Enterprise data migrations: maximizing success with adoption of best practice frameworks

The business buy-in constitutes the necessary condition for a successful data migration, as business is true custodians of data. And while one cannot talk about sufficient condition for a successful data migration, one can overcome serious challenges and avoid common pitfalls by adopting a best practise framework. Simon Ter-Antonyan, Senior Consultant, exigo s.a. gives some tips.

The business buy-in constitutes the necessary condition for a successful data migration, as business is true custodians of data. And while one cannot talk about sufficient condition for a successful data migration, one can overcome serious challenges and avoid common pitfalls by adopting a best practise framework. Simon Ter-Antonyan, Senior Consultant, exigo s.a. gives some tips.

With change being the only constant in life, organizations of different sizes, industries and structures face critical data migrations, making it part of business as usual. According to the industry analyst reports (Bloor Research 2014), the success rate of data migration projects, while exhibiting a positive increase over the past eight years, still remains low enough to keep data migrations in the category of high risk projects. Among the trends contributing to the growth of the success rate is increased adoption of tried and tested formal data migration methodologies. As veterans in the domain of data migration, we rely our own tried-and-true data migration methodology, which is flexible enough to allow a degree of customization welcomed by practitioners, as we know from experience that no two data migration projects are alike.

Few tips for delivering a data migration projects on time and budget

  • Adopt a business-centric attitude for common activities, such as performing the gap analysis and mapping, the data profiling, the archiving and legacy decommissioning: this will prevent an uncontrolled recursion between IT and the business, which typically results from various data quality issues.
  • Correctly integrate the data migration within a larger program: recent study indicated that 70% of data migration projects occur as part of a larger programme. Defining communication strategy, creating transitional business processes, integrating with release management and creating control flows and project reporting will establish how your data migration fits in and integrates with the larger programme.
  • Keep the scope creep under control: tight integration between the landscape analysis and key data stakeholder management, the latter providing the list of business owners and business domain experts to guide the discovery and documentation of the landscape and topography of the data stores, will help prevent the scope creep. Keep the scope creep under control by managing the data quality rules process to make sure that the appropriate data that is of the right quality is at the right place at the right time.
  • Follow an integrated set of processes and activities to keep complexity under control: actual scale for most data migration projects cannot be correctly gaged in advance, unless specific cautions are taken right from the start. The tight integration between landscape analysis and key data stakeholder management, the latter providing the list of business owners and business domain experts to guide the discovery and documentation of the landscape and topography of the data stores, will help you scope the project accurately upfront.
  • Start planning for source system retirement early: we always stress out the importance of system retirement planning, as this activity prevents serious fallbacks by effectively dealing with the essential business issues such as audit requirements, data lineage requirements, data retention requirements, migration restrictions, go-live restrictions, and transitional business processes.

Following tried-and-tested data migration framework helps tackle the complexity of delivering data migration projects on-time and on-budget within the realities of working under a great pressure in a politically charged data migration environments.