Nicholas Carr presented a thesis five years ago that IT no longer provided the differentiating competitive advantage and therefore no longer mattered. Since then, he has been proven wrong many times over with billions of dollars spent on new IT initiatives, simply because there was a positive, if not high, Returns on Investment (ROI) on most enterprise-wide IT projects.

There is always a scrutiny every time a CIO presents a project for approval. Being a relatively new discipline, Database Archiving had been one area that got relegated to a lower priority simply because there were few implementations and even fewer evidences of hard numbers on ROI.

A few years ago, at an Oracle Conference, a customer hammered “Why Aren’t You Archiving”? He presented a convincing case with a strong ROI. As with all new technologies, there’s a tipping point for adoption to accelerate. That tipping point may have just happened.

Gartner’s June 2008 Report on Hype Cycle for Storage Software Technologies has unequivocally stated that the ROI for implementing a Database Archiving solution is “exceptionally high”. It further goes on to add “When database archiving is used for application retirement (with retired data archived to tape in an XML format), the business impact can be even more dramatic”.

This Report could not have come at a better time. With the economy near to a recession, it underscores what we have been saying all along. Archive to not just improve application performance; archive to improve business agility. Archive to not just reduce storage costs; archive to reduce energy costs. Archive to not just ensuring data retention; archive to insure against an expensive litigation. The high ROI is indisputable.

And for applications retirement, the Solix archiving solution with its core as a metadata based repository, we not only move legacy data to a XML format, but we can also move it to the new database of the new application, before we finally retire it to a Tier 3 storage. Archiving before upgrading or migrating reduces time, implementation costs and carrying over “dirty” data.