As more companies look to accelerate their digital transformation, many are turning to the cloud to reap the benefits of a flexible, dynamic, and cost-effective environment. Achieving a headache-free cloud migration starts with the strategy.
A solid data mobility strategy should prioritize business considerations over data mobility constraints, utilize a solution designed for large data sets, and minimize downtime during migration. One of the first steps in adopting a cloud strategy is evaluating your data and determining which processes, applications, and databases are best suited for the cloud.
Choose Vendor-Agnostic Tools
Choosing a vendor-agnostic solution is a key aspect of successful and seamless cloud migration. This allows for greater interoperability and manageability between cloud providers, ensuring data mobility across any cloud or storage technology. Organizations should prioritize data mobility solutions that can handle large data sets and provide the ability to “rewind” in case of issues.
A multi-cloud strategy is increasingly common among organizations, so it’s crucial to have a data mobility solution that works across public, private, and hybrid clouds without compatibility issues. This enables businesses to make data location decisions based on their needs rather than constrained by data mobility limitations.
The User Comes First: Low-Impact Tech for a Seamless Migration
Experience matters in data migration projects, as does time. Gartner states that more than 50% of data migration initiatives will exceed their budget and timeline. One way to accelerate cloud migration speed is by leveraging data reduction with encryption throughout the migration process. This approach offers a more efficient migration without sacrificing security.
A successful data mobility strategy should also involve synchronizing data in the background without impacting users. As users work on the old site, the new infrastructure updates simultaneously, requiring seconds to minutes of cut-over time. This can be planned during off hours, minimizing disruption and avoiding the need for employee notification.
Plan Ahead to Avoid Common Mistakes
One of an organization’s biggest mistakes when planning a cloud migration is focusing too much on cloud performance and neglecting business requirements.
Companies should consider the following questions when planning their migration:
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What data and applications are you planning to move to the cloud?
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How expensive is downtime to your organization?
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How do you move your data to public clouds?
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Once it’s there, how do you move the data between clouds or even bring it back on-premises?
A common technique is the “lift-and-shift” approach, or the process of moving an exact copy of a workload into the cloud without modifications. An advantage to this approach is it allows organizations to maintain portability from cloud to cloud; however, it requires careful evaluation and right-sizing of data before beginning the migration process. Proper preparation can help organizations avoid mistakes that lead to cost overruns if the app or data isn’t formatted properly before migration.
Relying on “free tools” for data migration can also be costly. These tools often have hidden costs and risks that can lead to delays or data loss, particularly when blocking data migration. Organizations must consider the unique requirements of block data migration and the potential impact on business operations.
McKinsey estimates that around $100 billion of wasted migration spend is expected over the next three years, with many enterprises citing migration costs as a major barrier to cloud adoption. To avoid the “cost snowball effect,” organizations must invest time in upfront planning and preparation for large-scale data migrations.
A well-planned cloud migration strategy is essential for businesses looking to harness the benefits of the cloud. Data migration can be a complex process, but organizations can minimize headaches and maximize efficiency in their cloud migration projects by focusing on data evaluation, choosing a vendor-agnostic solution, prioritizing data mobility, and reducing the impact to users.
