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Hype Cycle for Data Management, 2006
6 July 2006
 
Ted Friedman   Donald Feinberg   Mark A. Beyer   Bill Gassman   Andreas Bitterer   David Newman   John Radcliffe   Andrew White   Raymond Paquet   Carolyn DiCenzo   Debra Logan   Mike Blechar   Rita E. Knox   Toby Bell   Karen M. Shegda  

Technologies and best practices for data management continue to evolve rapidly. The Gartner Hype Cycle for Data Management can help organizations understand the relative maturity of these technologies and best practices, the benefits of adoption, and key factors for successful deployment.




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Table of Contents

Contents
  • Analysis
    • 1.0 What You Need to Know
    • 2.0 The Hype Cycle
    • 3.0 The Priority Matrix
    • 4.0 On the Rise
      • 4.1 Model-Driven Data Services
      • 4.2 Open-Source Data Integration Tools
      • 4.3 Hosted Data Integration and Data Quality
      • 4.4 Database Archiving
      • 4.5 Enterprise Information Management
      • 4.6 Data Service Architectures
      • 4.7 Master Data Management
      • 4.8 Metadata Repositories
      • 4.9 Data Enrichment
      • 4.10 OSS DBMS for Data Warehousing
    • 5.0 At the Peak
      • 5.1 OSS DBMS for Mission-Critical Applications
      • 5.2 XQuery
      • 5.3 Content Integration
      • 5.4 Data Integration Tools Convergence
      • 5.5 Data Profiling
    • 6.0 Sliding Into the Trough
      • 6.1 Data Warehouse Appliances
      • 6.2 Data Federation/EII
      • 6.3 OSS DBMS for Simple Applications
      • 6.4 Linux as a DBMS Platform
      • 6.5 Real-Time Data Integration
    • 7.0 Climbing the Slope
      • 7.1 Data Cleansing and Matching
      • 7.2 XML-Enabled Database Management Systems
      • 7.3 Mobile and Portable Device DBMS
    • 8.0 Entering the Plateau
      • 8.1 ETL Tools
    • 9.0 Appendices
      • 9.1 Previous Iteration of the Hype Cycle
      • 9.2 Hype Cycle Phases, Benefit Ratings and Maturity Levels
  • Recommended Reading
Tables
Table 1.
Hype Cycle Phases
Table 2.
Benefit Ratings
Table 3.
Maturity Levels
Figures
Figure 1.
Hype Cycle for Data Management, 2006
Figure 2.
Priority Matrix for Data Management, 2006
Figure 3.
For Reference: Hype Cycle for Business Intelligence and Data Warehousing, 2005




Document History:
 
Hype Cycle for Data Management, 2011
26 July 2011
  
Hype Cycle for Data Management, 2010
22 July 2010
  
Hype Cycle for Data Management, 2009
24 July 2009
  
Hype Cycle for Data Management, 2008
9 July 2008
  
Hype Cycle for Data Management, 2007
2 July 2007
  
Hype Cycle for Data Management, 2006
6 July 2006
  




© 2006 Gartner, Inc. and/or its Affiliates. All Rights Reserved. Reproduction and distribution of this publication in any form without prior written permission is forbidden. The information contained herein has been obtained from sources believed to be reliable. Gartner disclaims all warranties as to the accuracy, completeness or adequacy of such information. Although Gartner's research may discuss legal issues related to the information technology business, Gartner does not provide legal advice or services and its research should not be construed or used as such. Gartner shall have no liability for errors, omissions or inadequacies in the information contained herein or for interpretations thereof. The opinions expressed herein are subject to change without notice.




Resource Id: 493657