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Automation has a tremendously positive impact on the enterprise; enabling companies to migrate their legacy database, platforms, and applications to more modern systems faster, with lower costs, and less impact on resources while drastically reducing exposure to risk.

The graph below shows the different levels of automation achieved through MDMS for the various functions of a database migration. The final output to each customer is 100% automation; thereby achieving the levels of success required.

MDMS based automated conversion levels per DB object are shown below:

automation-impact

Business Impact – Automation Value – Industry Benchmark

The largest risk for any organization is manual development and/or manual re-write of code

  • Manual code re-writes are 6 times more likely to fail than automated migrations driving up business risk
  • Project management overhead, sophisticated requirements gathering, converting requirements into detailed specifications, load and performance testing, and other activities drive up manual re-write scopes and complexities
  • Timelines are extensive and company resources spread very thin
  • Project costs can spiral as scope, complexity, and timelines all increase

Automation projects represent a far more risk averse path to meeting business needs

  • Stand out as having the highest chance of success and the lowest risk of failure
  • Mechanical, well suited for contemporary IT organizations and the state of their project management capabilities
  • Avoid the harder project management activities such as gathering user requirements and receiving the executive attention which manual code re-write projects necessitate

project-resolution

Business Impact – Resource Optimization

The major activities in an automation migration projects are mostly mechanical. Major activities include code conversion, data source conversion, refactoring, re-hosting, quality control, and testing.

Analysis: The time to do a manual migration of a legacy system with a million lines of code would require some 28+ man-years of labor to convert. With a team of 10 this takes 3 years, if done well. With larger systems, one has larger teams. Larger teams require more interactions, slowing the process down further.

Estimated Resource Utilization Efficiencies – Automated vs. Manual

  • Analysis Phase – 10% resource savings
  • Port Phase – 90% resource savings
  • Migration Hardening Phase – 75% resource savings
  • Testing and UAT Phase – 50% resource savings (automation produces far less errors than manual or semi-auto re-writes)