Data migration should be a strategic process. Yet, many organizations play fast and loose with their data migration strategy, overlooking the careful consideration and planning it requires. This lack of foresight triggers various costly consequences, such as overrun budgets, missed deadlines, and disrupted operations that ultimately affect not just the business but also its customers.
The recent studies shed light on just how widespread these issues are. 80% of data migration projects go over budgets and fail to deliver on time. Even more concerning, fewer than 70% are deemed successful. These numbers serve as a wake-up call, emphasizing the need for businesses to rethink their approach to the data migration process. Moreover, it also reveals how poor planning and lack of focus can instantly transform a critical process into a costly setback.
So why does data migration go wrong so often? Is it a lack of expertise, insufficient planning, or underestimating the scale of the task? In this blog, we’ll uncover the data migration challenges that cause migration failures and explore solutions that organizations can opt for to get over this critical process successfully. Additionally, we’ll provide a quick checklist to ensure a smooth and effortless data migration journey.
Learn more: Understand the concept of data migration, its importance, and its key types.
What causes data migration failure?
Data migration might sound like just moving data from point A to B but in reality, it’s a complex process that requires thorough planning and execution. Therefore, to avoid data migration failure, it is important to understand what can go wrong and why. Below are some of the major root causes that contribute to data migration failure, a strategic mitigation plan to address them.
Lack of proper strategy and planning
When it comes to migrating data, most organizations begin transitioning their data to an advanced data platform without understanding the “why” behind the data migration. For example, is the goal to consolidate systems, improve performance, or meet compliance requirements?
If the goals and objectives aren’t clear from the start, it’s easy for the whole process to get off track, with teams pulling in different directions. The data migration risks, such as unexpected downtime or data corruption, often go unnoticed in the early stages, and without a solid risk assessment, these issues can throw the entire migration off course.
Solution
To avoid failures stemming from inadequate planning and strategy, it’s important to approach migration with a clear and structured data migration plan, conduct a thorough risk assessment, and prioritize comprehensive data migration testing. This proactive approach will ensure a smooth migration without a hitch.
Poor data quality
When data quality isn’t up to par, the migration journey becomes much more challenging and prone to failure. Poor data – like incomplete, duplicate, or inconsistent data cause all sorts of issues, leading to errors that slow everything down. On top of that, dealing with outdated or unstructured data formats is challenging, as they often don’t map well to modern systems.
Another significant factor that causes data migration failure is data corruption when the files or databases get messed up, causing delays and threatening the integrity of your business data. Without high-quality, well-organized data, the data migration process becomes much riskier and puts your entire business operations at risk.
Solution
To avoid such failures, start with a detailed audit to identify and clean issues like duplicates, inconsistencies, and corrupted files. Use automated tools for data profiling and transformation and validate data through pilot testing before full migration.
Underestimating the data complexity
One of the biggest challenges in data migration is the underestimation of data complexity. Organizations often assume that transferring data from one system to another is a straightforward process. However, data migration is rarely simple – it involves multiple layers of complexity that, if overlooked, can lead to project delays, cost overruns, and even failure.
Hidden dependencies, poor data quality, and unstructured data challenges often lead to project delays, cost overruns, and operational disruptions.
On top of that, many data sets and applications are deeply interconnected, creating dependencies that aren’t always obvious. Overlooking these connections can cause integration issues, breaking workflows and disrupting operations.
Solution
To mitigate the risks of underestimating data migration complexity, businesses should thoroughly access data migration from legacy systems, map out how everything is connected, and run small test migrations first.
Quick read: Modern Data Platforms: Your guide to data-driven decisions.
