10 Big Data Migration Mistakes

And now for the five big data migration risks:

Risk #1: Employees entrusted with a data-migration project lack industry best-practices experience.
A organization's employees may be very good at what they do, but that doesn't mean they're experts in data management, migration, and governance.

"They are creators and consumers of data, but they're not fully familiar with best practices on tools, processes, services, templates, and accelerators," Singh said.

Risk #2: Your team relies too much on the tools of the job.
This problem is often the result of staff inexperience. A data-migration project often falls in the lap of IT, which may not be properly trained to manage it. A migration tool used improperly can wind up moving bad data. "It's the garbage-in, garbage-out analogy," Singh said.

Your goal, of course, is to transfer data quickly and reliably. What matters is how well you use data-migration tools, and "what accelerators and templates you have that go along with the tools," said Singh.

Risk #3: Cross-object dependencies.
"I can't tell you how many times I've sat in a meeting where (a client) said, 'We just discovered a whole new source of data that we were not even aware needs to be moved,'" Singh said.

Cross-object dependencies often are not discovered until very late in the migration process. A complex project may have 60, 70, or perhaps even 80 different data objects coming in from a hundred or so different applications.

"When we get involved with clients, we look for those missing pieces of data or dependencies," said Singh.

Indeed, cross-object dependencies--and discovering new sources of data late in the process--are major risks that can throw off your migration timeline.

Risk #4: Attempting to go live in one big upload at the end.
This is a recipe for disaster, Singh said, because you're assuming that everything is perfect--that you're going to be able to simply hit a button, and all the data will load flawlessly.

"That's a big risk," he said. "You need a project timeline with multiple, iterative test loads along the way."

Risk #5: Budget overruns due to inadequate scoping or preparation at the start.
This often happens when an organization believes its systems integrator (SI) will take care of these details. Big mistake.

"Most SIs usually don't handle data beyond saying, 'I'll connect the pipes and move the legacy data into a target system,'" Singh said.

"We get called into data-migration projects that are in the realization phase," he said, "and people are saying: 'Look, the data isn't tying together, we're not able to do user testing.'"

This problem, of course, can lead to cost overruns and wrecked timelines.

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