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From a program manager perspective, I would refer to the domains of PgM performance:
1. benefits management: is there a business case, which benefits are expected and how will they be measured
2. strategy alignment: any strategy is subject to change, driven from external but also internally from your program, as you identify reality
3. governance: how is your program embedded in the organizational decisions, where does your authority come from, how do you influence the projects within your program
4. stakeholder engagement: besides internal communication how do you deal with external stakeholders, build alliances, sell your program, control politics
5. there is much to say about your program lifecycle: do you have a roadmap, a set of projects, a cost timeline etc
These are all important questions to have answered and I have already asked several of them. In this case, there doesn't seem to be an enterprise strategy confirmed (yet) and data needs have been addressed in an ad-hoc and siloed manner within each product BU. I have more to say on that, but I'll leave it there for now.
I will certainly address all of the topics you shared above with my management team and in the meantime would love to hear some ideas on learning paths that could help me ramp up in the short-term and also as this role develops long-term. Of course, having a Master's in Data Science would be ideal, but something that might provide some tactical guidance would be extremely helpful to get me started. In the meantime, you've inspired me to take a look at the Standard for Program Management. :)
I think @Thomas as detailed pretty well most of the actions that you should follow, nevertheless I'd like to add that if you have to do this you have to develop a Information Security Governance if not exist in your company or if exists work in collaboration with the Information Security Manager, normally all data most comply with external regulations and internal policies and must exist access levels to the data.
There are a lot of information how to develop Information Security Governance plans normally the basic steps are the following:
1 - Formulate a information security strategy
2 - Integrating information security in the actual governance
3 - writing information security policies
4 - Creating Business cases
5 - Influence information Security Governance
6 - Gaining information security stakeholders commitment
7- Information Security Roles and Responsibilities.
This kind of projects implies that we must develop roadmaps of the information across all channels of the company , who is accessing the information, when and if have permissions for that.
I think a step like this should the foundation of your program.
A data strategy program needs to consider organizational design to support sustainment of the objectives once the program is over. For example, will you have a Chief Data Officer? Will there be defined Data Stewards and if so, what responsibilities will they have?
Thank you for your feedback. I’ve updated the title of my post to more accurately describe what aspect of an overall data strategy I will be managing. In this case there are several teams involved, Research, Product Teams, and other team(s) that manage the buildout of the platform that hosts the data sets (among other things) and will also deal more with the security/data governance. In my case, I will focus mainly on the items listed above and will probably interact in a very limited capacity with the data privacy and infosec teams (at least for now).
I have lot of experience in the field from the time where all these stuff was called just data warehousing. The reason because it is a program is because the whole endeavour is based on architecture and each architecture has components the usually each component is manage as a project. Then, here I fully agree with @Kiron. First of all this will be a solution for the company. So, sorry for put here a link to something I wrote, but perhaps this helps you: https://www.projectmanagement.com/blog-pos...-right-solution
Second, make some research on data warehousing components (the environment is still calling in that way) to understand the architecture and its components then to understand what you have to consider as projects. Returning to @Kiron point, it is critical to understand the strategy to select which components to consider. here is where my article could help.
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