The necessity of creating a robust migration plan that concisely explains the various data conversion phases is evident, as more than 80% of data migration projects go over schedule or over budget. Even though the same procedures may and should be used for all data migration projects, adhering to a rigorous, well-defined plan becomes especially crucial when working with complicated, massive volumes of data.
We work on a range of phased data migration projects and employ the subsequent step-by-step methodology to help ensure a smooth and successful relocation. Discover, data enhancement and cleanup, proof of concept, test migration, and production migration may all be summed up as these processes.
The five ETL processes of analysis, extraction, transformation, loading and validation are all included in each of these steps. By taking these actions, we may increase migration process effectiveness, enhance search and usability, and lower the chance of unscheduled downtime, lost or damaged data, or compatibility issues resulting from the migration.
Five Important Phases of Data Migration
Table of Contents
Discovering is the first phase of the data migration process. With the aid of this phase, a high-level understanding of the present repository may be developed. This understanding can include details about documents, containers, fields, locations, and schedules. To understand how it might affect the migration, a thorough report detailing any identified dependencies and constraints should be created.
It should include information on the current approach to security and access, the identification of ROT (redundant, trivial, and obsolete) information, as well as the current vs. target information architecture.
2. Data Enhancement and Cleaning
People normally won’t immediately pack up everything they own, including trash and broken belongings, and recklessly put them into a box to be transported into a new home while relocating to a new residence. Unless you’re a hoarder, most of us will spend some time going through our belongings, getting rid of anything we don’t need or use anymore, organizing things better, and giving each box a sensible description.
When migrating organizational information and data, a similar strategy should be used. It is uncommon that an organization wouldn’t gain from the elimination of ROT data, and a lift-and-shift strategy is useless unless the information architecture is up to date and appropriately serves business goals.
The usability of the repository must be enhanced during this phase in order to optimize search. It focuses on filtering out and deleting information that was recognized in phase one as being redundant, trivial, and obsolete as well as improving the metadata of the material that is still present. It is typically more difficult to classify and move files when migrating from one system to another because metadata fields don’t match up exactly, metadata was acquired in a different fashion, or sometimes no metadata was captured at all.
We use a variety of methods, such as optical character recognition (OCR) technology, to improve and enrich file metadata, allowing users to read and categorize document contents. This enables us to provide our clients with the most effective enterprise content management solutions possible.
3. Conceptual Proof
The proof of concept is the next stage of the data migration plan. To demonstrate that the migration will be successful throughout the full repository or data set, this phase is used to perform an initial test migration on a collection of sample data within test settings, as its name suggests. Field mapping between the present repository and the target repository is assisted by the knowledge gained during the discovery session.
Following configuration, ETL scripts are evaluated in test systems using a trial data set. In particular, these are the scripts for extracting, transforming, loading and validating data. During this stage, a performance benchmark can also be created. This benchmark can be used to estimate the length of time needed to carry out the production migration.
4. Test Migrations
The second phase of data migration involves running further test migrations that iterate through the ETL procedures of analysis, extraction, transformation, loading, and validation after proof of concept and the initial test migration have been finished.
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These extra test migrations are carried out to polish the migration until all mistakes and exceptions have been eliminated or accepted. Additional performance standards are noted, and any calibrations are put into practice or advised.
5. Migrated production.
The fifth and final data migration phase, the production migration, is initiated once all test migrations have been successfully completed and significant performance benchmarks have been obtained. This phase considers what was discovered throughout the discovery, data cleaning and enrichment, proof of concept, and test migration phases before presenting a final migration strategy.
This will also contain the amount of time needed for the transfer, which depends on a variety of elements, such as the migration strategy and technical infrastructure constraints like network bandwidth, server processing power, disc read/write performance, etc. The data migration will be carried out after all of this has been decided upon and agreed upon by pertinent stakeholders. Best practices for data validation should then be applied, along with agreed-upon reports, to guarantee the success of the migration. To confirm success, we always publish a content integrity report.
Businesses that want to prevent the potential calamity of a failed data migration must have a thorough migration plan that clearly describes the requirements of each of the data migration phases, regardless of the repository they are moving from or to.