You discover gaps in your data architecture framework. How will you address them effectively?

The practical step-by-step method to address these gaps are As-Is State Assessment: Evaluate the current data architecture to identify existing components, Data flows, and data subjects , entities and objects , according to requiered granulatory level. To-Be State Definition: Define the target state for the data architecture, aligning with business goals and technical requirements. Gap Identification: Identify discrepancies between the As-is and the To-Be, including missing components, inefficiencies, and areas for improvement. Business Impact: Evaluate how each gap affects business operations, decision-making, and overall performance. Technical Risk: Assess the technical risks associated with each gap

To address gaps in your data architecture framework effectively, start by conducting a comprehensive audit to identify and document the specific issues. Engage with key stakeholders to understand the impact of these gaps on business processes and objectives. Develop a prioritized action plan that addresses the most critical gaps first, focusing on aligning solutions with business needs. Implement corrective measures such as redesigning data models, enhancing data governance practices, or upgrading technology. Ensure ongoing monitoring and validation to confirm that the gaps are resolved and that the architecture meets evolving requirements. Regularly review and iterate on the framework to adapt to new challenges and opportunities.

Depends on what is the alleged gap and how it was discovered. DAF is a technical implementation of the business data set and flow and remediation varies whether there were flaws in the understanding of the business or in development skills.| It also could be that business evolved and it's not a gap per se, but the model outdated. All in all - a thorough review first.

Discovering gaps in our data architecture framework isn't ideal, but it's a reality we sometimes face. When it happens, I take a step back to fully understand the impact these gaps have on our current operations and future goals. Then, I work closely with my team and stakeholders to prioritize the most pressing issues. From there, we create a solid plan to address the gaps, which may involve bringing in additional resources or adjusting project timelines. Open communication and collaboration are key throughout this process, ensuring we resolve the issues effectively and keep things running smoothly.

Rolling out a data catalog alongside a data governance strategy will assist with tracking data changes and execution. To identify issues within a system post execution will be more streamlined if issues can be identified quickly and efficiently.