Data migration can be a painful process that involves multiple steps in the Extract-Transform-Load (ETL) process. The challenges with migration can be even higher when we are talking about big data. This is especially true when we are migrating different types of structured and unstructured data within the same system. In this article, we will take a look at some of the most common mistakes that can cause delays or worse, failure of the big data migration project.
Ignoring The Governance Structure
Big data migration processes can be quite overwhelming and organizations often spend most of the time firefighting infrastructure and load related issues. In the process, businesses often miss out on more critical aspects of migration like identifying the governance structure of the data. Understanding the ownership of data and who has permissions to access, create, edit or delete data is important to ensure that the data owners are in the loop of the process. This can be an issue from a legal standpoint as well, depending on the industry you operate in.
Not Cleansing Data
Merging or migrating your big data to a new system can be a good opportunity to cleanse the content and remove any legacy structures that your data is stuck with. Not cleansing your data would mean that you might be stuck with the inefficiencies that plagued your old system even after migration. But while you are at it, do remember to cleanse the data before the start of the big data migration process and not during the process itself.
Not Testing Loads
Big data migration projects rarely ever happen without issues. For this reason alone, it is not recommended to plan a migration in one go. The IT team that is responsible for the migration project needs to treat the migration project like any other project and needs to establish rigorous procedures with industry best-practices and also execute iterative levels of load testing during the migration process.
Not Leaving It To The Experts
Look through the steps for big data migration and it may seem like something you could just execute with your own in-house IT team. That can be a big mistake especially if you handle critical or vulnerable data in your database. It is always a good idea to contract the job to a third party service that specializes in such projects. While this may seem like a pricier option at the outset, it also needs to be noted that businesses risk time and budget overruns by using a team that is inexperienced to handle the challenges of big data migration.
Assuming Migration Is An IT-Only Project
The IT team may be responsible for all that happens with the data, but the ultimate ownership may lie with the business users. It is important to acknowledge their needs and requirements while charting out the migration process. In essence, the business end users of the data need to be kept in the loop and their necessities kept in mind while working through the different steps of migration like merging data, cleansing them or restructuring them.
