Author: Chris Bolus, Data Migration Team, Silverbear
If you’re embarking on a digital transformation journey and weighing up which membership management software to use, then you will probably be only too aware that the process will require some form of data migration.
Data migrations can be tricky projects. According to Gartner, research reveals that more than 50% of data migration projects exceed budgets and planned timelines. In some cases, they actually harm business operations due to flawed strategy and execution. This is why getting it right needs to be top of your priority list. And, in getting it right, you should reap the rewards of moving your data from an outdated legacy system to a new modernised platform, unifying disparate data, and saving time and money in the future.
So why is it so difficult? Well, there are many challenges with data migrations and a few common problems including inadequate business engagement with project management, lack of internal technical skills to deliver processes, and an incomplete understanding of existing data. All of this can lead to errors that generate higher, unexpected costs.
With all this in mind, we thought it would be useful to share our top five tips for executing a successful data migration to help you plan to take the leap:
- Plan, plan, plan.
Data migration may seem straightforward in principle but it is often one of the most complex and technically risky aspects of an IT project, so careful preparation is essential. As the saying goes, failing to plan is planning to fail. At Silverbear we understand this only too well, which is why we start data migration planning right at the beginning of our projects during the discovery phase. The Silverbear data migration Sprint 0 focuses on a deep dive into your existing system, to establish a full scope of the work required to build a complete mapping to Dynamics for all areas, and prevent any unexpected surprises.
- Don’t confuse data cleansing with data migration
Don’t try to undertake data cleansing at the same time as your data migration as this will increase risk, complexity, and time to deliver. Including data cleansing in the migration can also make data testing harder, as it adds an extra layer of movement to the data, making it harder for the test team to compare the data in your current system(s) to the target system. Either cleanse before the project or do it post-migration using your new system, which should benefit from modern data cleansing tools.
- Try to understand your data
Before migrating data, you must know (and understand) what you're migrating, as well as how it fits into the target system and your business processes. Try to get an idea of what data you need to migrate, who needs to use it, and what purpose it will serve when it’s migrated. Consider things like retention periods, archiving policies, and how you manage data protection for your members and stakeholders in general. Data is an extremely very valuable asset - many people say we live in the “age of data” - but it needs careful stewarding.
- Plan your system dependencies and cut over
The go-live is the crunch point, where your current system(s) stop being used, and your new system is in place for your users to continue day-to-day operations. The timing for go-live needs to be considered around key internal processes such as membership renewal run, and Direct Debit processing, to ensure a date is identified that results in minimal disruption. While a cutover period is required, where both systems are out of action simultaneously (else you risk losing data in between), Silverbear uses a delta load process to minimise down-time, by identifying and migrating only the changes from the previous data cut, as opposed to the full data set. So, if 2 weeks of data was loaded prior to go-live, only those 14 days’ worth of changes in the go-live cutover are required, thereby reducing the cutover time significantly.
- There’s no I in team (or in data)
Make sure you've got a strong team in place covering all aspects of your implementation, including technical know-how. Firstly, elect an efficient project management team and understand who is responsible for your data (the IT, DBAs, SMEs, and/or data champions). In addition, it is imperative to have an experienced data testing team to validate the data between the current and target systems. And, finally, make sure to choose a reputable, experienced provider you can work with. With the right team in place, success will be much easier to achieve.