Major Data Migration Mistakes
As we know, data migration is something that has to be carefully planned. It is not a great idea to move your data into the cloud or any other single repository, until you outline all the requirements and examine how the data migration solution will work in practice. Here’s an overview of the major mistakes that companies make when performing data migration:
Considering data migration a solely IT project. If business owners, project managers, and partners don’t work out a business approach to data migration, your project is likely to fail.
Getting the easiest work done first. If you move all of your data or the parts that are easiest to move first, and decide later what objects and master types are really needed, the solution is not going to work.
Hiring developers with no experience in data migration to design your solution. Don’t think that all software developers know how to move the whole records across source and target systems. Not all of them do.
Performing data migration as a part of a bigger project. Data migration is quite a challenge by itself. Don’t think that it is done in a couple of hours.
Not thinking about data quality standards. If you don’t have time for outlining the data quality standards, then it’s better not to perform data migration at all. You don’t want to use inaccurate and outdated information, do you?
Lack of requirements. If you are not sure what problem you are solving with a data migration solution, you’d better save your time and money, until you know exactly what you need.
Buying the data migration tool first. Are you sure this tool is going to address your business needs just because somebody told you that it has worked fine for their organization? There is no data migration technology that fits every business need. You might want to take time to do some research.
If you follow this advice, you will probably find that the effort required to perform data migration is significantly greater than you thought. It really is, and not realizing this fact can lead to considerable time, money, and data losses.