Poor Quality Data is the Achilles Heel of Migration Projects
Five Common Data Readiness Missteps
We are not talking about night and day differences, but we are talking about dissimilarities which, if not accounted for, will wreck a data migration effort and the larger project it is supporting. When it comes to delivering a data-set that is fully complete and ready to migrate into a new application, data readiness experts understand that there are significant best-practices that differentiate application code development from data readiness. The following differences should be considered during the initial planning phases and throughout the typical data readiness project:
- Because data readiness and migration are usually part of broader transformation projects with far reaching implications, the data team, the business and the technology team must work together hand-in-glove. For example, an SAP apps team that is responsible for a new S/4HANA instance must rely on the data team to handle critical configuration variances as well as phasing data into the live application. Data readiness plays a major role in risk mitigation.
- At the start of a project, the team for data readiness should include all stakeholders who have an interest in the outcome, not just IT professionals. The full team usually includes: business sponsors, operational data experts, data architects, the team lead and developers, and a dedicated team to test the data quality.
- Because quality of data matters, a few key questions underlying all data migrations are: what data should be pulled forward into the new application, and what data should not be pulled forward? How does the data need to be cleansed? How does data need be consolidated? How does the data need to be transformed to meet the requirements of the to-be business processes and the new system (which include all stakeholders’ requirements)? Mapping data based on fieldnames in the DDL or copybooks is insufficient. A competent data migration team must examine the data in both the source and target applications to determine the rules and relationships.
- With any data migration project there will be ‘leftovers.’ The teams must identify and consider what to do with information no longer needed by the business and the new application.
Planning a Data Readiness Project?
If you are planning a data readiness project to:
- Get value out of change.
- help your organization operate more efficiently.
- Conduct your business processes efficiently and accurately.
- or any of a thousand other business initiatives that rely on data
…then you should play close attention to the following five points. Below are common mistakes, frequently made, that are guaranteed to adversely affect or even destroy a data migration project:
1. Undervaluing the need to have business users and data steward working together
2. Failure to follow best practices for data readiness
3. Not understanding the source data or target application data requirements
4. Inadequate or incomplete strategy for moving, accessing, consolidating, validating and auditing data
5. Not having the appropriate tools and processes to support the data migration
For more information on the hazards of data migration as well as guidance on ways to circumvent the pitfalls, please visit the Method360 Data Readiness website or call our office at 877-310-5335.
For in depth information about data readiness and migration, you can order from Amazon: Data Services: The Comprehensive Guide, published in 2015. The book was written by Method360’s Co-Founder and Vice President, James Hanck.