Data migrations are, by their very nature, high-risk initiatives. If not planned effectively, they can create a significant headache for both the IT team and the organisation. That’s because modern technology transfers are massively complex undertakings. However, data migration problems can often be traced back to confusion and disorganisation surrounding the migration plan (if one is in place) and a failure to prepare for the move adequately.
Additionally, every company’s data landscape is unique. It may encompass everything from legacy systems to homegrown one-off databases, each with its own level of support. Documentation may be non-existent, institutional knowledge may be limited, and key staff may have left. It makes the task much more complex to undertake.
Other issues IT teams fear, or challenges they come up against, are data loss, compatibility issues and hardware challenges. According to analyst firm Gartner, 83% of data migrations either fail outright or exceed their allotted budgets and implementation schedules.
Adopting an alternative approach
But do data migrations need to adopt such a ‘Big-Bang’ approach? Does all the data need to be transferred at once, especially if there is so much inherent risk with migrations? Could a lower risk, iterative and agile alternative be adopted?
There are some key points to consider when moving data. For example, does it make sense to migrate all legacy data? Has the organisation considered what data will be reused? Likewise, what changes to data are required to accomplish the migration?
Here at Clearvision, we partner with Atlassian. We implement and supporting applications like Jira, Jira Service Management, and Confluence. Therefore, when I talk about data migrations, I do so with an Atlassian focus.
Understanding what data you need to transfer
The quality of the data that an organisation has within Atlassian applications significantly influences the risk and effort associated with migrating to another platform. Complex applications with history and bespoke hidden scripting can be a minefield. The IT team can mitigate this risk by considering how much history they will transfer to the new system. They require a thorough understanding of what data they will transfer from the source to the target system. With that, the negative impact of inaccurate, inconsistent and irrelevant data is amplified.
It is important to ensure that data that populates the new system is fit for purpose and delivers quantifiable improvements on the previous system.
Start afresh with the minimum viable data. It affords the opportunity to focus on data and workflows that offer the most business value. These will deliver efficiency and productivity improvements.
Only move the valuable data
The big bang data migration approach is about moving the entire dataset from the legacy to the target system. This is typically carried out over a weekend. To mitigate as much risk as possible, there are often several test migrations conducted. These increase costs and take significant effort to complete. For example, a Jira migration can take up to 30 days of effort. When considering the risk, cost and effort involved, a more agile, iterative and pragmatic approach makes more sense.
An iterative data migration means that you only move the valuable data, and this is all managed in smaller increments. However, this presents two key challenges: how do you keep both target and source systems data operable until the migration is complete? How do you coordinate the migration of distinct elements of the business users and functionality without breaking overall business continuity?
Taking a step-by-step approach
For an iterative data migration to succeed, the two systems need to run in tandem for the transition period without impacting each other. Therefore, IT teams should move business units or departments one by one. Start with new teams and projects on the new system and decommissioning old data on the legacy system.
Using an iterative strategy and migrating just inflight or minimum data allows effort, which would otherwise be spent on migrating a ‘Big Bang’ approach, to deliver tangible business value. Our experience is that 70 to 80% of the data that has not been migrated can be archived or decommissioned.
We have managed Atlassian migrations between platforms such as Server to Atlassian Cloud for many years. We know first-hand that there can be extended downtime and significant complexity and effort associated with migrating large volumes of data. There is also time and cost required to clean up unwanted legacy data. The iterative migration process delivers significant benefits at a lower cost and significantly lowers the impact and risk to the business.
If you are interested in understanding more about iterative Atlassian migrations, why not download our guide: “Avoiding the Big-Bang.”
As an Enterprise-Certified Atlassian Platinum Solution Partner, Clearvision is well placed to provide large enterprises and the public sector with consultancy, technical implementation, hosting, training, licensing and support services for the Atlassian stack, as well as vetted contractors via its sister company, ClearHub. Clearvision has also established partnerships with a number of leading technology vendors that bring added value to its customer base, namely AWS, Checkmarx and Mendix, amongst others.
