Comment gérez-vous les demandes de données ou les demandes de parties externes, telles que les régulateurs, les clients ou les partenaires ?

I think what @Anthony mentioned is absolutely right, In different respective logging the work you have done on the confluence and connecting it to the Jira/Asana card, and more importantly pushing your code to the ad-hoc repository would ease the process for later times and it actually helps the regulators to make quick decisions in emergency times like natural hazards.

Sometimes you do not have to reinvent the wheel and another department may have asked for a similar request. You can tap your managers or review your own email to see if there has been a request like this in the past. It should provide some insight into these questions before asking. Before you ask any questions make sure they a know a little about you and get to know them on a personal level (if they are willing to share). It will make communication easier in the future.

In my experience at MicroMentor, we have segmented our data into: - internal data - external data. We have further divided the labor so that only the revenue and program management team touch the external data and internal data's data governance is controlled by the data analyst team. I have seen this play out other ways in the past, but for a small team of twenty-five employees this way works best for our purposes.

In expansive organizations, it's paramount to effectively disseminate information about the data governance process and the procedures for data requests. Teams throughout the organization may often grapple with uncertainty about where or how to initiate data requests. To address this, the processes need to be clear, comprehensive, and readily available. The roles and responsibilities, including which groups or individuals are authorized to approve and grant access to data, must be distinctly outlined. This proactive approach not only promotes transparency but also ensures a smooth, well-coordinated process for accessing and utilizing data within the organization.

Share anonymized data whenever possible. For example, let's say you are sharing customer survey data with an external party. Instead of sharing your actual customers' names, consider creating a unique customer ID/code name and share that instead. This will allow the external party to differentiate customers while also providing security.

Making use of any type of experience makes a great teacher, especially when moving in between companies to gain more diverse experience. By being exposed to different environment - whether data related or not - one can be more familiar with structuring data as well as improving one's own problem solving skills. When working at a new environment for the first time, I highly recommend working from basics. Then, incorporate different styles and ways to make data better. This adds different palettes and improves creativity, which becomes beneficial when dealing with more complex situations.

Develop a prioritization framework A framework will help you not only prioritize the work for your team’s pipeline but make the process fair and transparent to your stakeholders on why your team is working on an initiative over theirs for example. I use with success the following categories: - Business Goals (how many goals does this request support?) - Request drivers: regulatory, client meetings, pilot, improvement, etc) - Data availability and quality (we have vs need to acquire) - T-shirt size in effort - Complexity (we can do it ourselves vs we will need support from other teams) - Analytics product usage/frequency: ad-hoc, annual, daily, etc - Escalation (from Leadership teams or tie-breaker) Then assign score and weights.