The data integration tools market is gaining new momentum as organizations recognize the role of these technologies in support of high-profile initiatives such as master data management (MDM), business intelligence (BI) and delivery of service-oriented architectures (SOAs). Recent focus on cost control has made data integration tools a surprising priority as organizations realize the "people" commitment for implementing and supporting custom-coded or semimanual data integration approaches is no longer reasonable. Vendor consolidation continues, driven by the convergence of single-purpose tools into data integration suites or platforms. While most vendors still approach this market with multiple products, metadata-driven architectures supporting a range of data delivery styles continue to emerge. Organizations seeking data integration tools must assess their current and future requirements and map them against product functionality, including support for a range of data integration patterns and latencies. Buyers must recognize that, as an evolving market, disruptions caused by merger and acquisition activity are likely as smaller vendors with valuable technology continue to be subsumed into larger entities to form more complete data integration tools portfolios.

The discipline of data integration comprises the practices, architectural techniques and tools for achieving consistent access to, and delivery of, data across the spectrum of data subject areas and data structure types in the enterprise, to meet the data consumption requirements of all applications and business processes. As such, data integration capabilities are at the heart of the information-centric infrastructure and will power the frictionless sharing of data across all organizational and system boundaries. Contemporary pressures are leading to an increased investment in data integration in all industries and geographic regions. Business drivers, such as the imperative for speed to market and agility to change business processes and models, are forcing organizations to manage their data assets differently. Simplification of processes and the IT infrastructure are necessary to achieve transparency, and transparency requires a consistent and complete view of the data, which represents the performance and operation of the business. Data integration is a critical component of an overall enterprise information management (EIM) strategy that can address these data-oriented issues.

From a technology point of view, data integration tools were traditionally delivered via a set of related markets, with vendors in each market offering a specific style of data integration tool. Traditionally, tools for extraction, transformation and loading (ETL) used predominantly in data warehouse/mart implementations held the largest market shares in this overall space and formed a "center of gravity" for market consolidation. BI efforts, with their focus on metadata, promoted the infusion of the combined data integration market with metadata management capabilities. However, in the past two years, vendors and leading organizations have been pursuing a strategy of centralizing their capabilities for semantic interpretation and reconciliation as a service within a platform and reducing, in relative terms, their emphasis on connectivity and specific data delivery styles. A variety of other related markets, such as those for data quality tools, adapters and data modeling tools, overlap with the data integration tools space. The result of this historical fragmentation in the markets is the equally fragmented and complex way in which data integration is accomplished in large enterprises — different teams using different tools with little consistency, lots of overlap and redundancy, and no common management and leverage of metadata. Technology buyers have been forced to acquire a portfolio of tools from multiple vendors to amass the capabilities necessary to address the full range of their data integration requirements. The market has not yet reached a point at which data integration is typically achieved via a single platform or suite, but notable improvements exist — especially relative to data services delivery and metadata management.

With the emergence of the data integration tools market, separate and distinct submarkets continue to converge, both at a vendor and technology level. This is being driven by buyer demands (for example, organizations realizing they need to think about data integration holistically and have a common set of data integration capabilities they can use across the enterprise, particularly when working on SOA initiatives that have many different consumers working in various contexts). It is also being driven by vendors' actions (for example, vendors in individual data integration submarkets organically expanding their capabilities into neighboring areas, and acquisition activity bringing vendors from multiple submarkets together). The result is a market for complete data integration tools that address a range of different data integration styles and are based on common design tooling, metadata and runtime architecture. This market has supplanted the former data integration tools submarkets, such as ETL, and becomes the competitive landscape in which Gartner evaluates vendors for placement within this Magic Quadrant. While the traditional ETL vendor submarket was the driver for consolidation, it is important to note that, even after acquiring federation or message-based integration capabilities, the market is not showing wide implementation of either of these approaches. Vendors that supply all three types of integration techniques generally exhibit an overwhelming strength in one — even those vendors that acquired market leaders in a second submarket.

Gartner estimates the size of the market for data integration tools at approximately $1.44 billion as of the end of 2007, and believes it is growing at a compound annual rate of more than 17% (see "Market Trends: Data Integration Tools, Worldwide, 2007-2012"). Services revenue from data integration tools implementations is also growing, with the time and effort required to implement the tools varying widely depending on the scope and complexity of the deployment (see "Toolkit Sample Template: Data Integration LOE Estimator").

During 2H07 and 1H08, the market showed the most pronounced dichotomy in execution that we have seen since 2006 when the Magic Quadrant for Data Integration Tools first replaced the Magic Quadrant for ETL. The top execution vendors — IBM, Informatica, SAP-Business Objects, Oracle and Microsoft — come from the more traditional data integration tools heritage and their execution capabilities arise from each vendor's traditional strength in supporting integration of tabular (structured) data. The vendors in the Leaders' quadrant have that traditional background, plus the incorporation of data integration for newly emergent (unstructured) data types, as well as the ability to support all major styles of data delivery. The remaining vendors demonstrate an interesting response to the market demands and represent the potential for disruptive practices in data integration. While ETI, Syncsort, Open Text and Pitney Bowes Software are successful in the traditional data integration tools market (providing only minimal support beyond ETL), the remaining vendors (all the Visionaries plus Sybase) are pursuing a different strategy. The Visionaries have chosen a different method of challenging the traditional Leaders. Some, including Sun Microsystems and Tibco Software, recognize the emerging importance of services architectures and, while the Leaders also offer this type of solution, the Visionaries recognize this area of performance as an opportunity. SAS, iWay Software, Pervasive Software and Sybase are each pursuing a focus that is unique and different from that of the Leaders in terms of market execution — channels, integration tightly linked with quality and analytics, and/or blending search with integration. The Leaders also possess features/functionality in these areas but the Visionaries differentiate on these characteristics (among others). The challenge for the Visionaries will be to expand on execution and that, in part, is dependent on their vision focus matching the emerging needs of the market related to services-style delivery, metadata and data quality.

The data integration tools market comprises vendors that offer software products to enable the construction and implementation of data access and delivery infrastructure for a variety of data integration scenarios, including:

• Data acquisition for BI and data warehousing: Extracting data from operational systems, transforming and merging that data, and delivering it to integrated data structures for analytic purposes. BI and data warehousing remain a mainstay of the demand for data integration tools.
• Creation of integrated master data stores: Enabling the consolidation and rationalization of the data, representing critical business entities such as customers, products and employees. MDM may or may not be subject-based, and data integration tools can be used to build the data consolidation and synchronization processes that are key to success.
• Data migrations/conversions: Traditionally addressed most often via the custom coding of conversion programs, data integration tools are increasingly addressing the data movement and transformation challenges inherent in the replacement of legacy applications and consolidation efforts during merger and acquisition activities.
• Synchronization of data between operational applications: Similar in concept to each of the previous scenarios, data integration tools provide the capability to ensure database-level consistency across applications, both on an internal and interenterprise basis, and in a bidirectional or unidirectional manner.
• Creation of federated views of data from multiple data stores: Data federation, often referred to as enterprise information integration (EII), is growing in popularity as an approach for providing real-time integrated views across multiple data stores without physical movement of data. Data integration tools are increasingly including this type of virtual federation capability.
• Delivery of data services in an SOA context: An architectural technique, rather than a data integration usage itself, data services are the emerging trend for the role and implementation of data integration capabilities within SOAs. Data integration tools will increasingly enable the delivery of many types of data services.
• Unification of structured and unstructured data: Not a specific use-case itself, and relevant to each of the above scenarios, there is an early but growing trend toward leveraging data integration tools for merging both structured and unstructured data sources, as organizations work on delivering a holistic information infrastructure that addresses all data types.

Gartner has defined several classes of functional capabilities that vendors of data integration tools must possess to deliver optimal value to organizations in support of a full range of data integration scenarios:

• Connectivity/adapter capabilities (data source and target support).
• Data delivery capabilities.
• Data transformation capabilities.
• Metadata and data modeling capabilities.
• Design and development environment capabilities.
• Data governance capabilities (data quality, profiling and mining).
• Runtime platform capabilities.
• Operations and administration capabilities.
• Architecture and integration.
• Service-enablement capabilities.

The ability to interact with a range of different data structures types, including:

• Relational databases.
• Legacy and nonrelational databases.
• Various file formats.
• XML.
• Packaged applications such as CRM and supply chain management.
• Industry-standard message formats such as electronic data interchange (EDI), SWIFT and Health Level Seven (HL7).
• Message queues, including those provided by application integration middleware products and standards-based products (such as Java Messaging Service [JMS]).
• Emergent data types, such as e-mail, Web sites, office productivity tools and content repositories.

In addition, data integration tools must support different modes of interaction with this range of data structure types, including:

• Bulk acquisition and delivery.
• Granular trickle-feed acquisition and delivery.
• Changed-data capture (ability to identify and extract modified data).
• Event-based acquisition (time-based or data-value-based).

The ability to provide data to consuming applications, processes and databases in a variety of modes, including:

• Physical bulk data movement between data repositories.
• Federated views formulated in memory.
• Message-oriented movement via encapsulation.
• Replication of data between homogeneous or heterogeneous database management systems (DBMSs) and schemas.

In addition, support for delivery of data across the range of latency requirements is important:

• Scheduled batch delivery.
• Streaming/real-time delivery.
• Event-driven delivery.

Built-in capabilities for achieving data transformation operations of varying complexity, including:

• Basic transformations, such as data type conversions, string manipulations and simple calculations.
• Intermediate complexity transformations, such as lookup and replace operations, aggregations, summarizations, deterministic matching and management of slowly changing dimensions.
• Complex transformations, such as sophisticated parsing operations on free-form text and rich media.

In addition, the tools must provide facilities for development of custom transformations and extension of packaged transformations.

As the increasingly important heart of data integration capabilities, metadata management and data modeling requirements include:

• Automated discovery and acquisition of metadata from data sources, applications and other tools.
• Data model creation and maintenance.
• Physical to logical model mapping and rationalization.
• Defining model-to-model relationships via graphical attribute-level mapping.
• Lineage and impact analysis reporting, via graphical and tabular format.
• An open metadata repository, with the ability to share metadata bidirectionally with other tools.
• Automated synchronization of metadata across multiple instances of the tools.
• Ability to extend the metadata repository with customer-defined metadata attributes and relationships.
• Documentation of project/program delivery definitions and design principles in support of requirements definition activities.
• Business analyst/end-user interface to view and work with metadata.

Facilities for enabling the specification and construction of data integration processes, including:

• Graphical representation of repository objects, data models and data flows.
• Workflow management for the development process, addressing requirements such as approvals and promotions.
• Granular role-based and developer-based security.
• Team-based development capabilities, such as version control and collaboration.
• Functionality to support reuse across developers and projects, and facilitate identification of redundancies.
• Support for testing and debugging.

Mechanisms for aiding the understanding and assurance of quality of data over time, including interoperability with:

• Data profiling tools.
• Data mining tools.
• Data quality tools.

Breadth of support for hardware and operating systems on which data integration processes may be deployed, specifically:

• Mainframe environments, such as IBM z/OS and z/Linux.
• Midrange environments, such as IBM System i (formerly AS/400) or HP Tandem.
• Unix-based environments.
• Wintel environments.
• Linux environments.

Facilities for enabling adequate ongoing support, management, monitoring and control of data integration processes implemented via the tools, such as:

• Error-handling functionality, both predefined and customizable.
• Monitoring and control of runtime processes.
• Collection of runtime statistics to determine use and efficiency, as well as an application-style interface for visualization and evaluation.
• Security controls, for both data "in flight" and administrator processes.
• Runtime architecture that ensures performance and scalability.

The degree of commonality, consistency and interoperability between the various components of the data integration toolset, including:

• Minimal number of products (ideally one) supporting all data delivery modes.
• Common metadata (single repository) and/or the ability to share metadata across all components and data delivery modes.
• Common design environment for supporting all data delivery modes.
• Ability to switch seamlessly and transparently between delivery modes with minimal rework.
• Interoperability with other integration tools and applications, via certified interfaces and robust application programming interfaces (APIs).
• Efficient support for all data delivery modes regardless of runtime architecture type (centralized server engine vs. distributed runtime).

As acceptance of data services concepts continues to grow, data integration tools must exhibit service-oriented characteristics and provide support for SOA deployments, such as:

• Ability to deploy all aspects of runtime functionality as data services.
• Management of publication and testing of data services.
• Interaction with service repositories and registries.
• Service enablement of the development and administration environments, such that external tools and applications can dynamically modify and control runtime behavior of the tools.

For vendors to be included in this Magic Quadrant, they had to meet the following requirements:

• Possess within their technology portfolio the subset of capabilities identified by Gartner as most critical from within the overall range of capabilities expected in data integration tools. Specifically, vendors must deliver the following functional requirements:
  • Range of connectivity/adapter support (sources and targets): native access to relational DBMS products, plus access to nonrelational legacy data structures, flat files, XML, and message queues.
  • Mode of connectivity/adapter support (against a range of sources and targets): bulk/batch and change data capture.
  • Data delivery modes support: bulk/batch (ETL-style) delivery, plus at least one additional mode (federated views, message-oriented delivery or data replication).
  • Data transformation support: at a minimum, packaged capabilities for basic transformations (such as data type conversions, string manipulations and calculations).
  • Metadata and data modeling support: automated metadata discovery, lineage and impact analysis reporting, and an open metadata repository including mechanisms for bidirectional sharing of metadata with other tools.
  • Design and development support: graphical design/development environment and team development capabilities (such as version control and collaboration).
  • Data governance support: ability to interoperate at a metadata level with data profiling and/or data quality tools.
  • Runtime platform support: Windows, Unix or Linux operating systems.
  • Service enablement (ability to deploy functionality as services conforming to SOA principles).

For this iteration of the Magic Quadrant, we added support for interaction with message queues, changed-data capture capabilities, and ability to deploy functionality as data services to reflect their importance in the ideal of a comprehensive information-centric infrastructure, as well as in response to increasing demand for this functionality in the market. Vendors had to:

• Generate at least $20 million of annual software revenue from data integration tools or maintain at least 300 production customers.
• Support data integration tools customers in at least two of the major geographic regions (North America, Latin America, Europe and Asia/Pacific).
• Have customer implementations that reflect the use of the tools at an enterprise (cross-departmental and multiproject) level.

We excluded vendors focusing only on one specific data subject area (for example, only customer data integration), a single industry, or their own data models and architectures.

Many other vendors of data integration tools exist beyond those included in this Magic Quadrant. However, most do not meet the above criteria and, therefore, we have not included them in this analysis. Market trends in the past three years indicate that organizations want to use data integration tools that provide flexible data access, delivery and operational management capabilities within a single vendor solution. Excluded vendors frequently provide products to address one very specific style of data delivery (for example, only data federation) but cannot support other styles. Others provide a range of functionality, but operate only in a single region or support only narrow, departmental implementations. Some vendors meet all the functional, deployment and geographic requirements but are very early in their maturity and have limited revenue and few production customers. The following vendors are sometimes considered by Gartner clients alongside those appearing in the Magic Quadrant when deployment needs are aligned with their specific capabilities and/or are newer market entrants with relevant capabilities:

Ab Initio, Lexington, Massachusetts, www.abinitio.com — Application development toolbox (Co>Operating System) and component library for metadata management and data integration.

Alebra Technologies, Minneapolis, Minnesota, www.alebra.com — Parallel Data Mover for cross-platform file and database copying and sharing.

Apatar, Chicopee, Massachusetts, www.apatar.com — Open-source data integration tools focused on ETL and data federation scenarios.

Attunity, Burlington, Massachusetts, www.attunity.com — A range of data-integration-oriented products, including adapters (Attunity Connect), change data capture (Attunity Stream) and data federation (Attunity Federate) for various platforms and database/file types.

CA, Islandia, New York, www.ca.com — Advantage Data Transformer provides ETL-oriented data integration. InfoRefiner provides replication and propagation capabilities for mainframe data repositories.

CDB Software, Houston, Texas, www.cdbsoftware.com — CDB/Delta provides change data capture and replication capabilities for IBM DB2 on the z/OS platform.

Composite Software, San Mateo, California, www.compositesw.com — Composite Information Server provides data federation/EII capabilities and supports delivery of data services.

Datawatch, Chelmsford, Massachusetts, www.datawatch.com — The Monarch Data Pump product provides ETL functionality with a bias toward extracting data from report text, PDF files, spreadsheets and other less-structured data sources.

Denodo Technologies, Palo Alto, California and Madrid, Spain, www.denodo.com — The Denodo Platform provides data federation and mashup capabilities for joining structured data sources with data from Web sites, documents and other less-structured repositories.

Embarcadero Technologies, San Francisco, California, www.embarcadero.com — The DT/Studio ETL tool provides support for a range of relational and other data sources, and integrates with the vendor's data modeling and database design tools.

ETL Solutions, Blaenau Ffestiniog, U.K., www.etlsolutions.com — Transformation Manager provides a metadata-driven toolset for the authoring, testing, debugging and deployment of various data integration requirements.

Exeros, Santa Clara, California, www.exeros.com — The Discovery product automates the process of discerning the business rules that enable mapping and transformation of data between dissimilar data structures.

expressor software, Burlington, Massachusetts, www.expressor-software.com — The expressor product is based on a semantic approach to designing and managing data integration processes.

GoldenGate Software, San Francisco, California, www.goldengate.com — Real-time, heterogeneous data replication capabilities provided by the Transactional Data Management (TDM) software platform.

Ikan Software, Mechelen, Belgium, www.etl4all.com — Java-based ETL technology named ETL4all, supporting transformation servers on Windows, Linux, Unix and IBM iSeries.

Innovative Routines International (CoSort), Melbourne, Florida, www.cosort.com — The Fast Extract and SortCL tools provide for rapid unloading and transformation of data in Oracle databases in support of ETL processes.

Jitterbit, Oakland, California, www.jitterbit.com — Freely downloadable software with a focus on both application integration (event- and message-based) and data integration.

Kalido, Burlington, Massachusetts, and London, U.K., www.kalido.com — The Kalido Active Information Management software enables dynamic data modeling and change management for data warehouses and master data environments.

Metatomix, Dedham, Massachusetts, www.metatomix.com — Follows a semantics-based approach to creation of data services and federated views of data across multiple data sources.

Pentaho, Orlando, Florida, www.pentaho.org — A provider of open-source BI solutions, Pentaho has added data integration tools to its portfolio by leveraging the Kettle open-source project and providing services and support.

Progress Software, Bedford, Massachusetts, www.progress.com — The DataXtend and DataDirect product lines provide tools for data access, replication and synchronization.

Quest Software, Aliso Viejo, California, www.quest.com — SharePlex provides real-time replication support for Oracle DBMS environments and is targeted primarily at high-availability applications.

Red Hat/MetaMatrix, Raleigh, North Carolina, www.redhat.com — The MetaMatrix Server, Enterprise and Query products support creation of data models and model-driven federated views of data.

Relational Solutions, Westlake, Ohio, www.relationalsolutions.com — The BlueSky Integration Studio provides ETL capabilities in a simplified, low-cost toolset that runs in the Windows environment.

SchemaLogic, Kirkland, Washington, www.schemalogic.com — Creation and maintenance of data models (Workshop), business models (SchemaServer), and the ability to propagate models and data across applications (Integration Service).

Seagull Software, Atlanta, Georgia, www.seagullsoftware.com — SmartDB for data migrations to the Oracle E-Business Suite.

SOALogix, Reston, Virginia, www.soalogix.com — The Confero SOA product offers a platform for the creation and delivery of data services for SOA.

Software AG, Darmstadt, Germany, www.softwareag.com — The Enterprise Information Integrator product provides data federation capabilities and is geared toward SOA deployments. The vendor's acquisition of webMethods in 2007 added process-oriented integration capabilities.

Software Labs, Roseville, California, www.softlabsco.com — The xFusion Studio product provides ETL functionality positioned toward a range of use-cases including BI and migrations.

Sypherlink, Dublin, Ohio, www.sypherlink.com — Metadata discovery and mapping via Harvester, and access to data sources for creation of integrated views via Exploratory Warehouse.

Talend, Los Altos, California, and Suresnes, France, www.talend.com — Open Studio is an open-source tool that primarily supports ETL-oriented implementations and is provided for on-premises deployment as well as in a software-as-a-service (SaaS) delivery model.

TigerLogic (formerly Raining Data), Irvine, California, www.tigerlogic.com — TigerLogic XDMS provides XML-based data federation and persistence, as well as delivery of data services.

Vamosa, Glasgow, U.K., and Boston, Massachusetts, www.vamosa.com — Provides content integration and migration, aimed at synchronization and consolidation of document repositories, via its Content X-Change and Content Migrator products.

Vision Solutions, Irvine, California, www.visionsolutions.com — Real-time database replication functionality is provided in the Vision Replicate1 product.

WhereScape, Portland, Oregon, www.wherescape.com — WhereScape Red enables rapid creation and maintenance of data warehouses, including ETL functionality.

XAware, Colorado Springs, Colorado, www.xaware.com — Provides support for the access, integration and service enablement of data sources via its XA-Suite product.

No vendors have been added since our last Magic Quadrant.

• Cognos — This vendor is no longer included in the Magic Quadrant because of its acquisition by IBM.
• Business Objects — This vendor is no longer included as a separate entity because of its acquisition by SAP.

To emphasize the need to address a range of data delivery styles, metadata management strength and other technical requirements of enterprisewide data integration activities, the Ability to Execute criteria in the data integration tools market include a strong emphasis on product capabilities. The Product/Service evaluation criterion includes all the major categories of functionality described in the "Market Definition/Description" section. In this iteration of the Magic Quadrant, in addition to the importance of the functional points that are part of the market inclusion criteria, we place an increased emphasis on metadata management capabilities and support for data governance via data quality capabilities. Also, as this is a dynamic and highly competitive market, Sales Execution/Pricing and Customer Experience (which includes the availability and quality of customer references, as well as overall customer satisfaction as measured by a survey of more than 300 customers across the included vendors) are key, and are therefore also heavily weighted. In this iteration of the Magic Quadrant, we place an even heavier emphasis on Customer Experience, in particular the quality of customer references. While also important criteria, Overall Viability (which includes an assessment of financial strength and growth), Market Responsiveness and Track Record, and Marketing Execution (which reflects the degree of vendor "mind share" in the market) are weighted lower than the other criteria to reflect that this is a market that is still somewhat early in its maturity.

The Completeness of Vision criteria most strongly emphasize an overall market understanding. These criteria include an assessment of the degree to which the vendor establishes market trends and direction, as well as the ability of the vendor to capitalize on market trends and survive disruptions. Both of these characteristics are crucial in the data integration tools market because of the volatility introduced by merger and acquisition activity, as well as the increasing impact on the market of the world's largest software vendors. In addition, we place a high weighting on Innovation to stress the importance of vendors developing new approaches to data integration that change the economics, scale, and impact of this technology. Specific to Offering (Product) Strategy, another highly weighted criterion, a key consideration is the degree of openness of the vendors' offerings. For success in this market, vendors must deliver independence from their own data models and architectures, and be capable of easily interoperating with the architectures and technologies of other vendors. The remaining criteria receive moderate weightings with a slight emphasis on Sales Strategy and Geographic Strategy, given the rapidly expanding size of the market.

Leaders in the data integration tools market are front-runners in the convergence of single-purpose tools into an offering that supports a range of data delivery styles. These vendors are strong in the more traditional data integration patterns such as ETL, they support newer patterns such as data federation, and provide capabilities that enable data services in the context of SOA. Leaders have significant mind share in the market, and resources skilled with their tools are readily available. These vendors establish market trends, to a large degree, by providing new functional capabilities in their products, and by identifying new types of business problems where data integration tools can bring significant value. Examples of deployments that span multiple projects and types of use cases are commonplace in their customer base.

Challengers in the data integration tools market are well positioned in light of the key trends in the market, such as the need to support multiple styles of data delivery. However, they may not provide comprehensive breadth of functionality, or they may be limited to specific technical environments or application domains. In addition, their vision may be hampered by the lack of a coordinated strategy across the various products in their data integration tools portfolio. Because this is a market that is at a relatively early stage in its maturity, Challengers can vary significantly with regard to their financial strength and global presence. The customer base of Challengers is generally substantial in size, though implementations are often of a single project nature, or reflect multiple projects of a single type (for example, all ETL-oriented use cases).

Visionaries in the data integration tools market will have a solid understanding of the key market trends and a position that is well aligned with current demand, but they may lack market awareness or credibility beyond their customer base or outside a single application domain, or they may not provide a comprehensive set of product capabilities. Visionaries may be new market entrants lacking the installed base and global presence of larger vendors, though they could also be well-established, large players in related markets that have only recently placed an emphasis on data integration tools.

Niche Players in the data integration tools market have gaps in both vision and ability to execute, often lacking key aspects of product functionality and/or exhibiting a narrow focus within their own architecture and installed base. These vendors have little mind share in the market and are not recognized as proven providers of data integration tools for enterprise-class deployments. Many Niche Players have very strong offerings for a specific range of data integration problems (for example, a particular set of technical environments or application domains) and deliver substantial value for their customers in that segment.

• ETI has a long association with the discipline of data integration, having been one of the earliest vendors offering technology in the original ETL tools market. With its mature and proven code-generating architecture that supports a range of platforms, ETI has historically been focused on physical, bulk data movement and delivery. More recently, the vendor has developed new differentiated offerings via alternative delivery and licensing models, including its built-to-order (BTO) and SaaS products, as well as continuing to enhance its support for service-oriented deployments.
• ETI continues to leverage its strength in supporting multiple hardware and operating system environments, including the mainframe and legacy data sources. It is often seen in sectors such as federal government and financial services where these characteristics, along with large data volumes and a high degree of complexity, are common. This reflects the vendor's roots in the market and is characteristic of most of its customer base, and these capabilities serve as a differentiator from much of the competition. As such, ETI's strategy and vision are focused largely on data integration scenarios involving the mainframe, as well as cultivating partnerships with independent software vendors (ISVs) and service providers to extend the range of platforms and data source types they can reach, and the manner in which they build data integration processes.
• During 2Q08, ETI was acquired by Versata, a conglomerate software vendor with a business model of purchasing smaller vendors and operating them under their existing names and brands. This will afford ETI more substantial financial resources which the vendor may be able to apply toward a product road map to expand its capabilities. Customer references continue to report positive experiences with service and support, pricing, and licensing terms. Customers that have been using ETI's technology for multiple years also indicate a positive experience regarding stability, scalability, and performance.

• Despite being acquired by a larger entity, ETI's modest size and limited global brand recognition remain a challenge. Its customer base continues to experience slow growth (it has approximately 450 customers), with some attrition of older customers. Most customer references indicate they will not expand their investment in ETI's technology in the next 12 months, representing a challenge for the vendor to capture upsell and cross-sell opportunities for its new offerings.
• ETI continues to struggle to keep pace with the market from a product functionality point of view. Customer references indicate areas such as modeling and metadata management, the design and development environment (as it relates to learning curve), and ability to deploy generated code as data services to be weaknesses. ETI's product plans — which include improvements in the design-time user interface with a focus on business analysts and architects — are positioned to address some of these gaps.
• The vendor must continue to innovate and deliver deeper support for delivery styles beyond ETL to increase the attractiveness of its technology to end users desiring traditional on-premises deployments. Customer references reflect only physical, bulk data movement, and these customers validate gaps and weaknesses relative to other data delivery styles (such as real-time data flow and creation of federated views of data).

• IBM continues to demonstrate the strongest vision in the market for extensive data integration capabilities. Its flagship Information Server product continues to progress toward a common data integration platform atop common metadata, common design tooling, and a common look and feel. IBM exhibits flexibility in licensing, from project-level-based and point-solution-based approaches, through enterprise-class organization standard solutions (for example, purchasing the entire Information Server product set).
• IBM reference customers report a high level of satisfaction with the tools that generally comes from a long experience with the products (one to three years, or longer than three years). IBM tools are highly adaptable to a wide range of data integration business needs. Further, customers report excellent product support and implementation expertise is available. Customers use IBM in a wide variety of initiatives: business-to-business (B2B) data integration, application data migration, BI/data warehousing, MDM and for deploying data management into SOA architectures. As a result of IBM's acquisition of Cognos, a traditional market leader in BI strategy is now combined with a market-leading data integration capability.
• Customers report use for batch/bulk processing and a significant amount of use for message-based transformations (enterprise application integration [EAI]). While a smaller number of references exists for low-latency/replication-style data integration, it is also strong. A small subset of IBM references is also using its tools for data federation. IBM demonstrates above-market metadata management capabilities and exhibits a strong tendency toward service enablement in its toolset.

• The IBM tools have a long-standing reputation as having a difficult learning curve. Many customers implement quickly (in six months or less), however, reference comments seem to suggest that this is for the straightforward functionality of light or prebuilt transformations (of which there are many). A significant percentage of customers report longer implementation cycles. These references report that they are using more advanced functionality within the tools. Such data integration efforts would, by their nature, be longer, but the impression given by references is that there is a two-stage learning curve with IBM tools. The first part of the learning curve is rapidly achieved, but the second is more complex.
• IBM continues to acquire disparate technologies and is challenged by product integration efforts. References continue to report difficulties with implementing combinations of the diverse products, including infrequent reports of instability. However, this appears patchy — many of these customers plan to purchase additional licenses, and most customers view their experiences with the products and their overall relationship with IBM as acceptable or better.
• Most customer references are using the toolset for bulk/batch data delivery or replication, indicating that federation and message-based use remains far less prevalent. This means that the reliable experience base for federation and message-based styles of implementation is smaller than for the more traditional ETL and replication styles.

• As one of the most widely recognized providers in this market, Informatica continues to expand its presence, with an installed base of more than 3,000 customers and substantial ongoing revenue growth. Despite market consolidation and encroachment by much larger application and infrastructure vendors, Informatica has increased its traction in the market through new customer wins and expansion within existing accounts. Its annual revenue is approaching $400 million and Informatica has the most substantial size and resources of the specialist data integration tools vendors.
• Informatica is respected for its consistent track record of delivering solid technology, regular releases, and a positive service and support experience. Most customer references have established Informatica as their enterprise standard for data integration tools and/or have deployed its tools in multiple projects and departments. Nearly all references reflect use in the data warehousing domain, but a significant percentage also apply the tools in other scenarios, including MDM, system migrations, and synchronization of data between operational applications. Informatica's customers continue to recognize a strong "ecosystem" of technology and service partners. While all complex data integration efforts are more demanding and take longer to deliver, Informatica's tools are noted for a straightforward learning curve with more gradual skills requirements as additional features/functions are demanded from the tool in implementations of increasing complexity.
• The release of PowerCenter v8.6 in 2Q08 increased Informatica's ability to address real-time data integration scenarios, while also making data quality functionality an inherent part of the PowerCenter platform. In addition, Informatica has delivered offerings specifically focused on B2B data exchange and data migration projects, further supporting the trend of data integration tools being deployed in a wide range of use cases. The product road map emphasizes Informatica's strong vision in this market, focusing on functionality for the various roles (both business and IT) associated with strategic data integration and data governance activities.

• While more advanced customer references reflect increasing deployments of real-time, granular data flow, most Informatica customers continue to use its technology for bulk, batch-oriented data delivery. The vendor must not only increase its presence with existing customers by supporting more projects of the traditional type, but also by enticing those customers to support a range of data delivery styles through expanded use of PowerExchange products (for operations such as changed-data capture) and adoption of the PowerCenter real-time and data federation options.
• As consolidation occurs across various markets related to data management, Informatica's position as an independent provider of data integration tools creates opportunities, but it also brings challenges. The vendor is increasingly competing with large application, infrastructure, and BI platform providers (SAP, Oracle, Microsoft and IBM). While Informatica's support for interoperability with those vendors' ecosystems remains, consolidation degrades the ability and desire to deliver tight integration with formerly "friendly" data quality and data integration technologies now owned by those providers. Consolidation has directly resulted in some of Informatica's major partnerships (both of an OEM and joint marketing nature) being substantially diminished in value.
• While Informatica customers are generally pleased with both product capabilities and their relationship with the vendor, references indicate challenges with limited experience in the field (skills availability and professional services) with non-ETL data delivery styles, such as data replication and data federation.

• A division of Information Builders, iWay Software creates and sells Information Builders' integration technologies, with the goal of building an integration software business independent of the BI capabilities for which Information Builders is well known. iWay offers capabilities for physical data movement and delivery (via its DataMigrator ETL tool), data federation (via the iWay Data Hub product) and real-time message-oriented integration (supported by the Service Manager product). All the data integration tools are supported by a wide range of adapters, providing access to relational, legacy/mainframe, and application sources.
• The various iWay data integration tools are well integrated, use common infrastructure for management and administration, and can be deployed on a wide range of platforms, including the mainframe. As a result, many iWay customer references indicate the tools are their enterprise standard, and their implementations reflect a solid percentage of enterprisewide deployments. The vendor's vision for a comprehensive platform supporting a full range of data delivery styles, as well as a deep understanding of service-oriented deployments, positions iWay well to capitalize on emerging trends in market demand.
• Information Builders' size and global presence afford iWay a strong foundation from which to execute its growth strategy. Customer references cite the broad connectivity capabilities, reasonable ease of implementation effort, and integration with Information Builders' BI products (specifically WebFocus) as main drivers for their selections of iWay data integration tools.

• While iWay products provide breadth of functional capabilities across a range of data delivery styles, gaps in specific capabilities remain. For example, customer references cite limited data quality support as a weakness. In addition, although Information Builders pioneered data federation technology well over a decade ago, most iWay customer references are not using these capabilities and, in some cases, are not aware they exist.
• While customer references reflect use of the tools in a range of different scenarios — including BI, MDM, migrations, and data services within SOA — experiences with product support are inconsistent (ranging from very positive to poor). A repeated theme among customer references indicates challenges with complexity and cost of upgrades, sometimes associated with stability of enhanced capabilities in new releases.
• iWay's product capabilities are well aligned with the evolving needs of the data integration tools market, but one of the vendor's biggest challenges is gaining recognition outside the Information Builders' customer base. iWay continues to suffer a low profile, rarely appearing in competitive situations versus the market leaders. This poses the risk of declining market share. The vendor is attempting to increase its market presence and mind share through iWay-specific sales overlays in the Information Builders' sales force and increased investment in various marketing channels on the Web and in print media.

• Microsoft's main offering in the data integration tools market is SQL Server Integration Services (SSIS). With SSIS, Microsoft provides support for bulk data movement (via ETL) and the ability to interface easily with BizTalk Server, which provides real-time, message-based capabilities. Microsoft also offers data replication and synchronization capabilities, and limited data federation support within the SQL Server DBMS. SSIS includes an extensible tool using scripts, Structured Query Language (SQL) and any .NET language. Microsoft has historically supported standards-based connectivity, plus adapters for BizTalk, message queues, XML and flat files. The newly released SQL Server 2008 expands SSIS connectivity to heterogeneous data sources, via connectors to SAP Business Information Warehouse (SAP BW), Oracle and Teradata. A BizTalk Adapter is also available for SAP ERP (formerly SAP R/3). Through partners, Microsoft provides access to additional data sources, including IBM DB2.
• SSIS includes standardized data integration best practices for data warehouses, including built-in features to manage dynamic and slowly changing dimensions. This supports data-mart-style tables in relational DBMSs or within SQL Server Analysis Services (SSAS) data marts, making it easier to move atomic warehouse data to version-controlled data marts. Customer references cite the overall low total cost of ownership, ease and speed of implementation, ease of use, strong performance, and the tight integration with the rest of the Microsoft SQL Server capabilities as the main reasons for choosing Microsoft over alternatives.
• Microsoft's size and global presence provide a huge customer base for best practices, excellent support and a distribution model that supports both direct and channel partner sales. Microsoft continues to expand the reach of SSIS, making it usable in more diverse and heterogeneous environments via more connectivity-related partnerships and internal development of the technology. The company's recent acquisition of Zoomix, a data quality technology provider (see "Microsoft Zooms In on Data Quality Market"), gives Microsoft the opportunity to offer SSIS users expanded matching and cleansing capabilities to complement the enhanced data profiling capabilities delivered with SSIS in SQL Server 2008.

• Microsoft has a range of components that address various styles of data integration, yet the vendor does not clearly articulate a comprehensive data integration vision and strategy to the market. All customer references demonstrated use of SSIS, but a small percentage is linking SSIS with other Microsoft products related to data integration, such as BizTalk or the replication and federation capabilities available in SQL Server. Microsoft needs to improve the breadth of its vision for data integration tools to emphasize multiple data delivery styles (not just physical bulk data movement via SSIS), positioning its capabilities in a form that enables customers to see data integration as a cohesive infrastructure rather than disparate point products.
• The SQL Server 2008 version of SSIS offers substantially expanded connectivity. However, changed-data capture capabilities for non-SQL Server sources and support for packaged applications beyond SAP represent gaps in the native SSIS functionality (although they can be addressed with partner products). Microsoft must continue to increase its ability to deal with the heterogeneous platforms and data source types that are common in large enterprises.
• Customer references continue to cite metadata management capabilities (metadata discovery, modeling, model-to-model mapping, and impact analysis and lineage reporting) as a weakness. In addition, the inability to deploy data integration workload on non-Windows environments is a limitation for customers wishing to leverage the processing power of diverse hardware and operating system platforms.

• Open Text focuses on the integration of data and content via its Genio product (which was developed by Hummingbird, a vendor that Open Text acquired in 2006). It can interact with most relational DBMSs (via native SQL, ActiveX Data Object [ADO]/Object Linking and Embedding for Databases [OLE DB] or Open Database Connectivity [ODBC]), nonrelational legacy systems (such as Customer Information Control System [CICS], Adabas and OS/390), Web services, ERP systems, message-oriented middleware (MOM) and structured files. Because of the capabilities of Open Text's full range of product offerings, Genio is strong in dealing with less-structured sources (such as content management systems and document repositories), something that many of Open Text's competitors are only just beginning to address.
• Both a strength and a caution, Genio is a simple-to-use tool that is generally used with ease in departmental deployments. Many organizations also find the licensing easy to consume for business unit solutions — especially when an enterprise standard is not in place and cost is a major concern. The attractive licensing model and the ability to interoperate with a wide range of source and target types are commonly cited by customer references as significant reasons for selecting Genio.
• The company's prebuilt functionality (for example, aggregation and statistical functionality, string functions, and numeric and date functions) is beginning to rival that of better-known market leaders, but relies on a scripting interface for transformation functionality, including text mining.

• Genio provides native connectivity to SAP but relies on Web services for other ERP solutions. With no federation, replication or direct data quality support, the product becomes utilitarian for its current capabilities, and the gap between this tool and those of the market leaders will be difficult to close.
• Customers do not tend to upgrade their installed versions of Genio. This appears to be by choice, as those with older versions have had straightforward migration experience in the past. This is probably due to the simplicity of the tool, which is rarely used for message-based or federation-style integration.
• Genio "Polling" and "Event Mechanism" support event capture via specified scheduled or data evaluation rules. However, other real-time support relies on leveraged MOM capabilities, database utilities or other third-party change data capture solutions — potentially weakening real-time and EAI support based on organizations' needs.

• Oracle's data integration capabilities are largely centered on two specific tools — Oracle Warehouse Builder (OWB), the base functionality of which is packaged with Oracle DBMS licenses, and Oracle Data Integrator (ODI), which is a stand-alone product within the Oracle Fusion Middleware product family. During 1Q08, Oracle released the Data Integration Suite, which is based on ODI and includes a range of other components (see "Oracle Begins Formulating a Cohesive Data Integration Strategy"). Oracle's acquisition of BEA Systems, which it completed in April 2008, affords Oracle the opportunity to expand its data federation capabilities (see "Oracle's Post-BEA Middleware Road Map: Product Recommendations for Users"). Oracle also provides replication capabilities and limited support for federated queries in the Oracle DBMS, thus providing additional data integration capability.
• Adoption of both OWB and ODI continues to grow, most commonly in traditional ETL-style implementations in support of BI and data warehousing. Customer references cite complete functionality for ETL, tight integration with the Oracle DBMS (in the case of OWB), integration with other Oracle Fusion Middleware components and applications (in the case of ODI), and Oracle's overall market presence and viability as the main reasons for selecting these tools.
• Oracle continues to make the most of OWB and ODI within its various offerings, providing embedded ETL functionality with Oracle applications, MDM, and BI offerings. This will increase the rate of adoption by the large customer base for various Oracle technologies. Oracle has taken some early steps to integrate OWB and ODI (for example, enabling the use of ODI "knowledge modules" for non-Oracle target types within OWB), and states it is working toward delivering a single data integration toolset in the long term. Initial steps include a single design environment and runtime interoperability across the tools. However, Oracle has not yet given time frames or road map details.

• Oracle has opportunistically acquired and partnered to deliver a range of desirable functionality that addresses immediate market demand, with an emphasis on traditional ETL, rather than establishing a comprehensive and innovative vision for the future of data integration tools and their role in the broader context of information management. However, given Oracle's large installed base for DBMS, applications, and BI, this pragmatic approach to the market will yield substantial growth in share and presence in the long term.
• Nearly 100% of customer references for both products are using the tools for batch-oriented, bulk transformation and movement of data, with a small percentage deploying the tools for other styles of data delivery (for example, real-time or federation) or in support of use cases beyond BI. Enterprisewide deployment of the tools is rare, with single-project implementations being the norm. Oracle will need to demonstrate the use of its tools by customers in less-traditional implementations across a range of application types. In particular, the combination of ODI's richer heterogeneous target support and the packaging of ODI with SOA-related infrastructure in the Data Integration Suite presents an opportunity to target data consistency and data services implementations.
• Despite the stated direction toward unification of OWB and ODI to deliver a single integrated tool, Oracle's portfolio remains fragmented across various products. This leads to complexity of implementation and confusion for customers (as noted by various customer references) about how the desired functionality is related and integrated. Oracle will need to do more than rationalize runtime architectures and design environments — reducing the number of metadata repositories underlying the various Oracle data management offerings will be key to long-term success and customer value.

• Pervasive offers a solid, low-priced and moderately advanced data integration tool. The company is unassuming in its interactions with customers, and is a capable player in the data integration tools market. With many years of experience in this market, and more than 3,500 customers, Pervasive Data Integrator is frequently used for ETL-style data delivery, and also supports EAI and real-time messaging-style solutions.
• Pervasive makes good use of its metadata capability for standards use and SOA. The design time interface produces an XML metadata repository, and supports structured, semistructured and unstructured data extraction (including a document schema designer for pulling recurring, untagged data from documents). It supports X12, Health Insurance Portability and Accountability Act (HIPAA) and HL7, and is delivered using services- or message-based data transfer for both intraenterprise and interenterprise integration. From an SOA perspective, Pervasive supports a Web service "invoke" utility and the ability to expose any of its data integration capabilities as a service.
• Pervasive customers are generally long-term users of the company's data integration products, and report they are on current versions and that the version upgrade processes were straightforward. This is significant, compared with the findings for other vendors. In another customer experience category, Pervasive has developed a SaaS offering for data integration services that has a good reference base and is starting to gain market traction.

• Pervasive's low pricing creates a phenomenon in which many customers license and use the tools in a variety of tactical situations, as opposed to making it an enterprisewide standard. This leads to highly divergent implementation practices and multiple metadata stores, and actually creates confusion in the organization when resolution is necessary. Recent references indicate a broader use of the solution throughout the enterprise and, frequently, embedded Pervasive solutions are transparent to end users — but they are not transparent to the development team. The general assumption for maturity models is that efficiency and consistency always go hand-in-hand or in tandem, but this is not necessarily the case. The ability to control efficiency in the face of inconsistency is also a maturity model.
• Pervasive has no federation capability (although the "building blocks" of, for example, data access and joins are present) and data cleansing is provided via partners (the vendor does offer data profiling capabilities). The lack of federation and fully integrated metadata for data quality partner solutions will restrict the ability to achieve reuse of data quality solutions, regardless of the data integration method (federation, messaging and database loads).
• Pervasive offers a straightforward tool that has some unexpected, advanced capabilities. However, its approach to the market using indirect channels (SaaS, OEM, ISV resellers) limits its overall presence. The existence of trained users is not as common as with other vendors. In contrast, Pervasive's product support is often described not simply as good, but exceptional. The overall market for data integration tools follows a consistency and standards approach, but Pervasive follows a "choice" delivery model — it can be used as a standard or tactical, embedded, via channel or direct. If an organization can use this model, it becomes a good fit.

• Pitney Bowes Software, a division of mailstream hardware and services vendor Pitney Bowes, competes in the data integration tools market via its Data Flow offering. Data Flow, which was developed by Sagent Technology, became part of the Pitney Bowes portfolio as a result of the company's acquisition of Group 1 Software in 2004. Following recent reorganizations, the strategy and development of Data Flow (along with that of the former Group 1 data quality products) is now driven from the Pitney Bowes Software unit, creating a consistent focus on data integration and data quality.
• Data Flow primarily supports ETL implementation patterns, although limited data federation scenarios can also be achieved. Implementations generally reflect traditional ETL use cases (bulk/batch-oriented data movement) in the BI domain, although some customers also use Data Flow in non-BI applications, such as MDM and system migration activities. Pitney Bowes delivered the latest version of Data Flow (v.6.5) in 1Q08, adding a range of technical feature improvements, such as Unicode support, additional transformation types, support for the Itanium platform, and an interface to the vendor's data profiling tool.
• Pitney Bowes supports a customer base of more than 400 direct Data Flow customers, although the total size of the Data Flow installed base is larger as a result of OEM agreements in which Data Flow is embedded in other vendors' products. Customer references continue to indicate strong ease of use, fast implementation times, and solid ETL functionality as the reasons for their selection, or continuing use, of Data Flow. However, most customer references reflect departmental implementations, with few having standardized on Data Flow for enterprisewide or large-scale use.

• Pitney Bowes Software is rarely seen actively competing against the market leaders for new data integration tools opportunities at the enterprise level. The vendor's repeated rebranding and reorganization exercises have reduced its ability to execute by continuing to raise questions about its strategy and its degree of focus on this market. However, with the formation of a focused software unit and alignment under a positioning of "business insight," Pitney Bowes has the opportunity to deliver a consistent and cohesive message to the market.
• While the latest release of Data Flow includes value-added enhancements, in general the Pitney Bowes product road map indicates the vendor has a somewhat narrow vision and is more of a follower in the market. Recent and future development activities will help to address weaknesses and fill gaps relative to market leaders, but the company does not fully reflect innovation in critical competencies such as metadata management and delivery of data services. However, the road map is consistent with the vendor's focus on BI-oriented deployments and departmental implementations.
• With such a strong focus on ETL for BI, the vendor will be increasingly challenged to expand its capabilities as market demand and more competitors turn their attention toward additional modes of data delivery, as well as to non-BI use cases. Customer references cite functional weaknesses in real-time and granular data delivery, service orientation, and breadth of platform support (because of the product's historic orientation toward Windows). In addition, customer references reflect an inconsistent support and service experience, which is likely to have resulted from the various changes in ownership and organizational structure associated with Data Flow in the past several years.

• SAP-Business Objects now blends its huge SAP applications installed base with the BusinessObjects Data Integrator customer base. Both companies had brand recognition and global presence before SAP acquired Business Objects, which gives the combined company many opportunities to push data integration capabilities into the market. The combination of Business Objects and SAP gives customers the choice of reasonable SAP-specific capabilities, as well as capabilities for diverse, heterogeneous environments. SAP seems to be delivering both sets of products with equally effective marketing — there has been little or no revenue drop-off following the acquisition, which is in stark contrast to the situation with other similar large-scale acquisitions in this market.
• SAP-Business Objects provides data modeling and metadata management that can be used across multiple integration scenarios. The portfolio also includes BusinessObjects Data Federator and the new Data Services platform, which combines Data Integrator and Business Objects' data quality functionality. Data Services specifically indicates the infusion of Business Objects' expertise and allows the combined company to follow a road map that market-leading end-user organizations are pursuing in their data integration efforts.
• SAP-Business Objects offers integrated or interoperable data profiling and data quality (as a result of Business Objects' acquisition of Firstlogic in 1Q06), and data mining and text mining capability (via Business Object's acquisition of Inxight in 3Q07). The development environment supports the introduction of third-party tools or advanced data manipulation/management capabilities from within the toolset, and permits the development of reusable directory objects.

• At this point in the data integration tools market, SAP and Business Objects are perceived by buyers as two companies, although the vendor has initiated joint development and begun to align its data integration strategy across the business units. Mergers and acquisitions are not new in the IT vendor space, but SAP has previously focused on internally developed products. This is somewhat new territory for the company. However, early indications are that SAP will "not fix it, because it isn't broken."
• For now, references report the most common method of delivering data integration is via a shared services approach. This permits the central team to use its discretion in determining the best approach to data integration. While this supports the use of separate tools, it also causes separate strategies to proliferate in the organization, and this is an emerging symptom of the two-brand approach. If SAP determines in the future to focus on one platform or the other (SAP NetWeaver or the Business Objects capabilities), it could cause some difficulty for organizations that implemented with the approach chosen for "sunset."
• The Business Objects data integration tools do not have runtime capability on the mainframe, although the vendor does offer connectivity for accessing mainframe-based data. For large organizations with many legacy systems and data warehouse sources still operating on mainframe systems, Data Integrator may not be viewed as a complete legacy-capable, enterprise solution.

• SAS's primary product in the data integration tools market is the Enterprise Data Integration Server, providing capabilities for ETL and data federation, including packaged transformations, metadata management, parallel processing, load balancing and, through the vendor's DataFlux subsidiary, rich data quality functionality (profiling, cleansing, matching and enrichment). SAS's size, global presence and longtime experience supporting data integration activities position it solidly from an execution perspective. As a result, customer references generally indicate a positive support and service experience, regardless of geographic region.
• The SAS Enterprise Data Integration Server has connectivity to virtually every data source, including packaged applications, data warehouse appliances, and many data sources on the mainframe. SAS has recently added connectivity for salesforce.com and data warehouse appliances, such as HP Neoview and Netezza. It can also run on every major operating system, including various Unix and Linux flavors and z/OS. Customer references cite the ability to work in diverse environments as a key driver for their selection of SAS's data integration tools.
• SAS has shown strong vision for this market through its recognition of the importance of an integrated platform, including data quality capabilities. Its recent investments in support of emerging trends (such as leveraging emergent data types via its acquisition of Teragram) and strategic partnerships (including in-database execution of data integration workloads, such as it is initially delivering with Teradata) show its ongoing diversification of data integration architectures and applications. In addition, SAS is beginning to target the small or midsize business market, which remains underserved by vendors of enterprise-class data integration tools.

• SAS is not regularly recognized by potential buyers as a provider of stand-alone data integration technology that is suitable for non-SAS environments. The vendor continues to struggle in gaining significant mind share with corporate-level IT organizations, which are the predominant buying centers for data integration tools. As IT organizations have not been the traditional targets of SAS's sales activities, refocusing and re-education of the sales force will be necessary to generate the sales execution needed for SAS to become a leader in this market.
• SAS must also become successful in selling its data integration tools for use in non-BI scenarios, given that the vendor is not commonly recognized outside the BI domain. Most deployments of SAS's data integration tools are seen as part of a SAS BI or data warehousing initiative. A small percentage of customer references is applying the tools for ensuring data consistency across operational applications and for delivery of data services in SOA efforts — two areas that are rapidly becoming significant drivers of data integration investment.
• Customer references generally report satisfaction with the SAS data integration tool functionality, but SAS customers often cite high licensing costs as a challenge. On the whole, customer references rated pricing methods and pricing as less than acceptable. This is likely to be a result of current budgetary pressures caused by economic conditions, as well as the tendency of the SAS sales force to be rigid in pricing negotiations. Both detract from SAS's ability to execute.

• Sun Microsystems' data integration tools are bundled into its Java Composite Application Platform Suite (CAPS) offering. Java CAPS is Sun's vision for all the components needed for its customers to deploy SOAs. Therefore, Java CAPS includes many technologies and products that are outside the boundaries of the data integration tools market. The data-integration-related components of Java CAPS — Data Integrator (formerly eTL Integrator), Master Index (formerly eView Studio) and Sun ESB (formerly eGate Integrator) — were initially added to the Sun portfolio following the company's acquisition of SeeBeyond Technology in 2005. Ongoing development of these capabilities is taking place in the Mural open-source community that Sun established. Sun reports an installed base of more than 2,000 customers across the various Java CAPS components.
• Data Integrator addresses batch, bulk-oriented data delivery; Master Index enables the creation of "single view" services that can draw together data from multiple sources; and Sun ESB provides foundation capabilities, such as messaging, data movement and transformation. Implementations by customer references are overwhelmingly centered on the use of Sun ESB and other components of Java CAPS to deliver data consistency integration patterns, support B2B data sharing, and deliver data services in the context of SOA initiatives.
• Sun is one of a small set of vendors in this market that demonstrates strong vision and understanding of the role of data integration capabilities in SOA. By setting trends in how data integration functionality (specifically, the development and deployment of data services) can be exposed to improve data management consistency in modern architectures, Sun is aligned with the vision for an information-centric infrastructure and support of EIM principles.

• Notably, users of Sun's data integration tools tend to be very limited in their application toward BI and data warehousing use cases, where market demand is heavy and market leaders have substantial strength. Customer references cite relative weaknesses in bulk data movement, metadata management, data federation, and support for data quality operations, which are all key capabilities required for more traditional data integration deployments and non-SOA implementations. In addition, customer references reflect a lack of industry diversity — virtually all references provided by Sun were healthcare provider organizations.
• Sun's strong focus on SOA deployments of its data integration capabilities demonstrates vision and innovation, but is well ahead of mainstream market demand for data integration tools. Sun misses many opportunities to capture data integration deals through limited acknowledgement of non-SOA demand. Sun appeared on shortlists in less than 5% of customer references across all vendors (a huge disparity with market leaders), indicating that Sun has limited mind share in this market.
• Customer references rate Sun's support and professional services as slightly below adequate, with slow support response times and a perceived lack of receptiveness to customer enhancement requests being the main issues. Pricing is perceived as adequate, and most customer references view as positive their overall experience of doing business with Sun in this market. However, only a small percentage of references indicates that it will expand its investment in Java CAPS components for data integration uses in the next 12 months.

• Sybase has assembled a range of capabilities to address a variety of data delivery styles, including ETL, data federation, and replication. Most widely used is the Sybase Replication Server product, which supports heterogeneous database replication and is often seen in high-volume, mission-critical scenarios involving operational data managed primarily in Sybase Adaptive Server Enterprise (ASE) databases, but also in other DBMS types. The vendor's size, global presence, and large installed base in key sectors, such as financial services, government, and telecommunications, create opportunities for it to execute in this market.
• Sybase has modified its sales and marketing strategy for its data integration tools by de-emphasizing the Data Integration Suite bundling in favor of positioning individual components for specific use cases. In particular, it is focusing strongly on supporting sales of Sybase IQ by bundling in the ETL product and offering Replication Server for real-time loading of Sybase IQ databases. This is a pragmatic near-term strategy, which is better aligned with current market demand, and will allow Sybase to improve its execution given the vendor's extremely limited mind share as a provider of data integration tools. However, to date, Sybase customer references reflect little use of the tools beyond Replication Server — the ETL and federation products have seen limited traction.
• Sybase uses PowerDesigner, its flagship modeling and design product, as the common design interface and focus of metadata across the various components of its Data Integration Suite. Currently, PowerDesigner models can be used to drive replication and federation deployments. Sybase has broadened its partnerships with data quality tools vendors, adding a reseller agreement for the QualityStage component of IBM's Information Server product family. In addition, the vendor continues to expand the heterogeneous support within Replication Server, with specific plans to add Microsoft SQL Server 2008, Oracle Database 11g, and DB2 v.10 during 1H09.

• Sybase lags behind the market leaders and other large vendors with regard to recognition as a competitor in this space. To gain mind share, Sybase must overcome the market perception that it cannot capably support heterogeneous environments (as opposed to Sybase DBMS-oriented ones) and that it cannot support data delivery styles beyond real-time replication.
• Customer references (nearly all of which are running only Sybase Replication Server) indicate gaps in various aspects of metadata management and modeling, as well as in supporting complex implementations. The limited awareness and use of Sybase's metadata capabilities is likely to be because of lack of exposure to, and lack of interest in, PowerDesigner functionality, as these customers are not using other Sybase data integration products. In general, Sybase shows substantial weakness in providing customer references that reflect the full breadth of its data integration tools product line, with no references provided for ETL and federation.
• Sybase's shift in sales and marketing strategy is contrary to longer-term trends in the data integration tools market. Organizations are increasingly seeking comprehensive suites of data delivery styles deployed in an enterprisewide fashion, and structured in the form of data services. Organizations that seek specific point solutions or more tactical and project-specific roles for data integration could benefit by following the Sybase strategy, but they should realize the vendor may be slow to adapt to wider needs in the future.

• Syncsort entered the data integration tools market in 2005 with its DMExpress ETL tool, based on its core Syncsort engine. Syncsort also has an early-stage joint marketing relationship with Composite Software to enable data federation scenarios alongside DMExpress. However, because of its functionality and the manner in which Syncsort has historically positioned and marketed the product ("faster ETL"), DMExpress is solely targeted at physical, batch-oriented data delivery.
• With 40 years of experience in high-performance data processing, sustained profitability, and a large and loyal customer base, Syncsort has a solid base on which to grow its market presence. With the recent majority investment by Insight Venture Partners, Syncsort has new leadership and a strong financial and experience base with which to expand investments in DMExpress. Early changes to organizational structure and sales and marketing processes have begun to show positive results, with the DMExpress customer base growing to approximately 400 customers by 1H08.
• DMExpress users often have investments in tools from the market leaders or other competitive vendors, and they use Syncsort's technology to fine-tune the end-to-end processes supported by such vendors. Most Syncsort customer references indicate that another provider's technology is their standard for data integration tools, and DMExpress plays a role in augmenting or extending their primary investment. Customer references commonly cite ease of implementation and use, strong performance, targeted functionality (batch-oriented ETL) and pricing (lower cost than the market leaders) as strengths and their main reasons for selecting DMExpress.

• Syncsort continues to struggle with gaining mind share in the data integration tools market, and only rarely appears in competitive situations against more established vendors. To date, Syncsort is infrequently cited by customer references as their enterprise standard for data integration tools, with most customers indicating DMExpress is used in a single department or project, or a small number of unrelated projects.
• Syncsort has a number of gaps to fill to improve its execution in this market. Specific areas include: lack of support for delivery styles other than ETL (in particular, real-time capabilities); connectivity (lack of packaged applications support and limited changed-data capture); various aspects of metadata management (for example, introspection of metadata from packaged applications and extensibility of the metadata repository); service enablement; and data profiling and quality functionality. Syncsort expects future releases of DMExpress to address some of the connectivity gaps by adding support for SAP and a limited set of mainframe sources, as well as expanding runtime metadata capabilities. In addition, an emerging partnership with Trillium Software is positioned to address data quality and profiling capabilities. Syncsort customer references reflect dissatisfaction with a lack of professional services capability, and the vendor is beginning to respond by piloting new services offerings.
• In addition to delivering functional improvements, to compete well in the longer-term, Syncsort must expand its vision of data integration to acknowledge more than just physical bulk movement of data. The high-performance ETL positioning suits a segment of the market, but, increasingly, buyers seek a broader range of delivery styles and richer modeling and metadata capabilities as they work toward delivering a strategic information infrastructure.

• Tibco Software offers several approaches to data integration, with its traditional focus being on message-oriented application integration via its flagship BusinessWorks product. Tibco directly targets the data integration tools market with DataExchange, an ETL-oriented product that extends Tibco's capabilities into the arena of bulk, batch-oriented data movement and delivery. The vendor positions DataExchange as a complementary component to BusinessWorks, and part of its integration and SOA stack. Tibco's Collaborative Information Manager product for MDM provides support for federated views, but is limited to creating a single view of master data objects.
• Tibco delivers these capabilities based on a common SOA infrastructure, which allows customers to expose data integration processes as services, and manage data integration together with other services and components through the vendor's common management infrastructure. By setting trends in how data integration functionality (specifically, the development and deployment of data services) can be exposed to improve data management consistency in modern architectures such as SOA, Tibco is aligned with the vision for an information-centric infrastructure and support of EIM principles.
• Customer references report positive experiences with ease of implementation and support, scalability, and performance. Tibco's professional services capabilities are also perceived as strong.

• While Tibco has a large installed base for BusinessWorks (more than 1,400 customers), the uptake of DataExchange remains very slow, with an estimated 50 customers. Reference customers of DataExchange are still scarce. Early customers using both products in an integrated fashion report success in orchestrating batch-oriented DataExchange processes in the context of a BusinessWorks workflow. In general, Tibco is unable to provide a substantial number of customer references that reflect a range of data integration scenarios. This is a significant gap relative to nearly every other competitor in this market.
• While the trend toward the convergence of application integration and data integration technologies continues, Tibco faces the major challenge of gaining recognition for high-demand data integration competencies such as ETL. At the moment, Tibco is acknowledged by few potential buyers as having functional depth and a track record of supporting mainstay data integration use cases, such as bulk data delivery and transformation for data warehousing, or point-in-time data migration/conversion efforts. The vendor's marketing and sales strategies rarely acknowledge such scenarios, which further hampers Tibco's execution.
• Tibco currently lacks significant data quality capabilities in its portfolio, but maintains a limited technology partnership with Trillium Software, with Trillium developing adapters to enable its data cleansing functionality to be called from both BusinessWorks and DataExchange. Tibco lacks data profiling capabilities, although profiling results can be used in DataExchange via metadata import functionality.