Special Report: Tableau versus Microsoft The demand for effective business intelligence and data visualization tools increased as organizations aim to leverage data for improved decision making and operational efficiency. This latest special report examines the offerings and positioning of two major players in the business intelligence market: Tableau (Salesforce) and Microsoft Power BI. Tableau, a Salesforce company, is known for its user-friendly data visualization capabilities. Tableau's platform allows users to create interactive dashboards and perform detailed data analysis. The platform emphasizes ease of use, community support, and integration with various data sources, enabling users to explore and visualize their data effectively. Microsoft Power BI is a business analytics toolset designed to deliver insights across organizations. Power BI integrates seamlessly with Microsoft's products, including Azure and Office 365. The platform is recognized for its ability to handle large datasets, perform real-time data analysis, and offer advanced data modeling. Power BI focuses on providing enterprise-grade analytics, scalability, and collaboration features. This newest Special Report compares Tableau and Microsoft on key aspects such as usability, integration capabilities, performance, scalability, and support. The goal is to provide a clear understanding of how each platform meets the needs of modern enterprises and supports their data-driven initiatives. Become a #DataLeader to continue reading this Special Report and many more critical thought leadership pieces - dresnerdataleaders.com #businessintelligence #dataanalytics #analytics #Tableau #Microsoft
About us
As an independent industry resource, Dresner Advisory Services provides an alternative perspective on information management, business intelligence, analytics, performance management and related markets through the objective, non-sponsored and crowd-sourced Wisdom of Crowds® industry research. Our Wisdom of Crowds® family of research includes our three Wisdom of Crowds® flagship reports, and wide array of thematic reports: - Analytical Data Infrastructure (Flagship) - BI/Analytics Competency Center - Big Data Analytics - Business Intelligence Market Study (Flagship) - Business Intelligence / Analytical Platforms - Cloud Computing and Business Intelligence - Data Catalog and Governance - Data Pipelines and Integration - Data Preparation - Data Science and Machine Learning - Embedded Business Intelligence - Enterprise Performance Management (Flagship) - Financial Close Management - Natural Language Analytics - Sales Performance Management (SPM) - Self-Service BI/Analytics - Small and Mid-Sized Enterprise Business Intelligence - Small and Mid-Sized Enterprise Performance Management
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- 11-50 employees
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- Privately Held
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- 2007
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- Thought leadership, strategy advisement, and research products
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Special Report: IBM, Oracle, SAP, and Teradata Data Warehouse Platforms The demand for cloud data platforms has increased as organizations look to leverage big data, drive digital transformation, and achieve greater operational agility. The transition from traditional on-premises data warehouses to cloud-based solutions offers advantages such as scalability, flexibility, and cost-efficiency. Our latest special report examines the offerings and positioning of four legacy vendors in the cloud data warehouse space: IBM, Oracle, SAP, Teradata. In an overall view of the market, legacy vendors—typically those that had products available before the onslaught of cloud computing—are frequently pushed to the side, the very fact of their longevity having the effect of making them less shiny and attractive to a new set of buyers. But this typical approach has the effect of overlooking two major strengths that are unique to these legacy vendors. - These legacy database vendors have products which have been in production for longer than the cloud has been in existence and in development, in some cases, for decades. Both the development span, which allows vendors to create truly deep functionality, and the deployment duration, which allowed vendors to work with their enterprise customers to understand issues and address them, contribute to a level of robustness which is not proven in newer, flashier vendors. - These legacy database vendors also have a significant overall market share, based on systems designed and implemented years ago. Moving these systems is nontrivial, and most companies will not move them to other database vendors before they reach their end of life. For legacy systems, this is years, if not decades into the future. Become a #DataLeader to continue reading this Special Report and many more critical thought leadership pieces - dresnerdataleaders.com #datawarehouse #oracle #ibm #SAP #teradata #businessintelligence #data #analytics
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Upsurge in SI and Consultant Use on BI Projects Require a Safety Net. Our latest data show that use and perceived importance of system integrators (SIs) and consultants continues to rise. Organizations increasingly allocate greater percentages of their data and analytics project budgets to the use of SIs and consultants. In addition, expertise matters most when choosing an SI or consultant. And when sourcing that talent, organizations increasingly consider their vendors as trusted advisors that can direct them to the right potential SI and consultant resources to use. Hiring SIs and consultants for an increasing percentage of project responsibilities requires greater expertise in managing the engagements. But in almost no instances—even for the most critical projects—should a data leader look to manage these engagements directly. Instead, a data leader should coordinate as needed with project managers but still primarily focus on influencing the foundations for and use of data and analytics throughout the organization. These data and analytics “guardrails” include data governance principles and programs; data management initiatives such as data integration, data quality, and master data management; guidance on where and when certain analytical techniques are appropriate and which data (and other) sources to use; as well as strategies regarding data availability and use in generative AI. Such prioritization make sense for a data leader—staff handles the granular detail of managing specific (often technically oriented) engagements while the data leader guides proper focus on the principles of Hyper-Decisiveness while data leader guides proper focus on the principles of Hyper-Decisiveness while ensuring initiatives improve the Hyper-Decisive® maturity of the organization. Become a #DataLeader to continue reading this and many more critical thought leadership pieces - https://ow.ly/snk050Sq0JC #businessintelligence #analytics #data #leadership #consultants #systemsintegrators
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FCCR is Not a Data Island Financial key performance indicators (KPIs), financial master data, and financial data are important analytic content. Financial KPIs and other financial analytic content are managed through financial consolidation, close management, and financial reporting (FCCR) capabilities Financial Consolidation, Close Reporting and, which create a consistent and auditable source of financial master data and financial analytic data. Because FCCR capabilities can draw data from multiple transaction systems, they provide the best way to link financial data with other enterprise data through data catalogs and other data management capabilities. However, as its name implies, FCCR has a heavy focus on the needs of the finance function. It also straddles the boundary between analytical data infrastructure (ADI) and operational data infrastructure (ODI). As such, organizations often treat FCCR as a finance-only domain application, rather than a key source of analytic content for the enterprise, with many data leaders perceiving it as a “finance-only” solution because it focuses on specialized capabilities such as financial consolidation and close management. FCCR’s position as a sub segment of the enterprise performance management (EPM) software market can overshadow the importance of FCCR. Budgeting, planning, forecasting, and management reporting have a higher profile in most organizations because these capabilities are widely used outside finance. EPM software appears to positively impact success with business intelligence (BI) while FCCR on its own does not, despite its role as a key foundation for EPM especially in organizations with multiple legal entities. Consequently, data leaders must ensure that FCCR is not treated as a “finance-only” capability. FCCR plays a key role in improving access to analytic content and increasing trust in data, both of which are fundamental to achieving higher levels of hyper-decisive maturity. Data leaders must therefore ensure FCCR solutions complement and augment data catalogs and other analytical data infrastructure capabilities and are not locked away in a finance-only data island. Become a #DataLeader to continue reading this and many more critical thought leadership pieces - www.dresnerdataleaders.com #businessintelligence #analytics #data #leadership #EPM #FCCR #finance
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Data Mesh and Fabric Confusing? Unpacking Next-Gen Data Architectures. Data architectures and their supporting technologies and capabilities face increased technology and business pressure to meet the requirements of more complex, diverse, and distributed business intelligence (BI) and analytics use cases and applications. The scale, distribution, mission-criticality, and pace of change facing data leaders and their teams are outpacing the ability of current architectural approaches. Data architectures that lack flexibility, adaptability, and scalability lead to challenges as organizations struggle to capture benefits and achieve positive returns from their BI investments. Active Data ArchitectureTM supports a platform-independent layer that sits between physical data stores and points of data consumption. It includes various data management capabilities, including virtualized and distributed data access, data governance, and security. At its core, Active Data Architecture serves as an abstraction layer, translating business and physical structures. It is an architecture dynamically optimized for performance, scalability, and cost management. Data mesh and data fabric are often associated with an Active Data Architecture. They involve managing distributed data, enabling consolidated data views, and provisioning data to various process and application points of consumption. Data mesh links together distributed data sources and enables these capabilities in a preprogrammed, practitioner-managed, and manually optimized fashion. Data fabric builds on these same capabilities and adds elements of automation to help make the Active Data Architecture truly dynamic, self-organizing, and continually optimized. By gaining an understanding of Active Data Architecture and supporting concepts (including data mesh and data fabric), and applying existing data engineering capabilities toward these opportunities, organizations can better enable delivery of data products, help create more business value from data, and move their organizations further toward being Hyper-Decisive. Data leaders can best position themselves and their teams for success by educating key constituents on the value of Active Data Architecture concepts, and linking the ideas with current and future strategic BI investments. They also must develop a plan for showing the value of this new approach, expressing it in metrics such as speed of deployment, performance, reliability, and adaptability. Become a #DataLeader to continue reading this and many more critical thought leadership pieces - https://ow.ly/jlRP50RXVK0 #businessintelligence #analytics #data #leadership #datamesh #datafabric #semanticlayer #datavirtualization
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