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Gartner Magic Quadrant for Data Integration Tools Snapshot

by Brian Rimes

Aug 11, 2015

The need for enterprises to rapidly and reliably deliver trusted data across a variety of usage scenarios has landed data integration at the top of IT executives’ minds. In order to successfully share data across all organizational and technological boundaries, enterprises need to employ comprehensive data integration capabilities.

According to Gartner’s BI Glossary, the definition of data integration is a discipline “comprised of the practices, architectural techniques and tools for achieving the consistent access 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.”

Data integration is often seen as a strategic imperative critical for enabling the construction and implementation of data access. In order to assist enterprise IT buyers in selecting the best-fit data integration tool for their business, Gartner assessed 13 software vendors and published their findings in the Magic Quadrant for Data Integration Tools Report.

Gartner Magic Quadrant for Data Integration

2015 Gartner Magic Quadrant for Data Integration Tools

The 13 vendors included were:

1. Actian
2. Adeptia
3. Cisco
4. Denodo
5. IBM
6. Informatica
7. Information Builders
8. Microsoft
9. Oracle
10. SAP
11. SAS
12. Syncsort
13. Talend

The above visual illustrated the results of Gartner’s study, ranking the 13 vendors on overall ability to execute and completeness of vision.

Let’s take a deeper look at the key strengths of those leading vendors, Informatica, IBM, SAP, Oracle and SAS, in order to identify what features customers value most in in a data integration tool.


  • Robust and reliable support for a diverse and balanced set of use cases
  • Capabilities in adapters and connectivity, comprehensive data transformation and encapsulating data into real-time message flows
  • Synergy of enterprise information capabilities and master data management functions
  • Seamless implementation integration, process management, data governance and analytics


  • Deployed for extensive use cases — often of complex scale, spanning a wide range of projects and involving teams of various sizes
  • Enterprise standard for data integration infrastructure
  • Diverse analytics support linked with data integration capability
  • Versatile in enabling information infrastructure and governed analytics
  • Increased self-service capabilities


  • Breadth of functionality across a diverse, synergistic portfolio
  • Diverse mix of data integration styles and use cases of increasing complexity
  • Built-in data integration adapters
  • Enhanced collaboration between business users and data integration practitioners


  • Broad usage and applicability
  • Enhanced big data support
  • Expanded focus on machine learning in modeling and design processes
  • Synergistic capabilities across a diverse portfolio for addressing data integration and other data and application-oriented requirements


  • Breadth and completeness of core functions and the integration of components
  • Guided user interface to facilitate data loading and data preparation
  • Dependable customer support
  • Overall stability, reliability, robustness and effectiveness
  • Synergistic capabilities across portfolio

Common cautions of those lesser ranked tools revolved around migration issues, barriers to broad user adoption, customer support, and lack of training and technical resources. Failure to answer key objectives such as big data capabilities and metadata integration were also common weaknesses.

To read the complete Gartnet Magic Quadrant for Data Integration Tools Report,  visit