Latest news with #AzureSynapseAnalytics
Fintech News ME
13-06-2025
- Business
- Fintech News ME
e& enterprise and Microsoft Expand AI Partnership in MENAT
e& enterprise, the digital transformation arm of technology group e&, is expanding its collaboration with Microsoft to deliver scalable AI and data solutions across the Middle East, North Africa and Türkiye (MENAT) region. The partnership is focused on accelerating AI adoption and supporting digital transformation efforts in key markets including the UAE, Saudi Arabia, Egypt, Qatar, and Türkiye. Amit Gupta, Vice President of Data & AI at e& enterprise, said: 'This collaboration combines e& enterprise and Microsoft's technological expertise and deep market insights to deliver transformative solutions that resonate with the unique dynamics of the MENAT region. Together, we aim to promote digital readiness and bridge the digital divide by providing essential sectors with next generation AI and data-driven tools.' Ahmed Hamzawy, Chief Partner Officer, Microsoft UAE, added: 'Our partnership with e& enterprise will give a significant boost to driving AI adoption across businesses. By leveraging our combined technological expertise, and the safe and secure properties of Microsoft's cloud and AI services, we are empowering organisations to better detect fraud, improve risk management and deliver personalised services through AI-driven insights to their customers.' The partnership will focus on industry-specific applications for sectors such as public services, telecommunications, education, BFSI (banking, financial services, and insurance), and retail. The approach will be tailored to address the requirements of individual markets while supporting broader sector-wide challenges, including customer engagement, operational efficiency, and data-driven decision-making. AI solutions will be developed and deployed using tools from Microsoft's Azure Cloud ecosystem. These include Azure Machine Learning, Azure Databricks, Azure AI Search, and Azure Synapse Analytics for model training, analytics, and data processing. Microsoft's Azure OpenAI service will support generative AI use cases including customer service automation, content generation, and forecasting. Additionally, Azure Power BI will provide data visualisation and insight-sharing capabilities, while Azure Data Lake Storage and Snowflake will enable scalable data storage and real-time analytics. e& enterprise will integrate these technologies with its own hybrid cloud services. Its Cloud Strategy and Advisory offering will guide adoption planning, with additional support through Migration and Adoption services. Managed Cloud Services and Cloud Security solutions will also be made available to ensure continuity, compliance, and resilience. In line with both organisations' ethical frameworks, the partnership will incorporate Microsoft's Responsible AI principles and e& enterprise's Responsible AI Framework throughout development and deployment, with an emphasis on transparency, data privacy, and regulatory compliance.
CairoScene
13-06-2025
- Business
- CairoScene
e& Enterprise & Microsoft Partner to Accelerate AI Adoption in MENAT
The collaboration aims to deliver scalable, sector-specific AI tools in the UAE, KSA, Egypt, Türkiye and Qatar. Jun 13, 2025 e& enterprise, the digital transformation arm of technology group e&, has expanded its strategic collaboration with Microsoft to accelerate artificial intelligence (AI) and data analytics adoption across industries in the Middle East, North Africa, and Türkiye (MENAT) region. The partnership will target key markets including the UAE, Saudi Arabia, Egypt, Türkiye and Qatar. As part of the partnership, the two companies will co-develop and deploy scalable AI solutions tailored to the needs of specific sectors such as government, telecom, education, banking, and retail. The initiative is designed to support organisations in adapting to growing digital demands by optimising operations, improving customer experience, and advancing innovation through real-time analytics and AI-driven insights. The alliance leverages Microsoft's Azure ecosystem—including Azure Machine Learning, Azure OpenAI, Power BI, and Azure Synapse Analytics—alongside e& enterprise's hybrid cloud solutions and advisory services. Together, these tools will allow businesses to automate workflows, personalise services, manage large datasets, and integrate data visualisation and predictive models into day-to-day operations. To ensure data privacy and ethical use of AI, the partnership will implement Microsoft's Responsible AI standards along with e& enterprise's internal governance frameworks. The companies also plan to support organisations through the full transition process by offering cloud migration services, managed security, and AI implementation roadmaps tailored to local markets.
Forbes
03-06-2025
- Business
- Forbes
Unlocking Value In Data Analytics: The Power Of Cloud Integration Tech
Govinda, Senior Manager at Cognizant, has 14+ years of expertise in SAP & Non-SAP Data Analytics, delivering innovative BI solutions. getty Data analytics platforms like SAP continue to be the foundation of enterprise operations across industries. From managing financial transactions and procurement to overseeing utility billing and customer service, SAP ECC and S/4HANA play a critical role in day-to-day business processes. These systems were built for data integrity, compliance and operational reliability. However, they were not designed to seamlessly support real-time data analytics or integration with cloud-based platforms. In this article, I'll explore how enterprises are overcoming the limitations of traditional data extraction by adopting data analytics platform connectors to enable secure, real-time data integration with cloud platforms. This is a topic that is particularly important to me as a senior manager in the tech space. As business leaders push for faster, data-driven decision-making, IT organizations are expected to deliver insights that go beyond traditional data analytics reporting. Cloud data platforms like Snowflake, Databricks, Azure Synapse Analytics, BigQuery and more offer elastic scalability, multi-source data blending and advanced analytical capabilities that traditional SAP systems lack. For enterprises seeking agility, combining data analytics platform foundations with these cloud data platforms' analytical horsepower is a compelling proposition. This integration, however, requires addressing architectural and operational barriers that stand in the way. Despite data analytics platforms' structured data quality, their architecture is inherently complex. Most modules rely on highly normalized tables where information is distributed across dozens or hundreds of interconnected records. For example, retrieving a single customer invoice might require joining data from multiple master data, transaction and configuration tables. This structure, while ideal for operational consistency, complicates external data access. In addition, many enterprises use custom enhancements or industry-specific solutions like SAP IS-U, which introduce proprietary fields and logic. These customizations increase the difficulty of standardizing data extraction. Security further complicates integration: these platforms' role-based authorization is highly granular, and bypassing it through external tools may introduce compliance risks. Traditional ETL (extract, transform, load) approaches have long been used to extract platform data into data warehouses. However, they suffer from several limitations in today's cloud-first environment: • They often require staging data in intermediate storage layers, increasing latency and cost. • Real-time data capture is difficult, as most rely on scheduled batch jobs. • Platform performance can degrade when large queries are executed repeatedly. • Data transformations often need to be manually maintained in both the ETL tool and target system. Moreover, many off-the-shelf integration tools lack deep awareness of analytics platforms' metadata and relationships. As a result, businesses struggle with partial, inconsistent or outdated datasets in the cloud—defeating the purpose of unified analytics. To address these limitations, organizations are turning to integration solutions. These tools are installed directly within the platform stack and are, specifically in SAP's case, certified by the provider to ensure compliance, performance and extensibility. They operate within the security and metadata framework, allowing them to read data with full awareness of business logic, table relationships and role-based access controls. A leading example of this is SNP Glue, which represents a class of solutions enabling secure, real-time data replication from SAP to cloud platforms. Rather than pulling data externally, these connectors push data from within SAP, minimizing disruption and enhancing control. While SNP Glue is a prominent example, other SAP-certified connectors are emerging with similar capabilities, reflecting a broader industry trend. Connectors like this provide a variety of benefits, including the following: • Real-Time Decision Enablement: Change data capture (CDC) and delta logic minimize load times and ensure cloud platforms reflect the latest analytics platform data. • Security And Governance: Data analytics platforms' internal role structures and authorizations are respected, maintaining enterprise-grade compliance. • Simplified Architecture: No third-party middleware or external staging areas are needed, reducing technical debt. • Support For Custom Enhancements: These tools recognize Z-tables and industry-specific modules, allowing comprehensive data coverage. In the utility industry, SAP IS-U is commonly used for managing metering, billing and customer service. However, operational reporting needs often go beyond what SAP's native tools provide. By integrating SAP IS-U data with cloud platforms like Snowflake, utilities can: • Merge meter reading data with external weather feeds to improve demand forecasting. • Detect anomalies in billing patterns to reduce fraud and revenue leakage. • Optimize field technician routing using geospatial analytics. These capabilities support real-time responsiveness, especially critical in outage management, regulatory reporting and sustainability initiatives. When connecting data analytics platforms with cloud data platforms, there are a few best practices that I would urge you to keep in mind: • Begin With A Data Strategy: Identify high-value use cases, such as forecasting, compliance or service optimization. • Establish Unified Governance: Extend your data catalog and lineage tracking to a cloud platform. • Test Incrementally: Start with a pilot on a specific module before scaling organization-wide. • Align IT And Business Stakeholders: Ensure technical capabilities map to real business outcomes. The integration of data platforms with cloud platforms is not just a technical upgrade—it's a transformation enabler. It can allow organizations to unify core business data with real-time analytics, enabling better forecasting, operational agility and customer insights. This shift is especially valuable in industries with large footprints and high data volatility. Native connectors exemplify how enterprises can modernize without sacrificing governance or performance. By reducing reliance on brittle ETL scripts and manual reconciliations, these tools empower organizations to shift from periodic reporting to continuous intelligence. With cleaner architecture and real-time access to data in cloud platforms, CIOs and data leaders can unlock new business models, drive automation and stay ahead of disruption. As digital transformation accelerates, now is the time to revisit your integration strategy and unlock the full potential of your enterprise data. In this new data economy, data analytics platforms are no longer just systems of record. With the right integration approach, they can become vital engines for innovation, analytics and enterprise-wide decision-making. Forbes Technology Council is an invitation-only community for world-class CIOs, CTOs and technology executives. Do I qualify?
Yahoo
24-03-2025
- Business
- Yahoo
Real-Time Analytics Market Size to Surpass USD 193.71 Billion by 2032 Owing to Rising Adoption of AI and IoT Technologies
The real-time analytics market is experiencing rapid growth due to the increasing adoption of AI, IoT, and big data technologies. Pune, March 24, 2025 (GLOBE NEWSWIRE) -- Real-Time Analytics Market Size Analysis: 'The Real-Time Analytics Market size was USD 25 Billion in 2023 and is expected to reach USD 193.71 Billion by 2032, growing at a CAGR of 25.60% over the forecast period of 2024-2032.'Get a Sample Report of Real-Time Analytics Market@ Major Players Analysis Listed in this Report are: Microsoft (Azure Synapse Analytics, Power BI) SAP (SAP HANA, SAP BusinessObjects) Oracle (Oracle Analytics Cloud, Oracle Exadata) IBM (IBM Watson Analytics, IBM Cognos Analytics) Informatica (Informatica Intelligent Cloud Services, PowerCenter) Amdocs (Amdocs Optima, Amdocs CES) Infosys (Infosys Information Platform, Infosys Nia) Google (Google Cloud BigQuery, Google Cloud Dataflow) Impetus Technologies (DataOps, Real-time Data Analytics) MongoDB (MongoDB Atlas, MongoDB Enterprise Advanced) ADA (ADA Data Solutions, ADA AI Platform) Databricks (Databricks Lakehouse, Delta Lake) Stream Charts (Streamcharts Analytics, Real-time Streaming Dashboard) Palantir Technologies (Palantir Foundry, Palantir Gotham) Salesforce (Salesforce Einstein Analytics, Tableau CRM) Teradata (Teradata Vantage, Teradata IntelliFlex) SAS Institute (SAS Visual Analytics, SAS Viya) Qlik (Qlik Sense, QlikView) Tableau (Tableau Desktop, Tableau Server) Amazon Web Services (Amazon Redshift, Amazon Kinesis MicroStrategy (MicroStrategy Analytics, HyperIntelligence) Cloudera (Cloudera Data Platform, Cloudera Data Science Workbench) Cisco (Cisco Meraki, Cisco Umbrella) Real-Time Analytics Market Report Scope: Report Attributes Details Market Size in 2023 USD 25 Billion Market Size by 2032 USD 193.71 Billion CAGR CAGR of 25.60% From 2024 to 2032 Base Year 2023 Forecast Period 2024-2032 Historical Data 2020-2022 Key Regional Coverage North America (US, Canada, Mexico), Europe (Eastern Europe [Poland, Romania, Hungary, Turkey, Rest of Eastern Europe] Western Europe [Germany, France, UK, Italy, Spain, Netherlands, Switzerland, Austria, Rest of Western Europe]). Asia Pacific (China, India, Japan, South Korea, Vietnam, Singapore, Australia, Rest of Asia Pacific), Middle East & Africa (Middle East [UAE, Egypt, Saudi Arabia, Qatar, Rest of Middle East], Africa [Nigeria, South Africa, Rest of Africa], Latin America (Brazil, Argentina, Colombia Rest of Latin America) Key Growth Drivers • Technological Advancements Empowering Efficient, Cost-Effective, and Scalable Real-Time Data Processing for Business Innovation• Rising Demand for Rapid Decision-Making in Fast-Paced Industries Driving the Need for Real-Time Analytics Do you have any specific queries or need any customization research on Real-Time Analytics Market, Make an Enquiry Now@ Real-Time Analytics Market Expands as AI, 5G, and Cloud Drive Data Revolution The real-time analytics market is booming driven by worldwide data creation, which is estimated to reach 149 zettabytes by 2024. Companies have started leveraging IoT, cloud computing, and big data analytics to deliver faster processing and actionable insights. In 2025, Google will work with The Associated Press to improve AI-generated responses in news, and in 2024, Qlik will integrate Databricks, SAP, and Snowflake to make real-time data access easier. The combination of AI, machine learning, and 5G networks is sparking innovation in healthcare, finance, and manufacturing. Cloud-based offerings further reduce the entry barriers for SMEs, widening the market, and adoption landscape. By Application, Marketing Analytics Dominates, While Customer Analytics Emerges as Fastest-Growing Segment in Real-Time Analytics Market The Marketing Analytics segment has accounted for the largest share of the market, with revenue of around 23% in 2023, due to the rise of businesses that heavily lean toward data-driven marketing strategies. Businesses use real-time analytics to monitor customer feel, change campaigns on the fly, and drive return on investment. With the growth of digital marketing and e-commerce, demand for real-time insights only accelerating. The Customer Analytics segment is expected to grow at the highest CAGR of 28.40% between 2024 and 2032. Companies are now increasingly using real-time analytics to dynamically analyze, understand, and predict individual consumer preferences and behavior, as the focus shifts towards personalized customer experience and retention strategies. By Technology, Streaming Analytics Dominates Market, While Complex Event Processing Sees Fastest Growth in Real-Time Data Analysis In 2023, the Streaming Analytics segment led the market with approx 36% share of the overall revenue. Real-time data analytics capabilities are critically important for businesses to quickly respond to emerging trends and operational problems, especially in sectors like finance, telecommunications, and e-commerce. The Complex Event Processing segment is projected to have the highest CAGR of 27.93%. Being able to process multiple data streams in parallel is critical for industries like IoT, smart systems, and cybersecurity where detecting anomalies and predicting the event as they occur is essential. By End Use, BFSI Sector Dominates Real-Time Analytics Market, While Healthcare Leads with Fastest Growth The BFSI sector dominated the market during 2023, with approximately 24% of revenue share. Evolving Regulatory Landscape Real-time analytics also plays a significant role in the financial industry by helping institutions comply with constantly changing regulations and combating financial crime. The healthcare segment is expected to grow with the fastest CAGR of 29.05% during 2024-2032. Most medical data are currently not monitored in real-time the integration of digital health solutions and connected medical devices in medicine has created a need for real-time analysis of data to promote favorable patient outcomes, streamline hospital activities, and increase medical diagnostics. Real-Time Analytics Market Segmentation: By Application Marketing Analytics Financial Analytics Operational Analytics Customer Analytics Supply Chain Analytics By Deployment Model On-Premises Cloud-Based By Technology Streaming Analytics Complex Event Processing Data Mining Data Warehousing By End Use Retail Healthcare Manufacturing Telecommunications BFSIBuy an Enterprise-User PDF of Real-Time Analytics Market Analysis & Outlook 2024-2032@ North America Dominates, While Asia Pacific Emerges as Fastest-Growing Market In 2023, North America held the largest share of the Real-Time Analytics Market, representing around 40% of the revenue share. Its dominance is backed by a strong technology infrastructure in the region, increased adoption of the cloud and extensive funding of investment in AI and Machine learning. Real-time analytics is making companies across different verticals more efficient, engaged with customers, and risk-averse. The Asia Pacific region is anticipated to hold the highest CAGR of 28.52% from 2024 to 2032. The rapid digital transformation of the region, the government initiatives for the support of AI and IoT, and the growing number of startups is rising demand for real-time analytics solutions. Increasing use cases such as the expansion of e-commerce, smart cities, and 5G networks is also boosting the growth of the market. Recent Developments Microsoft Fabric introduced Real-Time Intelligence features in 2024, including a unified real-time hub for event streaming data, real-time dashboards, and no-code event transformation tools. Future releases will include enhanced data exploration, Copilot assistance, and performance improvements. In March 2024, SAP launched innovations at SAP Data Unleashed, such as the SAP Datasphere knowledge graph for AI-driven insights, the SAP HANA Cloud vector engine for intelligent data applications, and integrations with Collibra for improved AI governance. Table of Contents – Major Key Points 1. Introduction 2. Executive Summary 3. Research Methodology 4. Market Dynamics Impact Analysis 5. Statistical Insights and Trends Reporting 6. Competitive Landscape 7. Real-Time Analytics Market Segmentation, By Technology 8. Real-Time Analytics Market Segmentation, By Application 9. Real-Time Analytics Market Segmentation, By End Use 10. Real-Time Analytics Market Segmentation, By Deployment Model 11. Regional Analysis 12. Company Profiles 13. Use Cases and Best Practices 14. Conclusion Access Complete Report Details of Real-Time Analytics Market Analysis Report 2024-2032@ [For more information or need any customization research mail us at info@ SNS Insider Offering/ Consulting Services: Go To Market Assessment Service Total Addressable Market (TAM) Assessment Competitive Benchmarking and Market Share Gain About Us: SNS Insider is one of the leading market research and consulting agencies that dominates the market research industry globally. Our company's aim is to give clients the knowledge they require in order to function in changing circumstances. In order to give you current, accurate market data, consumer insights, and opinions so that you can make decisions with confidence, we employ a variety of techniques, including surveys, video talks, and focus groups around the in to access your portfolio
