How can you handle data migration and integration projects with tight deadlines?

Engagement of business data owners is critical on this stage. Without their input the source data assessment is impossible. Business users will explain what exactly is available in the system and how they use that data. In many cases they will mention some other sources like files where they hold the important details. They can explain how many workarounds they make to get proper results in reporting. All that knowledge will make the analysis phase valuable.

At this step, I would say asking the right questions is paramount to getting the best scope and complexity estimations. Starting with why the business needs this?, what's the impact? how is going to improve revenue/sales/performance? Once you get all of this out of the way, determing the technical needs and estimating costs, complexity, and scope becomes an easy task.

Clearly delineate the project's objectives, outcomes, assignments, and timelines while identifying the factors impacting its complexity. This includes specifying goals, what will be delivered, tasks to be completed, and associated deadlines. Additionally, it involves recognizing factors like stakeholder count, technical challenges, coordination needs, and potential risks and uncertainties that contribute to the project's intricacy.

The two most important questions to keep in mind when choosing the right tools and methods are: 1. What's the easiest, fastest, and best way to accomplish the business need? (consider factors like tech learning curve, develop time, runtime, cost-effectiveness, and complexity) 2. How can it be non-dependant from me? (Make it scalable, maintainable, self-explanatory, well-documented, etc.) Many people think that doing very complex projects is better, and it's not; as long as you meet the requirements, you should always try to go through the simplest route possible.

Testing is something many people forget or take as granted but it's very important that you isolate the possibility of something breaking by using testing environments and samples that you can rely on before going with the actual data.

In data engineering, monitoring is key to enable a successful data orchestration. We can't always be on duty watching the pipelines so automation is a must in having a complete data infrastructure. It also helps with visibility with stakeholders and end users.

This set of steps illustrates the key flaw in data migration projects that I highlighted in the first edition of Practical Data Migration in 2006. Data migration is a business not a technical issue. In the project flow above, stakeholders feedback on the results of the project when it's all but over, the budget is spent and sponsors want closure. What happens when solicitation results in rejection? Unless it's a regulatory stakeholder, the runaway train of the project overides objections. The enterprise spends the next 2 years fixing broken processes. The problem is epistemological. The business knows what's right. Technology can show things are wrong it can't know they are right. To bridge the gap, get a copy of Practical Data Migration

I am with Johny Morris on this. To add to it, this list has not checked what type of migration this term "Data migration" covers. It is not all about pipelines and ETL. The business might be doing a full on application migration and moving the transactional data too. There may be massive organisational, process as well as system transformation required. The migration of the DWH will be the simplest aspect, especially if moving from a very archaic legacy system. If we do a waterfall method and validate with the business at the end, we are quite simply, planning to fail.

Most importantly, keep a lot of time for dealing with unexpected issues and optimization. Optimization is a lengthy process and eats up time which was never even part of the agreed timeline. So it always wise to set aside time for unexpected things such as data issues, optimization issues and orchestration issues.