Data migration is a key procedure to drive business performance and gain a competitive advantage. Whether carried out in isolation or as part of a project, giving it the attention it deserves will be decisive for its successful completion and, with that goal in mind, data quality is a priority issue.
Planning is one of the essential requirements at the time of implement a data migration project Y, inside of her, data quality is essential that we should work on different aspects.
Let's keep in mind that When data migration is done, while benefiting from greater agility, security and maximum updating of information, its quality improves. And it does it exactly thanks to the implementation of data quality initiatives throughout the migration procedure, focused on the detection and correction of:
- Incorrect information
- Deviations
- Duplicities
- Empty fields
- Inconsistencies
The quality of the data, key in the migration project
In the processes of extraction, data transformation and loading (ETL) data is cleaned and adapted to business rules, so that when it is concluded its quality improves. A goal that is met after taking a series of steps, whose beginning also marks the beginning of a migration project.
The data quality stage is essential to prepare O, if you like, Take the first step in a data migration, since it allows us to know what we are facing, essential mission before carrying out the migration itself.
The data discovery It is prior to analysis, and thanks to him we can know two things:
1. What data is available.
2. What is its quality.
In summary, we discover what data is relevant to the migration and then carry out a data analysis that begins with the accreditation of business rules. The next step will be to outline them.
With this analysis of the data of the systems we can have a greater knowledge of the data model to be treated, understand its structure, contents, quality and dependencies. Or what is the same we can know where problems may or may arise to build an ad hoc design.
For his part, integral profiling and auditing of all data sources can avoid unpleasant unforeseen events during migration. It is one of the reasons why it is essential to do it and it is also recommended to complete it ahead of time.
Specific, The data quality stage should respond to the phases of cleaning, homogenization and enrichment, with what to finish the data will be consistent, complete and reliable. Its realization from the beginning will avoid a lot of trouble later in the data migration procedure.
After performing the improvement of data quality in source systems proceed to your transformation according to business rules. Once converted and validated, we will proceed to load them. These are successive steps that only when carried out correctly allow you to proceed with guarantees.
Data quality is an essential task that must be addressed before and throughout the migration procedure using modern data quality solutions.. It could be said, in summary, that a successful migration is highly dependent on quality and its attributes.
At the same time, make a planning that takes data quality into account as a priority will help us follow a proven methodology, as well as to have data integration technologies and the support of consultants and key users with knowledge of the business. Instead of manual programming, it will be much more advantageous to opt for flexible solutions, intuitive and highly scalable, capable of offering a connection to different data sources.
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