Big data is driving the operations of modern-day businesses. As customers continue to favor digital solutions, e-commerce, and social networks, the volume of data that organizations hold will continue to grow. If companies want to realize the benefits of their data assets, they need to examine how they can scale up effectively. In many cases, this involves data and database migration.
Without a data migration plan, a business can run out of storage space, go over budget, or end up with several overwhelming processes that are difficult to keep functioning.
What is data migration?
A data migration process moves data from one system to another. It is often known as database migration, with the two terms being used interchangeably. Although at a base level, data migration sounds like it should be simple, there can be technical complexities around changing applications and services.
There are various reasons why an organization requires data migration. They could invest in new systems, move from on-premise to cloud services, create a new data warehouse, or acquire data from a different source. In each of these cases, they will need to carefully plan how data will migrate between the two platforms or applications.
Typically, data migration receives a lower priority than system implementation. However, new technology is only as good as the information within it, meaning businesses that fail to prepare their data effectively will not see the benefit of any investments.
The data migration approach
Phase 1 – Analyze and Discover
The first part of a database migration project is for resources to come together and ascertain all the source systems in scope. The process should involve documenting the different formats and attributes of the data and forming a strategy. The analysis phase is crucial for directing the rest of the data migration lifecycle.
Phase 2 – Profiling and extraction
Data profiling tools will analyze the data and help visualize what the business is working with. The insights from this stage will offer a data quality assessment and allow resolving any issues before moving towards database migration.
Phase 3 – Data cleansing
Following extensive profiling, data migration specialists will use automated tools to help cleanse the data. The processes will include deduplication, matching, and data integration or enrichment. For data cleansing services, contact StrategicDB.
Phase 4 – Validation
Before any data loads into a new system, the business should validate the results of the data cleansing exercise. In data migration terms, this is known as pre-load validation and will consist of generating several reports.
Phase 5 – Data loading
When the data validation is complete, an ETL developer will load it into the target system. The method they use will depend on the systems involved, but it will typically run outside of business hours to mitigate risk.
Phase 6 – Data reconciliation
Data migration does not finish after loading it into the new system or storage space. Teams will access the new location and run further validation reports to check the configuration. Post-load reports and samples will be given to departments sd they can complete data quality spot checks.
Data migration is an inevitability for businesses as they look to grow and adapt to changing markets. It is pivotal to follow the right processes to maintain data quality and avoid the common pitfalls with database migration projects.