Informatica has announced findings from an independent research study it commissioned Bloor Research to undertake on the data migration market.

The total budget for data migration projects undertaken by just Global 2000 companies in 2007 is set to break the $5 billion mark, rising to $8 billion by 2012, yet 64% of these projects are not delivered on time and 37% experience serious budget overruns that boost project costs by an average of 30%, according to the study. The research also reveals how this trend of data migration failures can be entirely reversible.

According to the study, data migration time and cost overruns can be substantially reduced or even eliminated through preventive actions and the application of appropriate disciplines—including data governance, on-going data quality programs, focused migration methodologies, and the up-front use of data profiling and analysis techniques.

The following are among the study's key project-related findings:

• Despite the enormous criticality of data migration projects, only 16% of them studied could be viewed as unqualified successes—i.e.: exhibiting neither time nor cost overruns.
• Where there were time overruns, they averaged in excess of 40% of the total budgeted time.
• Where there were cost overruns, they averaged in excess of 30 percent of the total budgeted cost
• A mere 10% of companies interviewed used data profiling tools to better understand their data and data sources before scoping their projects.
• Hand-coding is still the most prevalent "tool" in data migration projects—which can prove to be error prone and inadequate.
  
  Recommendations
  
  Based on these and other findings, the study recommends the following preventive measures and actions:
  

• Implement data governance across the enterprise so that the location, nature and condition of enterprise data is always properly understood.
• Implement data quality initiatives so that data is maintained at a high level of migration-readiness, in addition to the numerous other benefits of ensured data quality.
• Profile and analyze all data sources in advance using a proper tool in order to fully understand the scope of data issues that will be encountered including how they may impact project timelines and costs.
• Ensure full familiarity with, and training in the use of, any tools, prior to commencing the project.