Organizations choose to migrate databases for a variety of reasons including trying to reduce costs by moving to open source databases, explore the power of fully managed cloud-based databases, seeking specific database features and functionality, or their existing systems are simply outdated and unable to keep up with the demands of the business.
Whatever the reason, database migration isn’t always as straightforward or simple as it may seem. So, the big question is, why is data migration so difficult in today’s business climate?
Here are the top challenges to keep in mind as your organization prepares to move its databases from one database engine to another.
1. Poor Knowledge of Source Architecture
The most effective way of delivering a data migration program is to fully understand the source architecture, system integration and business growing demands before deciding the target database engine. This is best achieved with a complete profile and audit of all source databases, applications within the scope at an early stage. With complete visibility of the source architecture, migration team can identify and address potential problems that might have remained hidden until a later stage.
2. Lack of Knowledge in Source Database
Over time, every company accumulates data. And if your company has been around a while, there’s a good chance your data is housed in a disparate databases created by different departments or teams. One of the biggest challenges in migrating databases is making sure to locate the various data sources in your environment and deciding how you’ll normalize data and convert schemas.
3. Underestimating Data Analysis
Due to constraints in computer systems, information can be hidden in obscure places because often there aren’t specific fields to hold all elements of the data or users may not be aware of the purpose of the available fields. This will result in incomplete, inaccurate and outdated information being transferred during the migration, often discovered very late in the day, even after the project has been completed. The outcome can mean not having enough time or the right resources to be able to identify and correct this data. Performing a thorough data analysis at the earliest possible occasion, usually when planning and designing your data migration can help you uncover these hidden errors.
4. Insufficient Migration Strategy
Database migration is a strategic process fraught with risk, and coming up with a plan for safely, securely, and efficiently migrating databases is imperative for a successful migration. For many teams, the primary obstacle to developing a database migration strategy is deciding whether to pursue a “big bang” migration, in which the migration is completed in one step, or to leverage a more methodical, incremental approach called a “trickle” or “parallel run” migration.
5. Lack of Collaboration
It has already been mentioned that data migrations involve disparate people, using different technologies, and in some cases a mix of internal employees and external contractors. Some of these people may not even be in the same location. Working in silos can reduce efficiency, create more data silos and sometimes lead to misinterpretations. Working together can be difficult and when things start to go wrong most try to avoid blame rather than resolving the issues. Collaborative tools enable all parties invested in a migration to see the same picture of data as it moves through the project stages, leaving little room for assumptions and misunderstandings.
6. Thoroughly Testing & Validating Processes Involved
Rarely do you find organizations are ready to afford testing and quality assurance, but database migration programs demand it. If database migration programs don’t include testing the migration process thoroughly then organizations invite a lot of risk after migrating to a new database engine. Migration validation includes validating data, validating schemas, integration testing, load testing, application testing. Testing your migration using full volume data from the real world helps cover a wider range of possibilities and tests for the worst case scenario, which could be missed when using more convenient samples of data.
7. Late Evaluation of the Final Results
This problem can occur in the testing stage, where users only see the actual data that will be loaded into the new system at the end of the design and development. At this point, one of the worst outcomes can arise – incompatibility of the data in the new system. While an organisation is capable of working without remedying the problem, this is not best practice. Time, money and the embarrassment of a delayed project can be avoided by introducing early and agile testing phases and getting your users involved in evolving the test cases as they see actual prototypes of the data output.
8. Securing data and systems
Moving data from one platform to another isn’t just time-consuming and costly, it also has the potential for increased risk without the right protocols and plans in place. In any migration, there’s a treasure trove of high-value intellectual property that may be leaked, lost, or otherwise accessed by unauthorized users (either inadvertently or with malicious intent). Each instance could mean significant damage to company reputation, customer churn, and or even potential lawsuits and punitive fines.
A database migration project is challenging and high risk but nothing can stop if planned well. If each of these hurdles are acknowledged during the planning stage and are overcome early, you can be sure of success.