To align with your manager on technology integration speed, start by setting shared OKRs to focus on impact over speed. Use data-driven insights and SWOT analysis to present the risks of a rushed approach. Propose an Agile framework, suggesting an MVP to deliver core functions quickly while refining quality over iterations. Employ ISO 31000 for risk management, and use ITIL’s Change Enablement to implement controlled changes, balancing speed with stability. Monitor progress through ITIL CSI and KPIs, fostering iterative feedback to satisfy both speed and quality priorities. This structured approach helps find common ground effectively.

Ensuring data integrity when juggling multiple systems and third-party APIs is like keeping spinning plates balanced—communication and validation are everything. One trick I’ve used is setting up a centralized validation layer, where all data is checked before it’s written to the database. In one project, we had APIs throwing inconsistent data formats, so we implemented schema validation at the point of entry. Also, leveraging event-driven architectures helps, ensuring that updates are tracked and propagated in real-time. And always have a rollback plan! No matter how airtight you think it is, having a failsafe is essential for data consistency.

Implement Change Data Capture and elaborate auditing. Implement Data reconciliation. Ensure that data isn't inconsistent incomplete or corrupted.

I believe that integration testing is crucial (speaking from experience here). The must-have tests should test incorrect responses from the third-party API, including malformed data returned, inability to reach the API, and different errors returned from the API. In a sense, data integrity and consistency are easier to ensure between the system modules but are most critical at the system border. If one of your systems returns incorrect data, it is a bug you can fix, but if an error comes from the third-party API, graceful failure is the only option.

# Data Validation -Input Validation: Ensure accuracy, completeness. -Schema Validation: Enforce structures, types. # Transaction Management -Atomic Transactions: Ensure parts succeed before committing. -Two-Phase Commit: Ensure systems agree. # Consistency -Locks: Prevent conflicting updates. -Eventual Consistency: Data aligns over time. # Synchronization -Idempotent APIs: Repeated calls are stable. -CDC: Real-time changes capture. # Monitoring and Auditing -Monitoring: Track, detect issues. -Logs: Detailed transactions. # Error Handling -Catch Exceptions: Robust handling. -Retries: Manage transient failures. # Backup and Recovery -Backups: Recover from loss. -Reconciliation: Correct inconsistencies.

3rd party systems and APIs fill gaps within a our domain. Two reasons why we rely on them: 1. These are undifferentiated capabilities which we don't want to invest in and just consume them from a third party. 2. We want a faster time to market by using them. But they may not fully align with our internal domain model. Hence we need to build domain driven adapters that primarily focus on: 1. Validation: Ensuring that the data expected is complete, consistent and correct 2. Transformation: Adapting the data to our internal domain model 3. Enrichment: Injecting data necessary to further align incoming data with the internal domain model. Each of these stages should trigger alerts upon failure prompting intervention and remediation.