Pervasive digitization trends along with the severe impact of the pandemic 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 quick and smooth . However, these huge, multi-year data migration plans fail more often than companies would like to admit.
Rewind a few decades back and data migration was all about moving files from one folder to another. Today’s data sets have become so huge and complex that data migration has become a very complex process that requires more commonplace specialist knowledge and skills to a big data company than an ordinary commercial enterprise. In this article, we’ll focus on the less talked about methods that can help businesses make the transition less cumbersome.
Don’t do all data migrations at once
Traditionally, companies view data migration as a process of simultaneously transferring all available data to the new system. Since many data migration initiatives stem from the pressing need to move away from slow and cluttered legacy systems, this is an inherently inefficient approach.
It’s like moving your whole house to a new place trying to make sure that all the interior layouts and room layouts stay the same. It would be much more efficient, less risky and more convenient to carefully pack your things, transfer them and unpack them in the new house. Additionally, it may be a good idea to visit the new home to check out the layout, to better understand what needs to be transferred first. In fact, you might find that you don’t even need some personal effects in the new place.
This is exactly the approach companies should use when migrating data. Instead of trying to do everything at once, it’s more efficient to look at what data is critical to operations and move it first. This is why it is essential to understand the structure of the new system. Thorough evaluation of the new workflows offered by the new system may reveal that some data is unnecessary.
For example, the new system’s built-in analysis capabilities 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 dramatically speeds up data migration without filling the new system with unnecessary or rarely used data.
Prioritize quality over precision
In business, the pursuit of perfection is always laudable but can also be a rather ineffective tactic for achieving certain goals. For companies, data migration, especially when part of an enterprise-wide digital transformation, is a sign of a new beginning. Naturally, there is a great temptation to ensure that each data set is perfectly accurate.
However, instead of focusing on accuracy, it is far more important to ensure that the highest possible data quality standards are met. Legacy systems often store duplicate and redundant data, which is a common trace of business decisions made quickly and stress-induced from the past. This scattered and unorganized data is a much greater obstacle to a successful data migration than a usable data set that does not achieve perfect accuracy.
Finally, it is essential to test and audit at every stage of data migration, not just before going live. Whenever someone manipulates data within the scope of the project, certain problems may arise. Especially when using the aforementioned batch data movement approach, it is essential to perform thorough testing after migrating each batch. It is important to note that even after the data migration project is complete, it is essential to continue reviewing and testing.
Data migration projects can only be successful with the involvement of IT and business teams. Many important questions regarding data ownership, permitted downtime, and compatibility will inevitably emerge. To prevent IT teams from answering these questions incorrectly, involve a dedicated project management team that has a deep understanding of business goals.
Data migration projects often seem more complex than they appear – there are simply too many little things that can go wrong. Ultimately, high data quality standards, thorough testing, business team involvement, and a well-thought-out plan with disciplined execution will break down common roadblocks in data migration projects.
Andrey Koptelov is an innovation analyst at Itransition, a custom software development company headquartered in Denver. With extensive experience in IT, he writes about new disruptive technologies and innovations.