However, I am unmoved by your list of key wins (details below). If you indeed built something useful, is there a different way to deliver your message about the functionality that you enable?
Here are my reactions:
1) Shorten data discovery phase. In my experience, analysts and data scientists are always very familiar with what relevant data exists, or else they can find the right people to acquire what data they need. Often, kick-off meetings for new projects cover with stakeholders which data is useful.
2) Have transparency on how and by whom the data is used. For publicly available data, this is not something that a company usually cares about. Internal and proprietary data management is already a very mature space, and every company with such data already has processes in place to manage data access. I grant this is often a mess, but I also don't see any global solution on the horizon.
3) Foster data culture by continuous compliance and data quality monitoring. Data quality monitoring is extremely complex. I have seen many claims over many years of tools that solve this problem broadly, but I have yet to see any solution that matches the claims.
4) Accelerate data insights. This is a very bold claim for a new project, especially given the many (5+) decades of work and experience developing tools and techniques for data insights.
5) Know the sources of your dashboards and ad hoc reports. All dashboards I am aware of surface this sort of information.
6) Deprecate outdated objects responsibly by assessing and mitigating the risks. This is a good idea, but it is challenging in practice, as illustrated by several prominent examples [1,2,3].
Finally, unrelated to the above, your project's name (ODD) is very similar to the name used by the Outlier Detection DataSets (ODDS) project [4]
Good luck.
[1] http://www.lenna.org/editor.html
[2] https://scikit-learn.org/stable/modules/generated/sklearn.da...
[3] https://deepai.org/dataset/fb15k and https://paperswithcode.com/dataset/fb15k-237