The Forrester Wave : Enterprise Data Virtualization, Q1 2015

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1 For: Enterprise Architecture Professionals The Forrester Wave : Enterprise Data Virtualization, Q by Noel Yuhanna, March 11, 2015 Key Takeaways Cisco Systems, Denodo Technologies, IBM, Informatica, Oracle, And SAP Lead The Pack Although there are more than two dozen data virtualization solutions, the vendors in this wave were picked based on their offerings and credible deployments. Forrester s research uncovered a market where Cisco Systems, Denodo Technologies, IBM, Informatica, Oracle, and SAP lead the pack. Microsoft, Red Hat, and SAS Institute offer competitive options. The Data Virtualization Market Is Growing As Enterprises Look At New Use Cases The data virtualization market is growing because more EA pros see data virtualization as a way to address the demand for trusted and secure data in real-time. This market growth is partly due to enterprise architects increasing trust of data virtualization providers to act as strategic partners, advising them on key decisions. Scale, Performance, Security, And A Broader Level Of Integration Are Key Differentiators Batch-oriented technologies that physically move data need to be complemented with other real-time integration partners, such as data virtualization, to provide more up-todate information along with business self-service. Vendors that lack innovation to meet new data requirements are likely to be phased out in this competitive landscape. Access The Forrester Wave Model For Deeper Insight Use the detailed Forrester Wave model to view every piece of data used to score participating vendors and create a custom vendor shortlist. Access the report online and download the Excel tool using the link in the right-hand column under Tools & Templates. Alter Forrester s weightings to tailor the Forrester Wave model to your specifications. Forrester Research, Inc., 60 Acorn Park Drive, Cambridge, MA USA Tel: Fax:

2 March 11, 2015 The Forrester Wave : Enterprise Data Virtualization, Q An Evaluation Of Nine Data Virtualization Vendors by Noel Yuhanna with Leslie Owens and Elizabeth Cullen Why Read This Report As enterprise architects look at how to deliver a trusted, real-time, integrated, and secure data platform to support applications, they look at data virtualization. In the three years since Forrester s last evaluation, data virtualization vendors have improved their security, scalability, big data, data discovery, data quality, and cloud capabilities. Forrester has evaluated nine vendors Cisco Systems, Denodo Technologies, IBM, Informatica, Microsoft, Oracle, Red Hat, SAP, and SAS Institute on 60 criteria for current offering, strategy, and market presence. This report details our findings on how well each solution fulfills the criteria and where it stands in relation to other vendors. Enterprise architecture (EA) professionals can customize the requirements to re-rank the vendors according to their specific business technology needs. Table Of Contents 2 Data Virtualization s Flexible Architecture Drives Broader Adoption Data Virtualization Use Cases Expand Beyond Simple Federation The Data Virtualization Market Is Hot With Vendors Innovating And Expanding Features Enterprise Data Virtualization Evaluation Overview The Evaluation Criteria Focus On Scale, Performance, Cloud, And Appliances Evaluated Vendors Meet Functional, Architectural, And Market Presence Criteria Enterprises Have More Choices To Support Any Requirements Vendor Profiles Notes & Resources Forrester conducted executive briefings and product demonstrations in Q with nine vendor companies including Cisco Systems, Denodo Technologies, IBM, Informatica, Microsoft, Oracle, SAP, and SAS Institute. Forrester also spoke with two customers of each company. Related Research Documents Market Overview: Big Data Integration December 5, 2014 The Forrester Wave : Big Data Hadoop Solutions, Q February 27, 2014 The Forrester Wave : Enterprise Data Warehouse, Q December 9, 2013 Leaders Strong Performers 14 Supplemental Material 2015, Forrester Research, Inc. All rights reserved. Unauthorized reproduction is strictly prohibited. Information is based on best available resources. Opinions reflect judgment at the time and are subject to change. Forrester, Technographics, Forrester Wave, RoleView, TechRadar, and Total Economic Impact are trademarks of Forrester Research, Inc. All other trademarks are the property of their respective companies. To purchase reprints of this document, please clientsupport@forrester.com. For additional information, go to

3 The Forrester Wave : Enterprise Data Virtualization, Q Data Virtualization s Flexible Architecture Drives Broader Adoption The data virtualization market is growing as enterprise architects adopt the technology to support requirements for secure, self-service access to real-time data. These solutions provide a virtualized data services layer that integrates data from heterogeneous data sources and content in real-time, near-real time, or batch as needed to support a wide range of applications and processes. Data provided through the data services layer can be updated, transformed, and/or cleansed when (or before) applications access it. Data virtualization can do more than just basic federation; it can do transactions that write back to the original data source. In the early years of this market, most data virtualization was focused primarily on the financial services, telecom, and government sectors. In the past three years, however, Forrester has seen significantly increased adoption in other verticals, such as insurance, retail, healthcare, manufacturing, and ecommerce. Forrester defines enterprise data virtualization as: The integration of any data in real-time or near-real time from disparate structured, unstructured, and semi-structured data sources, whether on-premises or cloud, into coherent data services that support business transactions, analytics, predictive analytics, and other workloads and patterns. Data Virtualization Use Cases Expand Beyond Simple Federation The data virtualization market continues to change, as tech management professionals integrate diverse data to support mobile, social, and personal experiences. Data virtualization solutions support a range of use cases, such as: A 360-degree view of the business, customer, product, and employee. A company may want to learn about customer reaction to a product launch and how it compares with previous launches. Data virtualization can extract and transform data from social networks, such as Facebook and Twitter, and can then further integrate this data with weblogs and clickstreams from other sources (including internal) and finally integrate it with data warehouse and customer relationship management (CRM) data. Data virtualization delivers all kinds of unified data views to deliver new insights and predictive analytics. Real-time data sharing across lines of business, partners, and the enterprise. With growing data volumes and silos in most organizations, data sharing and collaboration is a major challenge. In large environments, widespread data movement is impractical, especially when dealing with hundreds of terabytes and petabytes of information. Data virtualization allows any application, process, user, or tool to access any business data regardless of its physical or logical location and data format. Information can be requested using standard SQL, XQuery, simple object access protocol (SOAP), or representational state transfer (REST) calls to access any business data. Data virtualization also eliminates excessive duplication of data, focusing on consistent and quality information for users and applications as well as reducing storage requirements.

4 The Forrester Wave : Enterprise Data Virtualization, Q Self-service data platform for tech management and business users. For business users, a data virtualization platform helps deliver easier and faster discovery, navigation, and consumption of data. For tech management, data virtualization enables developers and architects to map data sources faster, ensure greater data security and availability, and focus on business issues rather than deal with technology challenges. While every vendor has a self-service story to tell, most of vendor solutions are still ramping up their self-service capabilities. Securing critical data. Today, most enterprise architects and security professionals struggle to improve data security or meet compliance requirements, due to growing data silos and increasing data volumes. Applying uniform access control policies across databases, data warehouses, Hadoop, NoSQL, and files has become extremely challenging. Data virtualization helps overcome this challenge by centralizing data access through a common data platform and ensuring regulated control of private data, especially if the data comes from disparate data sources. However, it also offers a bypass option when necessary to bypass the policies and let the data source deal with authorization and access control. Delivering higher performance and scalability. Disk latency remains the biggest obstacle to supporting high-performance applications. Previous generations of distributed data access platforms were hampered by a lack of virtualized infrastructure. Such infrastructure now enables data virtualization to leverage in-memory, storage, server, and network capacity when and where needed. Distributed in-memory is an important component of data virtualization that delivers horizontal scale by clustering many servers to support high performance transactional and query workloads. The Data Virtualization Market Is Hot With Vendors Innovating And Expanding Features The data virtualization market is thriving, with more mature solutions available to support very large and complex deployments. Consulting firms, such as Accenture, BearingPoint Consulting, Deloitte, IBM Global Business Services, Infosys, Wipro, HCL Technologies, and Tech Mahindra, are aggressively seeking opportunities. Top vendors, such as Cisco Systems, Denodo Technologies, IBM, Informatica, Microsoft, SAP, and Red Hat, continue to extend their products and innovate in delivering enterprisewide data virtualization solutions. Oracle has significantly improved its position from being a Strong Performer to a Leader since the 2012 Forrester Wave evaluation, largely due to its continued focus on data integration and data management. SAS Institute, a new entrant in this Forrester Wave, is a Strong Performer based on its core data management capabilities, and is likely to compete with other large data virtualization players as it ramps up its offering in the coming years. The data virtualization market can be segmented into three main categories:

5 The Forrester Wave : Enterprise Data Virtualization, Q Large software vendors that offer broadest coverage and most options. The data virtualization market continues to be dominated by large vendors, such as Cisco Systems, Informatica, IBM, Microsoft, Oracle, SAP, SAS Institute, and Red Hat, that offer broad coverage to support most use cases. These large vendors continue to invest heavily in data virtualization and market their products aggressively. Specialized pure-play vendors offer greater automation and lower cost. The specialized vendors, such as Denodo Technologies, Radiant Logic, and Stone Bond Technologies, offer more integrated solutions and greater automation, ease of use, and simplicity to speed up development and deployment of data virtualization. System integrators help install, configure, and manage data virtualization solutions. When performing a very large and complex data virtualization initiative, systems integrators (SIs) often play an important role. Providers, such as Accenture, BearingPoint Consulting, Computer Sciences Corp (CSC), Deloitte, Goldman-Sachs, HCL Technologies, HP Enterprise Services (formerly Electronic Data Systems), IBM Global Business Services, Infosys, SAP, Tata Consultancy Services (TCS), Tech Mahindra, and Wipro, offer a range of consulting services to support any use case. Forrester estimates that two-thirds of large Fortune 5000 companies typically use a SI to help with enterprise data virtualization strategy. 1 Enterprise Data Virtualization Evaluation Overview To assess the state of the data virtualization market and see how the vendors stack up against each other, Forrester evaluated the strengths and weaknesses of top data virtualization vendors. The Evaluation Criteria Focus On Scale, Performance, Cloud, And Appliances After examining past research, user need assessments, as well as interviewing vendors and experts, Forrester developed a comprehensive set of evaluation criteria. We evaluated vendors against 60 criteria, which we grouped into three high-level buckets: Current offering. To assess the breadth and depth of each vendor s data virtualization product set, we evaluated each solution s architectural and operational functionality. Strategy. We reviewed each vendor s strategy to assess how each vendor plans to evolve its data virtualization solution to meet emerging customer demands. We also evaluated each vendor s go-to-market approach, commitment, and direction strategies. Market presence. To establish each data virtualization product s market presence, we evaluated each solution provider s company financials, adoption, and partnerships.

6 The Forrester Wave : Enterprise Data Virtualization, Q Evaluated Vendors Meet Functional, Architectural, And Market Presence Criteria After examining past research, user need assessments, and conducting vendor and expert interviews, we developed a comprehensive set of evaluation criteria. Each of these vendors has: An established enterprise-class data virtualization offering. The vendors actively market a productized data virtualization solution that includes data virtualization modeling, integration, security, transformation, quality, performance, delivery, and development. It features functionality to support on-premises, cloud, hybrid, big data, in-memory, and various new and traditional data sources. The solution supports key elements of information fabric 3.0, including: application programming interfaces (APIs)/connectors, dynamic discovery, data transformation, data profiling, data quality, transaction management, analytics management, logical/canonical model, data security, replication, monitoring, data governance, metadata management, external processing engine, and intelligent in-memory data fabric. A referenceable installed base. The vendors have 25 or more enterprise customers using their product that span more than one major geographical region. A standalone data virtualization solution. The vendors products are not technologically tied to any applications (such as enterprise resource planning [ERP] or CRM), nor business intelligence (BI); business performance solution (BPS); predictive analytics; extract, transform, and load (ETL), or middleware stack. It is supported as a standalone environment. Significant data virtualization revenue. The vendors have at least $10 million (USD) in data virtualization-specific revenues in the latest fiscal calendar year, where at least 80% of revenues derive from data virtualization solutions, not professional services. Publicly available data virtualization release. The vendors actively market an enterprise data virtualization-like solution. It may be called something else by the vendor, but the solution s characteristics and framework resemble that of a data virtualization architecture. The vendor must have initially released the product version included in the evaluation prior to September 1, Significant interest from Forrester clients. Forrester only included vendors that were mentioned by customers in at least 10 separate Forrester inquiry calls during the past 12 months. Through these detailed conversations, we prioritized criteria and vendors based on enterprise needs.

7 The Forrester Wave : Enterprise Data Virtualization, Q Enterprises Have More Choices To Support Any Requirements Forrester s evaluation of data virtualization solutions reveals six leaders and three strong performers. The evaluation uncovered a market in which (see Figure 1 and see Figure 2): Informatica, IBM, Denodo Technologies, Cisco Systems, SAP, and Oracle lead the pack. These Leaders offer innovative, proven platforms that support large enterprises data virtualization needs, some of which run into hundreds of terabytes into petabytes. Forrester raised the bar on various criteria and introduced new ones in this evaluation to address new and evolving customer requirements. Informatica and IBM continue to hold strong leadership positions and often remain the most popular vendors shortlisted by large enterprises. Denodo Technologies has improved significantly in this Wave, gaining an improved leadership position through continuous product innovation and enhancements. Through its acquisition of Composite Software in 2013, Cisco Systems entered the data virtualization market and is posed to ramp up its plans and efforts to deliver new and innovative features. SAP has invigorated its data virtualization strategy through the SAP Hana platform, integrating various core data management capabilities to deliver real-time and near-real time use cases. Oracle has newly entered the Forrester Wave as a participating vendor with strong strategy and good product offering to support most data virtualization use cases. Red Hat, SAS Institute, and Microsoft offer competitive options. Red Hat is the only leading open source vendor specializing in data virtualization and integration. Although Microsoft is not seen marketing data virtualization, it offers viable option to customers that primarily have data on Windows and cloud, and already use Visual Studio, SQL Server, and BizTalk. SAS, mostly known for its advanced analytics, business intelligence, data management and predictive analytics solutions, has done well in its first appearance in this Forrester Wave, by leveraging its existing data platform. Forrester believes that SAS is poised to extend its data virtualization offering in the coming years to compete against the leading vendors. This evaluation of the data virtualization market is intended to be a starting point only. We encourage clients to view detailed product evaluations and adapt criteria weightings to fit their individual needs through the Forrester Wave Excel-based vendor comparison tool.

8 The Forrester Wave : Enterprise Data Virtualization, Q Figure 1 Evaluated Vendors: Product Information And Selection Criteria Vendor Cisco Systems Denodo Technologies IBM Product evaluated Cisco Information Server Denodo Platform InfoSphere Federation Server InfoSphere Information Server InfoSphere MDM InfoSphere BigInsights Product version evaluated CIS Informatica Informatica Platform Microsoft SQL Server 2014 BizTalk Server 2013 Visual Studio 2013 Microsoft Azure SharePoint Server 2013 Excel 2013 Power BI for Office 365 PolyBase OData Oracle Oracle Data Service Integrator Oracle Data Integration Suite - Oracle WebLogic Suite - Oracle Service Bus - Coherence Enterprise Edition - Oracle BPEL Process Manager - Oracle Data Relationship Manager - Oracle GoldenGate - Oracle Enterprise Data Quality - Oracle Enterprise Metadata Management Oracle Database - Database In-Memory - Big Data SQL Oracle Event Processor Oracle Integration Cloud Service Oracle Industry Models Oracle Master Data Management 12c 11g Red Hat Red Hat JBoss Data Virtualization 6.0 SAP SAP Data Services SAP Information Steward SAP Process Orchestration SAP Replication Server SAP Landscape Transformation Replication Server SAP Hana Smart Data Access 4.2 SP3 4.2 SP3 7.4 SP SP SP8 SAS Institute SAS Federation Server 4.1 Source: Forrester Research, Inc. Unauthorized reproduction or distribution prohibited.

9 The Forrester Wave : Enterprise Data Virtualization, Q Figure 1 Evaluated Vendors: Product Information And Selection Criteria (Cont.) Vendor selection criteria The vendors actively market a productized data virtualization solution that includes data virtualization modeling, integration, security, transformation, quality, performance, delivery, and development. Each solution has features and functionality to support on-premises, cloud, hybrid, big data, in-memory, and various new and traditional data sources. It supports key elements of information fabric 3.0 including: application programming interfaces (APIs)/connectors, dynamic discovery, data transformation, data profiling, data quality, transaction management, analytics management, logical/canonical model, data security, replication, monitoring, data governance, metadata management, external processing engine, and intelligent in-memory data fabric. All of the evaluated data virtualization vendors have 25 or more enterprise customers using their product that span more than one major geographical region. The vendors products are not technologically tied to any applications (such as enterprise resource planning [ERP] or customer relationship management [CRM]), nor business intelligence (BI); business performance solution (BPS); predictive analytics; extract, transform, and load (ETL); or middleware stack. It is supported as a standalone environment. The vendors have at least $10 million (USD) in data virtualization-specific revenues in the latest fiscal calendar year, where at least 80% of revenues derive from data virtualization solutions, not professional services. The vendors actively market an enterprise data virtualization-like solution. It may be called something else by the vendor but the solution s characteristics and framework resemble that of a data virtualization architecture. The vendor must have initially released the product version included in the evaluation prior to September 1, Forrester only included vendors that have been mentioned by customers in at least 10 separate Forrester inquiry calls during the past 12 months. Source: Forrester Research, Inc. Unauthorized reproduction or distribution prohibited.

10 The Forrester Wave : Enterprise Data Virtualization, Q Figure 2 The Forrester Wave : Enterprise Data Virtualization, Q1 15 Strong Current offering Risky Bets Contenders Strong Performers Leaders Informatica Denodo Technologies IBM Oracle SAP Cisco Systems Microsoft Red Hat SAS Institute Go to Forrester.com to download the Forrester Wave tool for more detailed product evaluations, feature comparisons, and customizable rankings. Market presence Weak Weak Strategy Strong Source: Forrester Research, Inc. Unauthorized reproduction or distribution prohibited.

11 The Forrester Wave : Enterprise Data Virtualization, Q Figure 2 The Forrester Wave : Enterprise Data Virtualization, Q1 15 (Cont.) Forrester s Weighting Cisco Systems Denodo Technologies IBM Informatica Microsoft Oracle Red Hat SAP SAS Institute CURRENT OFFERING Data sources Development Quality and transformation Performance and scalability Administration Delivery 50% 20% 20% 15% 20% 15% 10% STRATEGY Product strategy and vision Corporate strategy Licensing and cost 50% 50% 50% 0% MARKET PRESENCE Adoption Revenue Partners and office locations 0% 40% 30% 30% All scores are based on a scale of 0 (weak) to 5 (strong). Source: Forrester Research, Inc. Unauthorized reproduction or distribution prohibited. Vendor Profiles Leaders Informatica executes on its innovative vision and increased adoption. Informatica sells one of the leading agile data integration platforms. Today, more than 5,000 companies worldwide use Informatica for their information management initiatives. Enterprise architects find Informatica s data virtualization delivers very good support for large structured data sets when integrating data with cloud sources, such as software-as-a-service (SaaS), extending existing ETL frameworks to support real-time initiatives, and data quality to ensure a high degree of accuracy. Overall, Informatica has a good vision and strong strategy, focusing on: 1) delivering all aspects of data management in real-time; 2) support for distributed in-memory data fabric; and 3) a comprehensive self-service platform that focuses on business users. Customers like Informatica s advanced data quality and profiling features, model-driven approach to provision

12 The Forrester Wave : Enterprise Data Virtualization, Q data services, role-specific tools, and drag-and-drop modeling capabilities. With release of Springbok a self-service data preparation tool that allows analyst and business users to directly exploit data without involving IT Informatica is starting to break-away from its traditional data integration category toward an intelligent data platform that is real-time, selfservice, and agile. Customer references pointed out that they were using Informatica to support data warehouse augmentation, federation of master data, big data analytics, and real-time analytics. However, some customers report that Informatica data virtualization suite is overkill when it comes to supporting small to midsized data virtualization projects. Forrester finds that most of Informatica s customers are large billion dollar companies, but with its Springbok product and low-cost cloud solutions, the vendor is likely to expand its adoption to the midmarket in the coming year. IBM has a broad solution with deep services to support large and complex initiatives. IBM has consistently invested in enhancing its data virtualization platform with a coherent long-term vision. IBM s solution is a good fit when it comes to integrating various legacy data sources, such as IBM DB2 z/os, IBM IMS, Software AG Adabas, and IDMS, or when application sources are not well documented. In addition, several reference customers mentioned that IBM Global Business Services helped them to implement data virtualization faster by discovering data, and building custom data models for their specific needs. Today, customers deploy IBM s solution across industries to support large and broader use cases that include a 360-degree view of the business, customer, and product; real-time analytics; mobile data platform; data warehouse/data mart consolidation; data migrations; peer-to-peer federation; and big data. Unlike some of the other solutions, IBM data virtualization is composed of several core and mature IBM products, such as the InfoSphere Information Server, InfoSphere Federation Server, InfoSphere MDM, and InfoSphere Big Insights. Although these products share a common metadata, enhancing these products to support new features and functionality, such as real-time data, self-service, and Agile development, has been challenging. IBM is likely to roll-out new products and solutions that will address new data platform requirements especially around distributed inmemory fabric, real-time discovery, and tighter integration with social media and Internet-of- Things (IoT) sources. Some customers pointed out that since the IBM solution is too complex and broad, it takes time to install, design, and deploy, especially to support small to midsized projects. For larger deployments, existing IBM customers already employing InfoSphere DataStage and InfoSphere QualityStage reported that transitioning to data virtualization was quicker and offered a reduced learning curve as well as cost savings. Denodo Technologies innovates to meet enterprise demands. Unlike other large software vendors in this wave, Denodo is a pure-play data virtualization vendor. Today, more than 250 enterprises across financial services, insurance, retail, life sciences, and government rely on Denodo to support their mission-critical data virtualization deployments. Customers appreciate Denodo s easy-to-use, tight integration with non-structured data, simple yet sophisticated data modeling capabilities, search, data life-cycle management, and support for comprehensive lists

13 The Forrester Wave : Enterprise Data Virtualization, Q of data sources including big data, IoT, and cloud sources. In addition, customers often mention lower cost and faster time-to-value are top reasons for selecting Denodo. Denodo s recent launch of Denodo Express a free data virtualization solution with community support is likely to drive awareness for the brand, especially in the midsize market and partner/si market. Forrester finds that Denodo is executing well on its vision to support more enhanced connectors and adapters to new sources; improve scale and performance to support complex and real-time analytics; and integrate with leading in-memory computing and cloud platforms. Customer references reported that they were using Denodo to support operational, analytical, and big data workloads, while others were expanding their deployments to support more data sources, such as social media, and deliver a broader 360-degree-view of the business and customer. Cisco Systems gets onto the map with its acquisition of Composite Software. Cisco s acquisition of Composite Software in 2013 was surprising and even questionable, given Cisco s lack of a data management offering. However, the acquisition has paid off, with Cisco significantly increasing the install base through improved capabilities around performance, scalability, security, and integration with Hadoop and IoT technologies. Based on customer feedback, Cisco supports some of the most complex data virtualization deployments in the world, in part because it s offering has been active in the market as long as or longer than any other player. Some customers are using Cisco to support petabyte-scaled data volume, while others are integrating with various IoT device data to do predictive analytics. Cisco s vision is to continue to expand its core functionality to support more real-time data platforms, self-service business access and sandboxing, enhanced big data integration, scalable cloud integration, and extending Internet of Everything (IoE) applications integration. Today, BI and analytics typically form the biggest set of use cases for Cisco, but enterprises are starting to expand into big data analytics, IoT, and age-of-the-customer applications across various verticals. Forrester believes that in the coming years, Cisco will integrate its data virtualization solution with its network routers and switches to deliver more data intelligence, optimizing data movement and network traffic to support faster data access across data centers and cloud. SAP Hana is forming the backbone of its data virtualization offering. Unlike other vendors, SAP offers a strong distributed in-memory SAP Hana data platform that can store, process, and access data to support low-latency driven applications and real-time insights. The combination of SAP Hana in-memory data fabric for on-premises and cloud, as well as its data management services, SAP Enterprise Information Management delivers a real-time data platform that allows integrating data from many sources quickly, and performing transactional, operational, and analytical workloads in the same platform. However, SAP is not yet done integrating all of the data management components with SAP Hana, such as data quality, data modeling, security, and transformation. SAP is seen extending its framework to support complex data virtualization requirements to support big data, real-time, and self-service use cases. Reference customers mentioned that SAP s data virtualization solution has helped them deploy data virtualization framework quickly for their SAP applications and SAP data sources. However, a few recounted difficulties updating the products with multiple regression tests showing loss

14 The Forrester Wave : Enterprise Data Virtualization, Q of functionalities that requires the company to restart the process a few times. SAP s road map focuses on delivering petabyte scaled in-memory data fabric that integrates with all of the data virtualization services layer to support all types of use cases in real-time. Oracle delivers a viable solution to support most data virtualization use cases. Although Oracle does not market a data virtualization solution, the combination of Oracle Data Integration Suite, Oracle GoldenGate, Oracle Database, and other related products, continue to help customers deploy data virtualization frameworks. Oracle s jump into the Leaders category demonstrates it has strong data management and virtualization capabilities, and threatens leading vendors, especially as Oracle further integrates its core data management capabilities with its new in-memory data fabric, expands its cloud offering, and integrates with its hardware appliances. Reference customers mentioned Oracle s ability to support very large volumes of data with fast replication using Oracle GoldenGate to support real-time queries. However, some customers are concerned that Oracle s red stack focus is too narrow for their data virtualization frameworks that require broader support for non-oracle data sources. Strong Performers Red Hat s open source offering makes it a unique vendor. Red Hat offers an open source alternative to enterprises that need more customization to integrate with unique data sets, IoT devices, complex platforms, and legacy systems and applications. It leverages community-driven innovation and an open source development model to provide enterprise-class products that are lightweight, lower-cost, and open source. Although not all of the Red Hat components are open source, its core open source data services platform can support most integration requirements and can avoid vendor lock-in. Forrester estimates that more than 400 enterprises are using Red Hat s data virtualization offering to support use cases, such as data federation, service-oriented architecture (SOA)-based application integration, real-time integration, self-service BI, agile SOA, and a 360-degree view of the customer and business, across financial services, government agencies, retail, insurance, and telecom. Red Hat still has work to do in order to challenge the Leaders, especially around support for documents and geospatial data sources, lineage, transaction management, self-service, and search capabilities, as well as integration with leading packaged applications. Microsoft offers a compelling low-cost solution that s suitable with its other platforms. The combination of Microsoft BizTalk, SQL Server, SQL Server Integration Services (SSIS), Microsoft Azure, and Visual Studio delivers an integrated framework to support data virtualization initiatives. Microsoft continues to extend this framework to support more data sources, such as unstructured and semistructured data, as well as improve on performance, integration of external data sources (such as SaaS and social media), and deliver real-time security features. Although Microsoft does not actively market a productized data virtualization solution, thousands of customers are using the Microsoft solution to support various data virtualization initiatives including agile BI, a single version of the truth, customer analytics, and

15 The Forrester Wave : Enterprise Data Virtualization, Q enterprise search. Microsoft customers pointed out that the solution often requires considerable time and resources to develop, deploy, and manage their data virtualization largely because the suite lacks automation, comprehensive integration of the products and common metadata. Microsoft s road map includes providing tighter integration with various data management services, improving dynamic data quality, elaborating on a security offering that includes data masking and end-to-end encryption, and integrating with more cloud data sources. We find that Microsoft s solution is a good fit when data sources are primarily SQL Server, Exchange, Microsoft SharePoint, and Microsoft Office, and the organization already has some Visual Studio expertise in-house to build data virtualization data models. SAS Institute extends its existing data management capabilities to include data virtualization. SAS is best known for its advanced analytics, self-service BI, big data analytics, and data management, but when it comes to data virtualization only a few enterprises know of such capabilities. SAS s broad data management capabilities, especially around transformation, data quality, modeling, data discovery, metadata management, and security, have helped it build a data virtualization offering. Today, more than 100 enterprises use SAS Federation Server product to support use cases such as self-service BI, data warehouse augmentation, big data virtualization, and a 360-degree view of the customer and business. SAS s entry into the data virtualization market is largely driven by market demand, where enterprises need to integrate data from many sources in real-time, bypassing the traditional ETL stack that often slows down data delivery. However, some customers worry that SAS data virtualization solution is not mature enough to support large and complex initiatives. SAS s road map focuses on improving security and data quality as well as utilizing distributed in-memory architectures. Supplemental Material Online Resource The online version of Figure 2 is an Excel-based vendor comparison tool that provides detailed product evaluations and customizable rankings. Data Sources Used In This Forrester Wave Forrester used a combination of four data sources to assess the strengths and weaknesses of each solution: Vendor surveys. Forrester surveyed vendors on their capabilities as they relate to the evaluation criteria.

16 The Forrester Wave : Enterprise Data Virtualization, Q Executive briefings. Once we analyzed the completed vendor surveys, we conducted vendor calls where each discussed the company s background, positioning, value proposition, customer base, and strategic vision. Product demos. We asked vendors to conduct demonstrations of their product s functionality. We used findings from these product demos to validate details of each vendor s product capabilities. Customer reference calls. To validate product and vendor qualifications, Forrester also conducted reference calls with two of each vendor s current customers. The Forrester Wave Methodology We conduct primary research to develop a list of vendors that meet our criteria to be evaluated in this market. From that initial pool of vendors, we then narrow our final list. We choose these vendors based on: 1) product fit; 2) customer success; and 3) Forrester client demand. We eliminate vendors that have limited customer references and products that don t fit the scope of our evaluation. After examining past research, user need assessments, and vendor and expert interviews, we develop the initial evaluation criteria. To evaluate the vendors and their products against our set of criteria, we gather details of product qualifications through a combination of lab evaluations, questionnaires, demos, and/or discussions with client references. We send evaluations to the vendors for their review, and we adjust the evaluations to provide the most accurate view of vendor offerings and strategies. We set default weightings to reflect our analysis of the needs of large user companies and/or other scenarios as outlined in the Forrester Wave document and then score the vendors based on a clearly defined scale. These default weightings are intended only as a starting point, and we encourage readers to adapt the weightings to fit their individual needs through the Excel-based tool. The final scores generate the graphical depiction of the market based on current offering, strategy, and market presence. Forrester intends to update vendor evaluations regularly as product capabilities and vendor strategies evolve. For more information on the methodology that every Forrester Wave follows, go to Integrity Policy All of Forrester s research, including Waves, is conducted according to our Integrity Policy. For more information, go to Endnotes 1 This estimation is based on extensive interviews, briefings, and advisory work with vendors and end-users.

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