you harness the power of real-time data to optimize your operations. By integrating data instantly from various sources—whether it’s sensors, databases, log files, or data warehouses—Striim ensures that your data is always current and actionable. Additionally, it enables you to pre-process and enrich data with streaming analytics, giving your organization the ability to make faster, smarter decisions.

Ready to see the difference of real-time data firsthand? Schedule a demo to explore Striim with an expert. 

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Mirroring SQL Server Database to Microsoft Fabric https://www.striim.com/blog/mirroring-sql-server-database-microsoft-fabric/ https://www.striim.com/blog/mirroring-sql-server-database-microsoft-fabric/#respond Tue, 19 Nov 2024 16:00:35 +0000 https://www.striim.com/?p=81798 SQL2Fabric Mirroring is a new fully managed service offered by Striim to mirror on premise SQL Databases.  

It’s a collaborative service between Striim and Microsoft based on Fabric Open Mirroring that enables real-time data replication from on-premise SQL Server databases to Azure Fabric OneLake. This fully managed service leverages Striim Cloud’s integration with the Microsoft Fabric stack for seamless data mirroring to Fabric Data Warehouse and Lake House.

Microsoft Azure Fabric is an end-to-end analytics and data platform designed for enterprises that require a unified solution. It offers a comprehensive suite of services including Data Engineering, Data Factory, Data Science, Real-Time Analytics, Data Warehouse, and Databases. Operating on a Software as a Service (SaaS) model, Fabric brings simplicity and integration to your solutions. Striim Cloud is fully integrated with the Fabric stack and runs natively in Azure next to Fabric services making it a simple, secure, fast solution to replicate data to enable Data engineering, Data science, Real-time Intelligence and Data insights through PowerBI etc.

Key Benefits

  • Simplified Setup: Quick and easy deployment with a user-friendly interface.
  • Real-Time Data Replication: Seamlessly transfer data from SQL Server to Fabric for immediate insights.
  • Automated Data Pipelines: Benefit from automated initial load and real-time CDC pipelines, ensuring efficient data transfer.
  • Automated Schema Management: Simplify data migration with automatic schema mapping and evolution.
  • Robust Security: Protect your data with advanced security measures and compliance standards.
  • High Performance and Scalability: Handle large volumes of data with ease and ensure optimal performance.

SQL2Fabric-Mirroring

Getting Started

Striim’s SQL2Fabric-Mirroring is a purpose-built solution for replicating on-premises SQL Server data to Microsoft Fabric. With a few simple steps, you can automate the entire process, from initial data load to continuous replication in a few minutes.

Step 1: Signup to SQL2Fabric-Mirroring service 30-day trial

Signup for SQL2 Fabric-Mirroring service through Azure Marketplace or Striim’s product page to avail 30 days free trial. Lets you create an Striim Cloud account and login

Step 2: Create a Striim Cloud service

To get started, create a Striim Cloud service in a region close to your SQL Server. Establish a secure connection (e.g., VPN or Azure Private Link) between your on-premises environment and Striim Cloud. Then, use the intuitive interface to configure your data pipeline, specifying source and target details. Striim automates the rest.

Step 3: Create a Data pipeline

Striim’s user-friendly interface allows you to easily connect your SQL Server source and Microsoft Fabric target. The platform automatically creates an optimized data pipeline with smart defaults for SQL Server and handling initial load, continuous replication, and schema mapping using SQL server reader and Fabric Mirroring writer that is integrated with Fabric Mirroring service to offer end to end experience. This streamlined approach eliminates the need for managing multiple solutions, complex configurations and enables you to quickly start leveraging your data for real-time analytics and insights.

Step 4: Monitor high throughput data streaming 

Simply monitor real-time and continuous streaming of data from SQL Server to Fabric on Striim’s intuitive monitor screen or directly on Fabric Mirroring monitor. While Striim does the heavy lifting of ingesting Initial load (Historical data) and seamlessly switching to CDC mode (Change data capture) with low latency and high throughput data performance. Striim enables users to just focus on building business decisions by consuming real-time PowerBI dashboard insights


Easily access and create Power BI Reports on the data mirrored by Striim in Fabric.

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Unlocking Operational Efficiency: A Major Home Improvement Retailer’s Path to Data Modernization with Striim https://www.striim.com/blog/home-improvement-retailers-path-to-data-modernization/ https://www.striim.com/blog/home-improvement-retailers-path-to-data-modernization/#respond Mon, 11 Nov 2024 10:51:34 +0000 https://www.striim.com/?p=81736 Organizations across various industries require real-time access to data to drive decisions, enhance customer experiences, and streamline operations. A leading home improvement retailer recognized the need to modernize its data infrastructure in order to move data from legacy systems to the cloud and improve operational efficiency. To achieve these goals, the retailer partnered with Striim to support its data modernization and real-time integration efforts.

About the Retailer 

A leading home improvement retailer with thousands of stores across North America generates annual revenue exceeding $150 billion. Serving both DIY customers and professional contractors, the retailer offers a vast range of products for home improvement, construction, and gardening. Known for its customer-centric approach and expansive product offerings, the company has maintained its leadership position in the industry for decades.

Challenges 

The retailer’s legacy data infrastructure presented significant hurdles, preventing the company from achieving its modernization goals. These challenges stemmed from a complex and fragmented data environment, which included:

  • Siloed Data Sources: The retailer’s on-premise databases were spread across various locations, creating silos that made it difficult to consolidate and manage data effectively.
  • In-House and Third-Party Solutions: The retailer relied on a combination of in-house developed tools and third-party software. This patchwork of solutions led to inefficiencies, as different systems were not always compatible or easy to integrate.
  • Complexity in Data Replication: Moving data between platforms, particularly from legacy systems to newer ones, was a time-consuming and resource-intensive process. This made it difficult for the company to support critical initiatives like supply chain optimization and migration to the cloud.
  • Real-Time Data Limitations: The existing infrastructure lacked the ability to ingest and process data in real-time, making it hard for the retailer to stay agile and responsive to market demands.
  • Scalability Challenges: As the company grew, its data volumes increased dramatically. The legacy systems were not built to handle this scale, creating bottlenecks and limiting the company’s ability to manage data efficiently.
  • Multiple Teams Using Different Tools: Various departments, including migration and supply chain teams, used different tools and processes to manage their data. This lack of standardization added complexity and slowed down decision-making processes.

These issues underscored the need for a more efficient, scalable, and unified approach to managing the retailer’s data infrastructure.

Solution

To address the complexity and inefficiencies of its legacy data infrastructure, the retailer sought a robust platform that could simplify the migration process and provide real-time data integration across its operations. 

The goal was to consolidate data replication efforts and improve supply chain efficiency by utilizing modern cloud infrastructure. After evaluating options, the retailer partnered with Striim to leverage its real-time data streaming and low-code/no-code integration capabilities.

  • Striim’s platform enabled the migration of data from legacy Oracle and PostgreSQL databases to Google BigQuery.
  • Using Striim’s low-code/no-code capabilities, the retailer streamlined the migration process, reducing the burden on internal resources and cutting costs.
  • Striim’s real-time data integration capabilities played a vital role in optimizing supply chain operations.
  • Timely, pre-processed data delivered by Striim ensured that reporting and logistics systems could optimize operations, such as configuring truckloads based on store orders.
  • The platform met real-time SLAs and performed data transformations and validations on the fly, further simplifying processes.

Outcome

After facing significant challenges with its legacy data infrastructure, the retailer partnered with Striim to completely transform its approach to data management and integration. By migrating critical on-premise databases to Google Cloud and unifying its replication and migration efforts into a single platform, the retailer achieved substantial improvements in operational efficiency, scalability, and agility. These enhancements have allowed the company to optimize its data infrastructure, enabling it to better respond to evolving market demands and maintain a competitive edge in the retail industry.

Key results include:

  • Unified Data Platform: Through Striim, the retailer successfully consolidated its fragmented migration and replication processes into a single, unified platform. This eliminated the need for multiple tools and reduced the complexity of managing data across various systems, improving overall operational efficiency.
  • Migration to Google Cloud: Critical on-premise databases were seamlessly migrated to Google Cloud, enhancing the retailer’s ability to scale operations and support large volumes of data with greater ease. The migration to the cloud infrastructure enabled the retailer to benefit from more flexible, scalable computing resources.
  • Improved Scalability: The modernization effort significantly enhanced the retailer’s ability to handle growing data volumes. With improved scalability, the company can now manage and process vast amounts of data more efficiently, which is essential for its expanding operations and growing customer base.
  • Real-Time Data Integration: Striim’s real-time data streaming capabilities allowed the retailer to ingest and process data in real time. This empowered the company to make quicker, data-driven decisions, enabling faster responses to market dynamics and customer demands.
  • Operational Efficiency: By modernizing its data infrastructure and integrating real-time data streaming, the retailer was able to reduce operational costs. The transition to a microservices architecture also improved system performance and reliability, resulting in smoother workflows and a more streamlined supply chain.
  • Cost-Effectiveness: By moving to a cloud-based infrastructure and consolidating its migration efforts, the retailer reduced its reliance on legacy systems and lowered resource allocation for maintenance, which resulted in significant cost savings.
  • Positioned for Future Success: The retailer’s newly modernized, agile, and cost-effective data infrastructure positions the company for continued growth and success. With its scalable cloud environment and real-time data capabilities, the company is well-prepared to adapt to future industry changes and remain competitive.
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Driving Business Impact: Real-World Applications of Real-Time Data & AI https://www.striim.com/blog/driving-business-impact/ https://www.striim.com/blog/driving-business-impact/#respond Fri, 08 Nov 2024 21:08:04 +0000 https://www.striim.com/?p=81742 https://www.striim.com/blog/driving-business-impact/feed/ 0 Enabling Seamless Cloud Migration and Real-Time Data Integration for a Nonprofit Educational Healthcare Organization with Striim https://www.striim.com/blog/nonprofit-educational-healthcare/ https://www.striim.com/blog/nonprofit-educational-healthcare/#respond Thu, 31 Oct 2024 21:43:45 +0000 https://www.striim.com/?p=81588 A nonprofit educational healthcare organization is faced with the challenge of modernizing its critical systems while ensuring uninterrupted access to essential services. With Striim’s real-time data integration solution, the institution successfully transitioned to a cloud infrastructure, maintaining seamless operations and paving the way for future advancements.

About the Nonprofit Educational Healthcare Organization

This nonprofit educational healthcare organization is committed to providing students with the knowledge and skills needed to succeed in the medical field. Serving thousands of students, it offers a variety of programs designed to prepare individuals for careers in allied health. The institution prioritizes student success by delivering high-quality education, supported by a robust infrastructure that ensures access to essential resources and services. Through its mission-driven approach, the institution plays a vital role in meeting the growing demand for healthcare professionals.

Challenge

This non-profit educational healthcare organization is navigating a dual challenge: migrating its core Student Information System (SIS) to a modern Azure SQL Server infrastructure while maintaining seamless data integration with their on-premise SQL Server databases. With student data central to daily operations and long-term outcomes, real-time data replication between the cloud and legacy systems ensures continuity and accessibility across platforms.

However, while the SIS migration was a significant step forward, the institution’s on-premise SQL Server systems remained vital. These legacy systems were deeply embedded into the institution’s infrastructure, supporting critical applications for student services. The challenge was not just migrating to the cloud but ensuring that the on-premise systems, still housing essential services, could continue to operate seamlessly and in real time with the cloud-based SIS.

This setup presented several technical hurdles. The reliance on SQL-based integrations had already caused performance bottlenecks, particularly around the API-driven data capture required for student inquiries and real-time updates. 

Without a solution to ensure uninterrupted access to both systems, the institution risked compromising student satisfaction, potentially leading to operational delays, downtime, and an overall negative student experience. Thus, the migration needed to ensure minimal disruption while maintaining the integrity and availability of critical data.

Solution 

In response to this challenge, the institution sought a partner that could help them achieve their dual goals: enabling cloud migration while supporting continued access to legacy on-premise systems. After evaluating various options, they selected Striim for its real-time data integration and streaming capabilities.

Striim’s solution was particularly suited to address the institution’s unique needs. Through Striim’s platform, real-time data capture and integration between the cloud-based Azure SQL Server and on-premise SQL Server systems were facilitated with minimal latency, ensuring that both systems remained in sync at all times. This was crucial for guaranteeing uninterrupted access to student records, class schedules, and other key services.

A key component of the solution was Striim’s in-memory processing capability. By leveraging this technology, Striim was able to efficiently capture, process, and transform data in real-time, reducing the reliance on custom-built integration solutions. This not only reduced the institution’s costs but also simplified the entire process, minimizing the need for ongoing development and maintenance efforts. With Striim, the organization could confidently migrate its SIS to the cloud while maintaining seamless data flow between the cloud and legacy on-premise systems.

Moreover, the integration allowed the institution to maintain critical student-facing applications, such as portals for class registration and transcript requests, without experiencing downtime. This real-time synchronization provided a stable environment that improved the student experience during a period of significant technological transition.

Results 

The partnership between Striim and the nonprofit educational healthcare organization resulted in several tangible benefits that went beyond ensuring a smooth cloud migration. Striim’s real-time data integration not only ensured operational continuity but also created opportunities for future growth, enhancing the institution’s ability to leverage data for more advanced use cases.

Real-Time Data Access:
Striim’s platform enabled immediate access to student, faculty, scheduling information, eliminating delays that had previously hindered the institution’s ability to serve its students. This real-time access provided more responsive services, allowing students to receive up-to-date information at any time, enhancing their overall experience.

Improved Response Time:
The seamless integration of real-time data also improved the institution’s ability to respond quickly to inquiries from prospective students. As a result, response times to student inquiries were significantly shortened. This quicker response fostered better communication between prospective students and admissions staff, creating a more positive experience for applicants.

Increased Conversion Rates:
The operational efficiency gained through Striim’s data integration helped the institution streamline its processes, and can result in improved conversion rates for prospective students. With faster access to accurate, up-to-date information, administrative staff were better equipped to assist prospective students in their decision-making process, ultimately increasing enrollment rates.

Seamless Integration of Systems:
Striim’s real-time data streaming and in-memory processing ensured that critical systems across both the cloud and on-premise environments remained fully synchronized. This seamless integration was particularly important for student-facing and administrative functions. By maintaining up-to-date, synchronized data, the institution ensured that students and staff had continuous access to the information they needed without disruption.

Foundation for Future Initiatives:
Perhaps most importantly, the nonprofit educational healthcare organization’s new cloud-based infrastructure, empowered by Striim’s real-time data integration, provided a strong foundation for future innovations. With the flexibility of real-time data streaming and a scalable cloud environment, the institution is now well-positioned to explore advanced analytics and AI-driven insights. This can lead to further improvements in student services, operational efficiencies, and decision-making.

 

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Bloor InBrief Report https://www.striim.com/blog/bloor-inbrief-report/ https://www.striim.com/blog/bloor-inbrief-report/#respond Thu, 24 Oct 2024 19:28:09 +0000 https://www.striim.com/?p=81345 https://www.striim.com/blog/bloor-inbrief-report/feed/ 0 Morrisons Updates Data Infrastructure to Drive Real-Time Insights and Improve Customer Experience https://www.striim.com/blog/morrisons-data-infastructure/ https://www.striim.com/blog/morrisons-data-infastructure/#respond Tue, 22 Oct 2024 00:48:10 +0000 https://www.striim.com/?p=81007

Morrisons, a leading UK-based supermarket chain, is modernizing its data infrastructure to support real-time insights and operational efficiency. By embracing advanced data integration capabilities, Morrisons is transitioning to a more agile, data-driven approach. This shift allows the company to optimize processes, enhance decision-making, and ultimately improve the overall customer experience across its stores and online platforms.

About Morrisons

Morrisons is one of the UK’s largest supermarket chains, with over 100 years of experience in the food retail industry. Proudly based in Yorkshire, it serves customers across the UK through a network of nearly 500 conveniently located supermarkets and various online home delivery channels. With a commitment to quality, Morrisons sources fresh produce directly from over 2,700 farmers and growers, ensuring customers receive the best products. Dedicated to sustainability and community engagement, Morrisons continually invests in innovative solutions to enhance operations and improve the shopping experience.

Challenge 

Morrisons set out to modernize its data infrastructure to achieve five key goals:

  1. Elevating Customer Experience: Creating a better shopping experience for customers.
  2. Loading to Google Cloud: Transitioning to Google Cloud and leveraging Looker for enhanced reporting capabilities.
  3. Accessing Real-Time Data: Shifting from batch processing to real-time data access, enabling faster decision-making and improved operational efficiency.
  4. Enhancing Picking Efficiency: Morrisons sought to streamline their online picking process by improving stock visibility across depots and warehouses. 
  5. Improving On-Shelf Availability: Ensuring products are consistently in stock and accessible to customers.

To meet these goals, the team needed to move away from their legacy Oracle Exadata data warehouse and strategically align on Google Cloud. This involved transitioning their data to Google BigQuery as the new centralized data warehouse, which required not only propagating data but also ensuring real-time access for better decision-making and operational efficiency. Moreover, prior to this transition, Morrisons never had a centralized repository of real-time data, and only ever had batch snapshots delivered from its disparate systems.

“Retail is real-time. We have our online shop open 24/7, and we have products moving around our distribution network every minute of every day. It’s really important that we have a real-time view of how our business is operating,” shares Peter Laflin, Chief Data Officer at Morrisons. 

In order to accomplish this, Morrisons needed a tool that could connect their separate systems and seamlessly move data into Google Cloud. Striim was selected to ingest critical datasets, including the Retail Management System (RMS), which holds vast store transaction data and key reference tables, and the Warehouse Management Systems (WMS), which oversee operations across 14 distribution depots. The integration of these systems into BigQuery in real time provided critical visibility into product availability, stock levels, and core business metrics such as waste and shrinkage. Most importantly, Morrisons needed this mission-critical data delivered in real time. 

“We’ve moved from a world where we have batch-processing to a world where, within two minutes, we know what we sold and where we sold it,” shares Laflin. “That empowers senior leaders, colleagues in stores, colleagues across our logistics and manufacturing sites to understand where we are as a business right now. Real-time data is not a nice to have, real-time data is an absolute essential to run a business the scale and size of ours.” 

Morrisons sought to move away from their existing analytics suite and leverage Google Looker for their reporting and analytics needs. This meant they had to regenerate all existing reports that previously ran on the Exadata platform, aligning them with the new Google Cloud infrastructure. Striim played a critical role in centralizing their data in BigQuery and delivering it in real time, enabling Morrisons to power their reporting with fresh insights. This transformation is key to achieving their goal of a more agile, data-driven operation and supporting future business initiatives.

Solution 

Morrisons now leverages Striim to connect disparate systems and ingest critical datasets from their Oracle databases into Google Cloud, using BigQuery as their new centralized data warehouse. They required a solution that could seamlessly load data from multiple sources while providing real-time access through BigQuery, and Striim provides this. 

Striim plays a pivotal role in ingesting two core databases: the Retail Management System (RMS) and the Warehouse Management System (WMS). The RMS, a vast dataset containing store transaction tables and key reference data, requires efficient data transfer to minimize latency, and Striim ensures that this high volume of data is processed seamlessly.

Striim also ingests data from all 14 distribution depots, which are connected through 28 sources in the WMS. This integration provides real-time visibility into stock levels, enabling ‘live-pick’ decision-making by revealing what stock is available, where it is located, and at what time. Backed by real-time intelligence, this capability accelerates business processes that were previously reliant on periodic batch updates. As a result, Morrisons can optimize the replenishment process and ensure that shelves remain well-stocked, ultimately improving overall efficiency and increasing customer satisfaction.

Striim’s real-time data delivery powers Morrisons’ reporting transformation as they rebuild all reporting within Google Looker. By centralizing and accelerating the flow of data into BigQuery in real time, Striim enables faster, actionable insights that drive operational excellence and future business initiatives. “My team felt that Striim was the only tool that could deliver the requirements that we have,” shares Laflin.

Outcome 

By leveraging Striim to transition from batch processing to real-time data access, Morrisons has significantly enhanced their ability to track and manage three critical key performance indicators (KPIs): availability, waste, and shrinkage. With access to faster, real-time insights, executives can more effectively identify risks and implement strategies to mitigate them, ultimately leading to improved operational decision-making and better performance across the organization. This shift allows Morrisons to optimize their processes and drive positive outcomes related to these key metrics.

“Without Striim, we couldn’t create the real-time data that we then use to run the business,” shares Laflin. “It’s a very fundamental part of our architecture.”

The move towards real-time data has allowed Morrisons to identify that their shelf availability has notably improved, ensuring that products are consistently in stock and accessible to customers. Best-ever on-shelf availability in December 2024 boosted customer satisfaction, marking a significant milestone for Morrisons. As a result, they are beginning to uncover the full range of benefits that this transformation can bring, including enhanced inventory management and reduced waste.

From the customer perspective, better shelf availability translates into happier shoppers, as they can find the products they want when they visit stores. This improvement not only fosters customer loyalty but also positions Morrisons to compete more effectively in the marketplace, ultimately driving growth and enhancing overall customer satisfaction.

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