From a high-level point of view, the task of data migration is simple: moving data between two or more locations. Yet these days, data migration is much more than moving a folder from one filing cabinet to another. In practice, migrating data is one of the trickiest and most complex data management initiatives for your IT department, especially if you want to follow data migration best practices.
According to a study by Bloor Research, the failure rate for data migration projects stands at a concerning 38 percent. Gartner confirms this pessimistic outlook, finding that 83 percent of data migrations either fail or exceed their original timeline and budget.
Why do so many data migration projects encounter serious challenges? Often it’s because practitioners fail to observe some of the well-established data migration best practices. In this article, we’ll discuss the 7 best practices for data migration.
Table of Contents
Why Data Migration Best Practices Are Essential
Data migration is a necessary part of maturing as a data-driven organization. As the volume, variety, and velocity of big data continue to increase, you’ll likely find that your old systems, software, and databases are no longer suitable for your needs.
This means that you’ll have to move data from the old system to the new one quickly, efficiently, and while keeping downtime to a minimum: in other words, by following data migration best practices. However, this feat is easier said than done. The major issues surrounding data migration initiatives include:
- Data Gravity: “Data gravity” is a term coined by software engineer Dave McCrory that refers to the tendency for data and software to “attract” each other over time. As more and more data accumulates in a single location, organizational inertia makes it harder and harder for the data to move away from that location without breaking the applications that depend on it.
- Compliance and Security: Your enterprise data must be protected from security threats during the migration, both while in transit and at rest. Data for certain industries, such as healthcare and finance, is also governed by strict regulations such as HIPAA and Sarbanes-Oxley, which come with harsh penalties for misusing or mishandling sensitive information.
- Legacy Systems: Legacy systems are a fundamental part of many organizations’ data management strategies. However, there comes a time when maintaining your aging, out-of-date infrastructure becomes more painful than the migration itself. Moving data away from legacy systems will require you to define certain data transformations and normalizations, in order to adapt the information to its new resting place.
The good news is that you can overcome these challenges and ensure a successful migration project—if you follow the data migration best practices and recommendations. However, if you don’t carefully strategize and set up the right infrastructure for the move, it’s all too easy for a promising data migration project to fail, continuing to hold your business back from achieving digital transformation.
In the next section, we’ll go over what these data migration best practices actually are, and what impact they’ll have on your data migration project.
7 Data Migration Best Practices
1. Setting the Mission Scope
The path to following data migration best practices starts with defining the parameters of your data migration project, well before you begin.
Like any other large IT undertaking, data migration is vulnerable to “scope creep,” in which the goals of the project expand over time, causing delays and further complexity. Instead, it’s a good idea to start small when migrating data—for example, moving only the contents of a single database—and then expanding the scope when the pilot is a success.
2. Establishing a Realistic Time Frame
Establishing a concrete time frame is another of the most important data migration best practices. Be aware that large projects can take several months or even as much as a year.
The migration shouldn’t slow down your existing business operations, so spreading it out over time will allow the project to be as minimally disruptive as possible. Make sure to give yourself enough room to allow for unexpected delays and extra work.
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2. Analyzing the Data
Data migration initiatives range from the simple to the very complex, based on a few important factors: the size of the data, the type of data being migrated, and the source and target systems.
Before the migration begins, take the time to understand how much data actually needs to be moved. Are there fields or records that you don’t use, and that can be left behind? Is there missing information that needs to be filled in from another data source?
Once you take a closer look at the data itself, and how it’s used in your organization, you should have a good idea of which data migration strategy to pursue. The two most popular options are often referred to as “big bang” and “trickle” migrations, based on the speed of the move and how many phases will be involved. “Big bang” migrations are better when you can afford a designated window of downtime, while “trickle” migrations are better for systems that shouldn’t be interrupted.
3. Cleansing the Data
Data migration is a GIGO (garbage in, garbage out) process. If you don't follow data migration best practices from start to finish, you'll simply be moving low-quality data from one place to another; the quality won’t magically improve once it arrives at its destination.
That’s why a large-scale data migration can be the opportune time to perform a “deep clean” of your enterprise data. Removing inaccurate, out-of-date, and duplicate information before the project begins will have a marked impact on your data quality. In addition, if the data migration is being done in phases, you should run testing and validation checks throughout the project to correct any issues in a timely manner.
However, a one-off data cleansing is no replacement for solid, thorough data management policies. Establishing good data governance will pay dividends long after the migration project is complete.
4. Aligning IT and Business
Data migration is the IT department’s responsibility under the hood, but it affects the entire organization. Getting approval from key stakeholders, and aligning the IT and business teams before the project kicks off, are crucial steps for successful data migration.
IT must understand the overarching business goals of the migration, while business should be mindful of the technical challenges involved in a project of this nature. Both teams need to collaborate in order to counteract the harmful impact of data gravity and data silos, which can delay or derail a promising data migration. Many tools can even empower non-technical business users to perform data migrations themselves (see the next section).
5. Selecting the Right Tools and Vendors
If you don’t have the in-house experience and know-how to follow data migration best practices yourself, then using third-party migration tools and/or vendors is a wise idea. Given enough time and energy, cobbling together a data migration solution on your own is possible, but the transition will be much faster and smoother if you work with someone who knows what they’re doing.
Do your research to find out which data migration platforms or vendors are best-suited for your situation. Tools like Xplenty make it easy to move data between systems for straightforward data migration projects. If you plan to join forces with a vendor, evaluate them based on the technology they use and their previous experience working on similar projects.
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6. Backing Up Data
Data loss is a frighteningly realistic prospect during a migration. According to a survey by the consulting firm Kroll, 32 percent of IT administrators have lost data when migrating to new software from a server. However, losing even a few critical records during the migration could be a data catastrophe for your organization.
In order to avoid these pitfalls, have a robust data backup and a business continuity plan in place well before the migration begins. It’s also a good idea to first run the migration in a test environment, to make sure that the entire operation is seamless before using it in production.
In this article, we've covered these best practices for data migration:
- Setting the mission scope
- Establishing a realistic time frame
- Analyzing the data
- Cleansing the data
- Aligning IT and business
- Selecting the right tools and vendors
- Backing up data
From strategic planning to choosing the right set of tools, these are the recommended steps to follow when moving data between systems. However, data migration is only the first step in what should be an ongoing process of data management.
To ensure that you don't fall behind the competition, data migration needs to be a component of your broader data integration and data management strategy to ensure that your databases and data warehouses enjoy access to up-to-date, accurate information. Fortunately, many of these same data migration best practices, such as aligning IT with business and choosing the right tools, apply to the broader field of data management as well.
Observing these data migration best practices—and enacting a long-lasting data management strategy—is a lot easier when you’re using a tool like Xplenty. Xplenty is a powerful, feature-rich data integration and migration platform with a user-friendly, drag-and-drop interface. With more than 100 pre-built connections, Xplenty makes it easy to move data between the systems and software that you need for your data migration project, whether it’s databases and data warehouses, SaaS applications, cloud data storage, analytics tools, and more.
Want to learn how Xplenty can make your next data migration project a success? Get in touch with the Xplenty team today for a chat about your business needs and objectives, or sign up for a free trial of the Xplenty platform.