When migrating data to a new system, be wary of these common mistakes that can derail your project.
Data migration can bring numerous benefits for the companies that move forward with it, such as improved business agility, better employee productivity and significant cost savings. However, businesses must keep in mind the most common data migration challenges and how to mitigate them before they start big migration projects.
This guide covers several data migration mistakes that often lead projects off track. Knowing about them is the first step to avoiding them.
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Common data migration mistakes
Trying to migrate data without a plan
Some companies get so excited about migrating their data that they try to proceed without a migration plan in place. Perhaps they underestimate the data center migration challenges ahead, or maybe they think they’ll figure out solutions as problems arise. Both of these mindsets are recipes for disaster during a complex process like data migration.
It takes extra time to develop a thoughtful plan, but having a strong foundation before starting data migration is one of the smartest ways to avoid pitfalls.
SEE: How to handle a multicloud migration: Step-by-step guide (TechRepublic)
While working out your data migration plan, consider getting advice from a vendor or other third-party company with extensive experience. Many of these companies offer consulting and managed services to help clients move through cloud and other migrations smoothly. These specialists should be able to answer any questions you have and bring up factors you might not otherwise consider in your own migration plan.
Scheduling a migration at the wrong time
Even if a data migration goes better than expected, it will likely cause business disruptions and downtime. Some people overlook that fact then panic when they realize it’s taking longer than expected and affecting customer experiences.
Figuring out the best time to do a migration means paying attention to relevant business activity patterns. Do most customer interactions occur at certain times or on particular days? Does your business have a particularly busy season?
A good tip for migration scheduling is to schedule big moves on the weekends when incoming data volumes might be lower. This scheduling decision usually reduces the possibility of data migration errors.
SEE: Data migration vs data integration: What’s the difference? (TechRepublic)
Regardless of your line of business and its idiosyncrasies, pay attention to the seasons, months, hours or days when your company is the busiest. Avoid those times and stick to the slowest periods of activity instead. In many cases, you can schedule and automate migration tasks during non-business hours as well.
Failing to set aside time and resources for testing
It can be tempting to delay testing or not engage in it at all during data migration, especially because testing can be tedious. However, forgoing the testing stage makes the project more likely to fail or be slowed down by unexpected and adverse outcomes.
Another mistake related to testing is leaving all of the testing to developers. Developers can and should certainly be involved in the testing process as technology professionals, but people from all other areas of the business should join them by actively participating in testing. Employees from other departments are more likely to catch application- and use-case-specific errors as they emerge.
Ultimately, testing should happen at every phase of the data migration. It’s also best if company representatives run a combination of formal test cycles and shorter unit tests to get to the bottom of data migration errors early.
How to cut down on data migration risks
It’s unrealistic to expect that your project will be free of data migration mistakes, but reducing these possible errors is well within your control. A good starting point is to choose specific individuals who will be responsible for overseeing the migration. Depending on the scope of your efforts, it may even be necessary and worthwhile to create a team of people who bear that responsibility.
SEE: Hiring kit: Data architect (TechRepublic Premium)
Your team should also determine which data you’ll move, when you’ll move it and why. Some business leaders make the mistake of moving every piece of information associated with their organization. However, that strategy elevates the likelihood of data migration mistakes and makes testing take longer than necessary. Most businesses have at least some legacy data sets and operations that do not need to be migrated.
Another best practice is recognizing when you might need external expertise to make the data migration process go smoothly. Third-party specialists can assess your situation, advise how long it should take and look for any issues that could pose problems later.
And finally, it’s best to anticipate that certain migration steps will take longer than they appear on paper. Perhaps a certain phase requires more in-depth testing or causes unforeseen obstacles. Maybe certain budget or resource constraints arise unexpectedly. Whatever the case, your team should err on the side of caution when making time-related estimates for your data migration plan.
What is the data migration failure rate?
Looking for statistics about how many data migrations fail can be a daunting exercise. For example, one study of large enterprises indicated that two-thirds of the businesses did not achieve their cloud migration goals.
Other research indicated that 48% of respondents felt concerned that data migration could lead to leaks and cyberattack increases globally. Another statistic showed that only 39% of respondents felt confident in their ability to secure cloud-stored data.
SEE: Top data integration tools (TechRepublic)
Executives define migration failure in various ways, and although it can be nerve-wracking, it’s critical to consider what a migration failure looks like for your business before the migration begins. Thinking about your project in the framework of what could go wrong makes it easier to spot problems early and bring in additional help at critical tipping points in the process.