I’ve dealt with multiple data migrations over the years, moving from one system to another can be surprisingly convoluted, even when you think the process should be straightforward.
One recurring challenge is the temptation to migrate only the “current” or “relevant” data into the new system while stashing historical data somewhere else, like an archive or backup database. At first, this seems like an easy solution.
You move just enough data to get the new system running, and you tell everyone, “If you need to analyze or report on the old data, we have it stored over there.”
But sooner or later, someone needs that historical data for analytics, reporting, or to merge with new information. Because it’s sitting in a separate place, you end up with a small, “cottage industry” of sorts, people who specialise in pulling and analysing the legacy data.
This cottage industry might start small, but over time, new demands arise:
More Reports: Someone wants to combine current and historical data for deeper insights.
Third-Party Integrations: The old data needs to connect with external systems or tools for advanced analytics.
Ongoing Maintenance: The “temporary” archive starts receiving updates, patches, or new data sources.
Before you know it, this cottage database becomes a permanent fixture, growing more complex with each update. It morphs into its own mini data lake, complete with specialised scripts and workflows.
As soon as you try to integrate it back into the main platform or move to yet another new system, you face a new migration project, often bigger and messier than the first one.
This all costs you extra money and extra issues such as:
Hidden Complexity: By storing historical data separately, you create a silo that quickly becomes complicated to manage.
Inconsistent Reporting: Different teams might use different data sources, leading to confusion and inconsistent insights.
Maintaining multiple databases (and the specialised knowledge around them) can be expensive in both time and money.
Lessons Learned
Plan for the Long Haul: Even if historical data seems irrelevant now, plan for a future where you might need to integrate it easily.
Evaluate All Options: Sometimes, a phased or partial migration can work, but do so with a clear strategy for eventually merging the old data.
Streamline Reporting: Encourage a single source of truth to avoid duplicate effort and maintain consistent reporting across the organisation.
Don’t Underestimate Governance: Good data governance from the start saves countless hours and headaches later.
Round up
A partial data migration might seem like a quick fix, but it often creates a “cottage database” problem that can spiral out of control.
By anticipating the need for historical data, and planning an approach that brings everything into one coherent system, you’ll save yourself (and your team) a lot of frustration down the road. Data migrations are rarely simple, but with the right foresight, you can avoid building a hodgepodge of separate repositories that become a long-term drag on your organisation.