In a data migration project, unexpected delays can arise due to various factors, such as data quality issues, technical challenges, or resource constraints. To mitigate these delays, Qlik & Talend can be used to: Talend: Automate the ETL process, reducing manual intervention & potential errors. Talend's data quality checks & validation rules can help identify and resolve issues early on. Qlik: Provide real-time insights into the migration process, allowing for timely decision-making and problem-solving. Qlik's data profiling capabilities can help identify data quality issues and potential bottlenecks. By leveraging the automation and insights provided by these tools, organizations can accelerate the migration process and reduce the delays.

It is most important to find the root cause of the Delay. Go through project reports, logs, and tools to see where things started to slip and ask team members what’s slowing them down, uncover hidden blockers. Otherwise, look at common issues: • Dirty or inconsistent data in the source system. • Underestimated workload or data complexity. • Integration problems between systems. • Migration tools not working as expected. • Lack of people, expertise, or infrastructure. After determined the problem, adjust the plan accordingly to ensure quality

Prioritize quality by focusing on the most critical tasks first. Ensure the data is clean, accurate, and validated before moving it. Use automated tools to speed up checks without cutting corners. Communicate with your team and stakeholders about the delays and adjust timelines if necessary. Test the migrated data thoroughly in small phases to catch errors early. By staying organized and prioritizing accuracy over speed, you can ensure the project meets high-quality standards despite the delays.

- Implement automated data quality checks at following stages on data migration steps so that it has minimum impact in case of delay: * When data is loaded into staging area *When data is cleansed and transformed to be loaded into target system *do reconciliation after data is loaded into target system - clearly define RACI for data quality check execution and correction - Run multiple mock load cycle in lower environment to understand the challenges and identify the resolution

Delays in a data migration is to be positively expected and anticipated. This mindset changes our perspective to incorporate delay in all stages of the project starting from design all to way to implementation in production. One way to plan and deal with this is to learn from other migration projects and plan accordingly. Another flip side to also consider is even after incorporating all of the steps above we might get an unexpected and unanticipated issue from any facets of environmental, coding, design, testing, validation, implementation in production. Research, design, develop, test, implement with delay always included at various stages and always to be looked out and be prepared for.

Data Catalogs and Data Jobs Repository are essentials before any data migration project some times if you are migrating data to a side enricher you may find an orphan job which is missed after completing the jobs migration. This case happens and delays the main project timeline and project completion

Assuming that the delay is from operational issues (access, unclear requirements etc.), one might plan to invest the idle time towards: - 1. Ensuring there is an agreed framework and team understands it well 2. Setting up automation and developing reusable components wherever possible 3. Most importantly, identifying the impact on timeline and communicating it to all the stakeholders as early as possible and getting the sign-off on the same. This will give the development team due time to delivery quality results 4. Additionally, explore the possibility of using of any accelerator If the delay is because of technical issues (lack of skills, poor planning), there is very to do. Document your learnings from this experience instead.

Facing unexpected delays in a data migration project is undoubtedly challenging, but ensuring quality must remain the top priority. Rushing to meet deadlines can lead to errors that compromise data integrity, security, and system performance, ultimately costing more time and resources in the long run. A structured approach to addressing delays—reassessing timelines, improving communication among stakeholders, and implementing quality checks at each stage—can mitigate risks. Remember, the goal is not just to complete the migration, but to do so with a solid, reliable outcome that supports long-term business objectives. Patience and precision are key to a successful data migration.

To ensure quality during delays in a data migration project, prioritize tasks, focus on critical data integrity checks, communicate delays transparently, and allocate time for thorough testing and validation.

In such situations, I recommend to follow the "STRICT-MI" Framework (Strategic Timelines, Risk-Informed Implementation, and Critical Task Management Integration) This acronym encompasses the key components: - Strategic Timelines (Reevaluate and Realign) - Transparent Communication - Risk Management and Logging - Identification of Bottlenecks - Critical Data Prioritization - Tool Investment - Mitigation Strategies - Integration of Solutions