What determines the complexity of a Data Migration?
If we haven’t made it clear yet, there is a LOT to consider during a Data Migration and getting it right shouldn’t be something you compromise on, no matter how complicated your data is. There are few things that might affect your migration:
Understanding your incoming data sources is key, as often data can be held in multiple locations. If it’s a case of migrating one data management software, to another target data management software then it might be simpler than, if you have 7 different siloed sources of data to migrate.
Data is often only one of the items that need to be migrated, as other items such as assets, images and even metadata are often overlooked. As the number of individual pieces of data, or objects, increase, the complexity also increases. Understanding the volume of data you’ll be migrating will be critical to understanding the effort and time that will be needed for your migration project.
Data Quality & Cleansing
If you have strong data quality rules in your current source systems, these can be replicated within your target system. However, if your data’s not good quality, work will need to be done to ensure data quality issues are identified, fixed, and reloaded in an efficient way.
Getting your data from A to B, will mean you need to map where each piece of data is moving from and to. This is the process of connecting a data field from one source to a data field in another source. There can be all types of mapping, including merging data, splitting data, removing unnecessary data – or just directing one data field to another.
Access to experts
The complexity of your data migration will be made easier by your access to data professionals who have the skills and ability to guide your new data model – whether these be your own internal employees, or an external data consultancy like Amplifi - you will need people who are able to sort your data correctly, and understand the complexity of your migration needs.
Migration to your target system
This final phase can be done with a Big Bang or a Phased Approach, pushing the transformed data to your new system. A single cutover is usually quickest but doesn’t leave much flexibility if there are any issues, whereas a phased approach gives you greater room to manoeuvre. Here you have to think about things that might make an impact, like the downstream impact of your migration on other systems, such as data that feeds into websites, or whether your people will all be trained to use the new system at the same time.