Software Project Failure Disputes Litigation Frequently Due To Failed or Delayed Data Migration 

Avoiding Delays in Data Migration Projects

Organizations often must transfer legacy business data when replacing their current software with a new system. This process, known as “data migration,” is a major source of delays in software implementations. ¹

Through analyzing hundreds of software-related lawsuits, DisputeSoft’s software experts have identified several common themes in troubled data migration efforts, which are a substantial cause of software project implementation failure disputes and litigation. This article outlines typical issues in data migration projects and suggests strategies for mitigating them.

Overview of Data Migration

At its core, data migration involves extracting data from a legacy system, transforming the data, and loading it into a new system. ² The data extraction phase includes creating a backup of the legacy data, determining the extraction order, and writing and running extraction queries. ³

Data transformation entails converting data formats and organizing the extracted records into a suitable format. ⁴ The load phase involves transferring data into the target system, often by using an automated loading tool. ⁵

The migrated data is usually validated after the load process completes. ⁶ Data validation can be conducted manually, by visually inspecting the legacy and target systems and noting data discrepancies, or using automated comparison software. ⁷

Technical staff will typically investigate and fix any data faults. ⁸ New computer systems are rarely released into production until all steps of the data migration process are completed. ⁹

Migration Risks, Delays, and Failures

Many things can go wrong during the migration process. Indeed, at least 40% of data migration projects experience delays, cost overruns, or outright failure. ¹⁰

DisputeSoft’s experts have investigated data migration issues delays and failures in multiple numerous software projects failure-related disputes. By analyzing these projects, we have identified several common factors in unsuccessful data migrations.

One recurring problem is failing to anticipate gaps in legacy data quality. Legacy systems often contain inaccurate, incomplete, and redundant information, which may render it unsuitable in the target system. ¹¹

Addressing these data gaps can be costly and time consuming. ¹² Moreover, customers and vendors may disagree regarding the level of data quality required for the target system to be released into production, increasing the risk of project delays. ¹³

Underestimating data migration projects is another, related risk. Ignorance of the effort needed to locate, understand, and transform legacy system data contributes to understaffing, underfunding, and allocating insufficient time for data migration. ¹⁴

Failing to conduct adequate sampling of legacy data can lessen the quality of data transformation rules, thus contributing to underestimation and delays. ¹⁵ These risks can be mitigated through proper planning and project scoping. ¹⁶

Finally, the failure to provide enough skilled staff is a major contributor to data migration delays. ¹⁷ Data migration benefits from specialized tools and skillsets, the absence of which can pose unexpected technical challenges.

Success is also much more likely when senior and executive customer stakeholders are actively involved in the process. ¹⁸ Strong customer engagement provides critical background information on legacy data and reduces the likelihood of project scope disputes.

Conclusion

Despite the importance of data migration, errors and delays are all too common and often result in software projects failure disputes and litigation. Our team of experts has analyzed data migration issues in countless software failure disputes.

In our experience, the principal oversights main challenges are underestimating the scope and impact of data migration projects, neglecting legacy data gaps, and having insufficient or insufficiently trained staff. Organizations can mitigate these risks by giving data migration the attention and organizational visibility they deserve, carefully planning data migration efforts, and deploying and leveraging staff with adequate knowledge, training, and experience in successful data migration.

About DisputeSoft

DisputeSoft has served as trusted software failure experts on over 425 software project failure and IT failure disputes both at the pre-litigation and actual litigation stages. DisputeSoft’s proven software experts and trade secret experts deliver the most educated independent, objective and defensible analysis, opinions and testimony in any complex software disputes, software copyright infringement cases or intellectual property disputes. 

Contact us for free consultation to see how we can help enlighten and improve your case. 

 

 

Footnotes

1. David A. Gordon, The Data Conversion Cycle 8 (2017).
   David A. Gordon, The Data Conversion Cycle 8 (2017). ↩︎
2. Johny Morris, Practical Data Migration 203-05 (3rd ed. 2020). ↩︎
3. David A. Gordon, The Data Conversion Cycle 38-46. ↩︎
4. Id. at 43. ↩︎
5. Id. at 46. ↩︎
6. Id. at 21. ↩︎
7. Id. at 50-51. ↩︎
8. Johny Morris, Practical Data Migration 218-25 (3rd ed. 2020). ↩︎
9. David A. Gordon, The Data Conversion Cycle 44 (2017). ↩︎
10. Johny Morris, Practical Data Migration 10 (3rd ed. 2020). ↩︎
11. Oracle®, Successful Data Migration: An Oracle White Paper 2 (Oct. 2011), https://www.oracle.com/docs/tech/middleware/successful-data-migration-technical-brief.pdf (last visited Oct. 2, 2023). ↩︎
12. Id. at 6. ↩︎
13. Johny Morris, Practical Data Migration 22 (3rd ed. 2020). ↩︎
14. David A. Gordon, The Data Conversion Cycle 8 (2017). ↩︎
15. Oracle®, Successful Data Migration: An Oracle White Paper 6-7 (Oct. 2011), https://www.oracle.com/docs/tech/middleware/successful-data-migration-technical-brief.pdf (last visited Oct. 2, 2023). ↩︎
16. David A. Gordon, The Data Conversion Cycle 23 (2017). ↩︎
17. Patrick Allaire, Justin Augat, Joe Jose, and David Merrill, Reducing Costs and Risks for Data Migrations ii (Feb. 2010), https://www.hitachivantara.com/go/cost-efficiency/pdf/white-paper-reducing-costs-and-risks-for-data-migrations.pdf (last visited Oct. 4, 2023). ↩︎
18. Johny Morris, Practical Data Migration 119-22 (3rd ed. 2020). ↩︎