Migration Gone Wrong: The High Cost of Poor Data Quality

It is no secret that high-quality data is essential for any organization to reach its full potential. Without robust, reliable, and well-maintained information, it can be difficult to achieve desired outcomes and meet regulatory requirements. This is why it is so important for companies to carefully assess their data quality before embarking on a migration project to a new Enterprise Content Management (ECM) system.

One simple tip for saving time, resources, and costs during a technology rollout plan is to focus on data quality from the start. It is common for IT project teams and business leaders to assume that the chosen new ECM system will be able to overcome any challenges posed by the data. However, this is not always the case as with the saying "Garbage In, Garbage Out"; ignoring potential data quality issues can lead to delays and unexpected costs down the line.

Given the crucial role of data in digital transformation programs, it is surprising how often it is overlooked in the planning process. By prioritizing data quality and taking the time to improve it, organizations can ensure that their data migration initiatives go smoothly and deliver the desired results. This may require additional effort and resources upfront, but it will ultimately save time and money in the long run.

It is essential to challenge the assumption that data quality will not be a problem during an ECM migration project. This should be done well in advance of any platform implementation and before any deadlines have been agreed upon. Otherwise, there is a risk that the project will have to be stopped part way through when it becomes clear that the data sets are not up to par.

The Importance of Identifying Data Discrepancies

Even small discrepancies in data can cause major problems for organizations. Simple inconsistencies, such as using different abbreviations for the same term or misspelling a name, can result in duplicates appearing in a system. These issues can become even more complex when the project involves integrating multiple line of business applications, as there may be data fields that require content from other sources. When dealing with potentially millions of data points, the risk is significant.

Whether consolidating multiple systems into one new system or migrating to a newer ECM system, it is important to consider differences in formatting, data duplication, and variability in data quality. There may be dependencies and checkpoints between systems that need to be assessed in order to ensure the success of the content migration project. In addition, interdependencies between the data in different systems and links between content need to be considered. Organizations can avoid delays and unexpected costs during the consolidation process by taking the time to assess and address these issues.

To avoid these problems, it is essential for organizations to carefully assess the quality of their data before implementing any new technology. By identifying and addressing discrepancies early on, they can ensure that their project stays on track and delivers the desired results. This may require additional effort and resources upfront, but it will ultimately save time and money in the long run.

Don't Let Data Quality Derail Your Project

If data quality issues are not identified until a migration project is already underway, project teams must recalculate and recalibrate, which can be time-consuming and costly. This can extend the project timeline and require an additional budget that may not have been planned for. In some cases, addressing data quality issues can increase the overall project cost by 25% or more.

The delay in addressing data quality issues can impact the availability of key resources and cause delays for users waiting for new capabilities. In the end, the quality of the data is critical to the project's success, and without it, the new system cannot go live.

To avoid these problems, it is essential for organizations to carefully assess the quality of their data before beginning any system implementation project. By taking the time to identify and address data quality issues upfront, organizations can save time, resources, and costs in the long run. By calling in experts early on in the planning process and performing appropriate analyses, organizations can ensure that their projects are successful and deliver the desired results. This proactive approach to data quality can help organizations avoid project recalibration's high cost and frustration.