Today, the majority of enterprises are engaged in ongoing system migrations – and most of those projects will either fail completely, overrun on time, or exceed their budget.
Too often, key failings during data migrations combine with insufficient testing and design. This leads to compliance risks and costly bugs, which are often only identified once it’s too late.
This series of articles will evaluate key causes for data migration failures, setting out a unified approach for data migration success. This first article opens by summarising the shocking rates of migration failures today. Part two will set out three key risk factors to mitigate during a successful data migration, before part three identifies four key reasons for data migration failures.
Part four will then outline a unified solution for solving data migration failings. The integrated approach will combine data analysis and archaeology, with automated test generation and requirements modelling. This provides the data, requirements and understanding needed for a successful data migration, while moving “shift left” testing far earlier in the migration project.
You can read the entirety of this article series in Curiosity’s latest eBook, How to avoid costly migration failures.
Too many migration projects today fail
Migration projects are lengthy, costly, and risky.
They are risky because they are lengthy and costly, and because of their unacceptably high failure rate. Two outdated figures are often quoted in articles today:
- Bloor Research’s 2007 Data Migration Customer Survey found that over 80% of data migration projects overran or were aborted .
- Bloor’s follow-up survey in 2011 found that 38.3% of overran or were aborted, with an average overrun cost of $268,000 .
These figures are still cited today because they are shocking, and because there is a lack of current research into overall migration project success. There is, however, uncited research referenced in a 2021 Forbes article, which states that 64% of data migrations overrun their forecast budget, with 54% overrunning on time .
Otherwise, for newer research, it seems you need to look at particular types of migration. This doesn’t make for much better reading, suggesting that the challenges of migrating systems are yet to be solved. Consider, for instance, ERP and Cloud migrations:
- In 2019, McKinsey found that ¾ of ERP project fail to stay on time or within budget, while a whopping 2/3 have a negative ROI .
- McKinsey research in 2021 similarly found that 75% of cloud migrations ran over budget, while 37% ran behind schedule .
- ERP migrations to the cloud paint a similar picture. According to 2019 research from the Cloud Security Alliance, the average ERP to Cloud migration takes 12 months, while 75% are delayed. Overall, 90% of CIOs have experienced failed or disrupted ERP to cloud migrations .
Far too many migration projects today fail or are challenged.
A necessary Challenge to solve?
Given their length, cost, and evident risk, why are most enterprises engaged in migration projects? Because they are necessary to modernisation, and to achieving the scalability, operational efficiency and capabilities offered by new technologies.
This is why, for instance, most enterprises aspire to spend 80% of their IT hosting budget on the cloud by 2024 , while cloud spending currently represents approximately 30% of overall IT budgets .
This next article in this series will consider a key reason for migration project failures: challenges during the data migration . It will identify three core risks during data migrations, which are often not mitigated successfully today. Parts three and four will then discuss criteria for data migration success, presenting a unified solution for enabling data migration success.
Read the entirety of these articles in Curiosity’s latest eBook, How to avoid costly migration failures.
 Philip Howard (Bloor Research: 2007), Data Migration Survey, Pp. 1-2. Cited in Philip Howard (Bloor Research: 2011), Data Migration Report. Retrieved from https://www.bloorresearch.com/research/data-migration-report-2011/ on August 4th 2022.
 Philip Howard (2011), Data Migration Report, P. 10.
 Steve McDowell (Forbes: 2021), “Overcoming the Challenges of Data Migration”. Retrieved from https://www.forbes.com/sites/moorinsights/2021/03/15/overcoming-the-challenges-of-data-migration/?sh=22794d8e427c on August 4th 2022.
 Didier Casanova, Swati Lohiya, Jerome Loufrani, Matteo Pacca, and Peter Peters (McKinsey Digital: 2019), “Agile in enterprise resource planning: A myth no more”. Retrieved from https://www.mckinsey.com/business-functions/mckinsey-digital/our-insights/agile-in-enterprise-resource-planning-a-myth-no-more on August 4th 2022.
 Tara Balakrishnan, Chandra Gnanasambandam, Leandro Santos, and Bhargs Srivathsan (McKinsey: 2021), “Cloud-migration opportunity: Business value grows, but missteps abound”. Retrieved from https://www.mckinsey.com/industries/technology-media-and-telecommunications/our-insights/cloud-migration-opportunity-business-value-grows-but-missteps-abound on August 4th 2022.
 Cloud Security Alliance (2019), Enterprise Resource Planning and Cloud Adoption, cited in Cloudreach (CIODive: 2021), “Why do cloud migrations fail?”. Retrieved from https://www.ciodive.com/spons/why-do-cloud-migrations-fail/600946/ on August 4th 2022.
 “Cloud-migration opportunity: Business value grows, but missteps abound”.
 Dave McCarthy (IDC), quoted in Paula Rooney (CIO: 2022), “CIOs contend with rising cloud costs”. Retrieved from https://www.cio.com/article/403231/cios-contend-with-rising-cloud-costs.html on August 4th 2022.
 See, for example, Velostrata and Dimensional Research (2017), cited in ChannelProNetwork. Retrieved from https://www.channelpronetwork.com/news/research-shows-nearly-half-all-cloud-migration-projects-over-budget-and-behind-schedule on August 4th 2022.
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