The Strategic Necessity of Migrating Legacy Systems

I'd like to start this article by focusing on why migration projects are essential, even when legacy systems seem to be doing their job and remain relatively stable over time. Why do we need to rebuild or migrate these systems to modern solutions? Is it truly cost-effective and beneficial for the organization or the initiative itself? The short answer is yes. Essentially, we must rebuild these systems because they are gradually becoming obsolete. Like everything else, they are designed with a finite lifespan.

I'm not suggesting that these systems were intentionally designed to fail by their creators, but rather that every solution has a natural lifespan. During the design and creation phase, it's impossible to foresee the full cost implications of the system over time. A system must have limitations, including a lifespan, target active users, and other factors. In this sense, every system will naturally reach the end of its useful life.

What can we do for these aging systems? First, we need to be aware of their lifespan or "shelf time." This is another topic that I had the pleasure of reading about in an article by my current CTO titled "Shelf Time in Software." I strongly recommend taking a look at that brilliant article as well.

As our systems age, we need to maintain, renew, rebuild, or replace them if necessary. But is it enough to simply build a new replacement for the systems we are currently using? What about the unique dataset, history, and value they have accumulated over time? These are assets that no company can afford to ignore, which brings us to the topic of migration.

What do we mean by migration?

Migration involves transferring the experience and data from old systems to new ones. But wait a second—they don't look alike! Of course, they don’t; if they did, why would you create the same system again, right?

First of all, over time, data structures have changed in modern systems due to evolving costs related to storage, memory, or even CPU usage. These cost changes have pushed software architects to adopt new approaches when designing new systems. A common example is the shift from relational databases to document-based databases, driven by cheaper storage costs and other technical metrics such as reading speed. However, cost is often the primary driver in these design preferences. Cloud solutions have significantly impacted our sector, especially for software designers. They are now less concerned about the infrastructure that will host their solutions.

All of these changes affect how we design new solutions or replacements for existing ones. Many of you may be familiar with the refactoring principle, which emphasizes keeping the input and output the same while changing (or refactoring) what is in between. I genuinely agree with these principles, but on the other hand, we have solutions that are far beyond their shelf life and are still running in production environments.