10 Big Data Migration Mistakes5 Big Risks
(Page 2 of 2)
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.
- The Critical Importance of High Performance Data Integration for Big Data Analytics
- Transforming the Energy and Utility Industry with Big Data from Smart Grids
- Big Data and Smart Trading
- Big Data Analytics: Profiling the Use of Analytical Platforms in User Organizations
- Take the InformationWeek 2013 Database Technology Survey
- Strategy: Smartphone Smackdown: Galaxy Note II vs. Lumia 920 vs. iPhone 5
"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.
One of the biggest challenges facing IT today is risk assessment. Risk measurement and impact assessment aren't exact sciences, but there are tools, processes, and principles that can be leveraged to ensure that organizations are well-protected and that senior management is well-informed. In our Measuring Risk: A Security Pro's Guide report, we recommend tools for evaluating security risks and provide some ideas for effectively putting the resulting data into business context. (Free registration required.)