Making data migration painless in an era of digitalization
The all-permeating digitization trends along with the pandemic’s severe impact have made it clear: moving to more flexible, cloud-based operating systems is the name of the game. Many organizations hope to make the transition fast and smooth. However, these huge multiyear data migration plans fail more often than companies would like to admit.
Rewind just a few decades back, and data migration was about moving files from one folder to another. Today’s datasets have become so enormous and complex that data migration has become a highly intricate process that requires specialized knowledge and skills more common to a big data company than a regular commercial enterprise. In this article, we will focus on the less talked about methods that can help companies make the transition less cumbersome.
Don’t do all data migration at once
Conventionally, companies consider data migration as a process of moving all available data to the new system at once. Given that many data migration initiatives stem from the pressing need of moving away from slow and cluttered legacy systems, this is an inherently ineffective approach.
It’s like moving your entire house to a new place trying to ensure that all the interior arrangements and room layout remain the same. It would be way more efficient, less risky, and more convenient to carefully pack your belongings, transfer them, and unpack in the new home. Also, it might be a good idea to visit the new home to check the layout, so you can have a better understanding of what things need to be transferred first. In fact, you may realize that you don’t even need some of the belongings in the new place.
This is exactly the approach that companies need to use when migrating data. Instead of trying to do it all at once, it’s more efficient to examine which data is indispensable for operations and start moving it first. This is why understanding the structure of the new system is critical. The thorough assessment of the new workflows offered by the new system can reveal that some data is unnecessary.
For example, the built-in analytics capabilities of the new system may require fewer metrics to calculate certain probabilities. The remaining non-essential data can still be kept in a variety of cold storage options for later use. This approach significantly speeds up data migration without filling the new system with data that is unnecessary or rarely used.
Prioritize quality over accuracy
In business, striving for perfection is always commendable but can also be a rather ineffective tactic for achieving certain objectives. For companies, data migration, especially when it’s a part of a company-wide digital transformation, is a sign of a new beginning. Understandably, it’s very tempting to make sure every dataset is perfectly accurate.
However, instead of focusing on accuracy, it’s far more important to ensure that the highest possible data quality standards are met. Legacy systems often store duplicated and redundant data, which is a common trace of rapidly-made and stress-induced business decisions of the past. This scattered, unorganized data is a much bigger barrier to successful data migration than a usable dataset that doesn’t achieve perfect accuracy.
Lastly, it’s critical to test and audit at every stage of data migration, not just before going live. Every time someone manipulates data as part of the project, there is a chance for some issues to creep in. Especially when you utilize the aforementioned approach of moving data in batches, it’s critical to conduct thorough tests after each batch is migrated. Importantly, even after the data migration project is complete, it’s paramount to continue reviewing and testing.
Data migration projects can succeed only with the involvement of both IT and business teams. Many important questions regarding data ownership, allowed downtimes, and compatibility will inevitably emerge. To prevent IT teams from answering these questions wrong, involve a dedicated project management team that has a thorough understanding of business objectives.
Data migration projects often appear to be more complex than they seem – there are just too many little things that can go wrong. Ultimately, high data quality standards, exhaustive testing, involvement of business teams, and a well-thought-out plan with disciplined execution will eliminate common hurdles in data migration projects.
Andrey Koptelov is an Innovation Analyst at Itransition, a custom software development company headquartered in Denver. With a profound experience in IT, he writes about new disruptive technologies and innovations.