Planning a Data Migration in 2020? What You Need to Know and Tools to Help
By: Kayla Matthews on August 23, 2019
Proceeding with a data migration is a major undertaking, especially in today’s high-speed business environment where even a few minutes of downtime could result in sizable financial costs. Whether a company is only relocating data or executing a full-scale data center migration, there is a wide range of factors that could impact the project’s outcome. Fortunately, taking the time to carefully plan every aspect of the migration and identify potential risks along the way can protect a company’s IT investments, minimize service disruption, and help the transition go more smoothly. Here are some considerations for business owners and IT staff members as they prepare to make a move.
5 Steps for Planning a Data Migration in 2020
1. Understand What a Data Migration Is, and When to Plan One
The first thing for people to keep in mind is that many actions could encompass a data migration. In the simplest terms, data migration is the movement of electronically stored information.
One of the most common forms of data migration is moving data to a cloud environment for the first time or switching to another provider. People may also transfer data to a new file type or system, especially during upgrades.
Several possibilities exist for companies assessing the best options for moving their information. They might use import and export tools that make it possible to export data to a neutral format and upload it somewhere else. Alternatively, instead of doing that process through a user interface, the people handling a data migration may use database scripts. Other data migration tools make it possible to lift and shift data into the cloud with little change, although this may not maximize the potential of the cloud environment.
For other situations, there are extract, transform, load (ETL) tools that provide migration support for large collections of data. Many ETL options can connect to multiple sources and automate some of the steps. Other data integration tools have components that support initial migrations, especially in the case of enterprise networks.
Picking the most appropriate tools to use is an essential part of well-planned data migration. Generally, data migration tools fit into one of three broad categories:
On-Premises Migration Tools: These data migration tools are designed to transfer data within a medium to large enterprise data network. They are typically used to relocate data from one server to another.
Examples: IBM InfoSphere, Microsoft SQL, Clover DX.
Open Source Tools: These data migration tools are community-developed and are available either free of charge or at a very low cost. They tend to be most useful for migrating multiple types of data across multiple systems that might not be compatible. While they are extremely versatile, they often require a great deal of coding expertise.
Examples: Apache NiFi, Myddlware, Pentaho.
Cloud-Based Tools: A cost-effective form of data migration tools that focus on cloud environments. They are useful for migrating data between public and private cloud networks and can handle many different types of files.
Examples: Snaplogic, Fivetran, Alooma
3. Performing a Premigration Assessment Could Prevent Problems
Some companies want to begin a data migration without making any preliminary considerations. However, it's much less risky to have a thorough data migration checklist that begins with a premigration assessment. That segment involves going through an impact evaluation to measure a project's viability. The assessment should also include information about costs and necessary resources.
Taking the time to do a premigration assessment could allow businesses to steer clear of issues. This phase of the project may also make it apparent that outside help is necessary to carry out the data migration.
4. Recognize What Could Go Wrong
Companies may be overly confident about their abilities to have trouble-free migrations. Even if a business has an exceptionally experienced team and a firm grasp of the steps to take, pitfalls could still arise. For example, migrations could become problematic if enterprises don't establish a data governance body early in the process and clarify the role each person in that group plays.
A company that's going with an iterative approach may not keep previous versions of a system, making it impossible to refer to that earlier version when needed.
It's also possible that things could go wrong if a company doesn't properly vet the capabilities of its outsourced team members. Then, people that seemed adept at the start may prove their knowledge falls too short of the mark.
Data migration may also be destined for failure if the people working on it don't test anything until the end. In the best-case scenario, testing should happen throughout the migration. If it doesn't, late-stage testers may discover severe problems that testing may have uncovered sooner.
5. Don’t Overlook Security
Data migration can sometimes make it so file permissions and security settings don't stay intact. Then, the information may be more vulnerable to attacks or unauthorized access.
Although some data migration tools keep security specifics intact, that's not the case with all of them. Instead of making assumptions, company representatives should get clarification from vendors or other entities assisting with the migration.
For example, some cloud environments don't have the same security options as on-premise servers. Also, the service level agreements (SLAs) should offer transparency in the responsibilities the vendor has for keeping the migrated data secure.
Anticipating data migration needs will improve the likelihood that the relocation will be a success. Like almost anything else, a data migration can fail if businesses don't evaluate the large and small aspects of it. As such, companies should avoid making hasty decisions that don't involve thinking about the necessities to facilitate the process and get the business ready for the future.
About Kayla Matthews
Kayla Matthews writes about data centers and big data for several industry publications, including The Data Center Journal, Data Center Frontier and insideBIGDATA. To read more posts from Kayla, you can follower her personal tech blog at ProductivityBytes.com.