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Executive Summary | Multiplatform Data Architectures

We’re experiencing a time of great change as data evolves into greater diversity (more data types, sources, schema, and latencies) and as user organizations diversify the ways they use data for business value (especially via advanced analytics). To capture distributed enterprise data, big data, and other new data assets—plus leverage them fully for business advantage—user organizations are expanding and integrating their portfolios of data platforms and tools to create what TDWI calls multiplatform data architectures (MDAs).

An MDA is an eclectic mix of old and new data, managed on traditional and modern data platforms, whether on premises or in the cloud, with diverse tool types from many providers, stitched together by some form of data architecture. Synonyms include enterprise data architecture, hybrid data ecosystem, distributed data architecture, and data fabric.

MDAs are already in production in many multiplatform data warehouse environments and the hybrid data environments of customer relationship management (CRM) and sales force automation (SFA). TDWI has also seen MDAs for analytics programs, the online supply chain, and capturing data from the Internet of Things (IoT) for both operations and analytics.

According to this report’s survey, the leading challenges to MDA success are governance, complexity, cost, data quality, and a lack of skills. However, users overcome these barriers, such that the chief beneficiaries of MDAs are analytics, self-service data practices, the leverage of new data assets, and business collaboration via integrated data. Eighty percent of users surveyed think that an MDA is more opportunity than problem, 83% feel MDAs are critical to their data strategy, and 57% have experienced improved business outcomes because of their MDAs.

Data managed within an MDA can include traditional enterprise data (61%), mixtures of old and new data (21%), and modern data such as big data, Web data, social media, and data from IoT (15%). Given that an MDA’s data is heterogeneous in the extreme, satisfying the storage and analytics requirements of all these data types demands an equally heterogeneous portfolio of data platform types, including relational databases (56%), analytics databases (31%), Hadoop (43%), and cloud storage (21%).

Note that a multiplatform data architecture (MDA) is not a mere bucket of siloed platforms. Instead, an MDA is a collection of related platforms unified into a true data architecture through several means, such as data integration infrastructure, multiple approaches to metadata and other semantics, data virtualization, data governance, and shared data models and other enterprise data standards.

The data architecture for a multiplatform environment is created one thread at a time. Each thread reaches across multiple platforms to create and leverage relationships among physically distributed data elements and platform functionality. The threads then weave together into a data architecture.

An MDA’s cross-platform threads can take many technical forms, including workflows, dataflows, and data pipelining, plus orchestration to control and optimize these. However, a truly modern data architecture will also rely on virtual threads, ranging from simple federated queries to sophisticated modeling and interfacing via semantics-driven data views.

This report explains in detail what MDAs are and do, with a focus on helping data professionals and their business counterparts worldwide architect, govern, and grow their MDAs for better business outcomes via well-integrated and unified distributed data from many sources.

Datometry, SAP, Sqream, StreamSets, and Talend sponsored the research and writing of this report.

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About the Author

Philip Russom, Ph.D., is senior director of TDWI Research for data management and is a well-known figure in data warehousing, integration, and quality, having published over 600 research reports, magazine articles, opinion columns, and speeches over a 20-year period. Before joining TDWI in 2005, Russom was an industry analyst covering data management at Forrester Research and Giga Information Group. He also ran his own business as an independent industry analyst and consultant, was a contributing editor with leading IT magazines, and a product manager at database vendors. His Ph.D. is from Yale. You can reach him by email ([email protected]), on Twitter (, and on LinkedIn (

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