Top 5 Data Migration Challenges

Data migration challenges

Data migration is the most common way of moving data from one location to the next, from one format to another, or from one application to another.

For the most part, this is the consequence of presenting a new system or location for the data. The business driver is usually an application migration or combination in which legacy systems are replaced or expanded by new applications that share the same dataset.

Nowadays, data migrations are often started as firms move from on-premises infrastructure and applications to cloud-based storage to upgrade or transform their organization.

Even though data migration has been a reality of IT life for a long time, horror stories are still reported consistently. Not so fun fact- 38% of data migration projects fail.

So what are some of the pitfalls to avoiding a data migration horror story?

Here are 5 hurdles of data migration that organizations need to steer clear of. 

Lack of engagement from stakeholders and key executives

Given data migration is not a regular business activity, perhaps the most significant issue companies face is the absence of cooperation and contribution from key stakeholders in the project, leading to failure in data migration projects. Therefore, data migration procedures frequently stress collaborative efforts from all key stakeholders to guarantee that desired outcomes are obtained from the migration project.

Regardless of the size of the migration, there is somebody somewhere who cares about the data you’re moving. Find them and explain the need for this project and the impact on them before commencing the project. If you don’t, you’ll undoubtedly hear from them at some stage, and the chances are they probably won’t be happy.

Compromised quality of source data

Data quality issues stem from data accumulated over a long period. Factors such as various data structures for similar entities, incomplete or missing data sections, legacy data systems, and more, accentuate data quality issues, each time data is stored in an organization. Compromised data quality, repeated entries, and different designs, formats, and arrangements cause issues during data transfer.

This information gap includes not monitoring the issues that exist in organization data. It tends to be quite simple to get complacent and expect your data can easily be configured into the parameters of another system. However, the truth could mean critical failures regarding user acceptance. So, to ensure success, you need a good understanding of the source data.

Incomplete roadmap for the process

Data Migration Process involves different steps that should be planned strategically on priority. Teams working on data migration should know every stage involved, beginning to end. An inadequate or non-existent roadmap generally leads to disappointment. On the other hand, a word with experts in the field, alongside ideal use of state-of-the-art technology, can save your project, time, and efforts.

Data migrations typically include a disparate set of individuals using disparate technologies. A classic example is spreadsheets to archive data particulars, which are prone to human blunders and can’t be quickly interpreted while analyzing or transforming data. Using different technologies can lead to failure in the transfer of data and its plan between the analysis, development, testing, and execution stages. Things can get lost in the migration, resulting in increased costs, and wasted hours. Companies should hope to use a platform that successfully links the primary sources of info and results from every one of the stages to reduce error and save time and cash.

Underestimating the costs involved

Unfeasible estimations of scale and expenses of the data migration projects lead to overwhelm regarding both time and money if it doesn’t fail. Therefore, the scope and budget expected for the execution of the data migration strategy should be determined beforehand. Furthermore, as discussed before, even the slightest difficulties in the roadmap can affect expenses, time, and the requirement for involvement from the management. 

Inappropriate use of expertise

It’s a good idea to source specialists, and generally, this is applied to the administration and technical aspects of data migration. However, the experts on data, usually hidden in the business, don’t show up until late in the day. As a result, those with access to data often can’t interpret it, while those who can not obtain access to it, sometimes until the new system is ready.

Introducing data specialists into your migration projects right from the start will guarantee they sort out the different data sources and guide the data transformation to suit the users involved in the target system.

Although this is a straightforward task, there’s a great deal of complexity involved with moving data. Having an experienced professional with good references helps the process go smoothly.

A data migration project is a challenging and high risk; however, if every one of these obstacles is acknowledged during the planning stage and is overcome early on before data is transformed and transferred, you can be sure of success.

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