Now that you have the inventory of your data environment, you understand the value, and have influenced changed in how people work, its now time to have an engine that can run and support future needs of the organization. What I mean by an engine, is by collecting the inventory and building a product around it, people throughout the organization now will have innovative ideas. While making enhancements to your product will help the organization, part of being a good product is enabling others to build upon what you already have. This changes the product to a platform.
- A Product is a unique tool with a specific design/purpose. There are use cases and scope, users, and ultimately an end to its development lifecycle.
- A Platform is a series of products and API's that not only enable the use cases and scope, but also allow others to utilize that information/content to expand into other use cases and purposes, far beyond the scope what the original product team may come up with.
To enable to this, you need to enable systematic communication both for receiving content and sending out content. You may ask, why this is needed for both incoming and outgoing content.
- With incoming content, others within the organization may want to provide additional information to the platform like SLA, data quality, comments, and more. Allowing this to be systematic enables other products and other teams to ultimately work off of a single platform.
- With outgoing content, others within the organization may want to consume inventory counts, relationships, and more to provide a custom / unique experience to their user base.
When speaking to the context of the data inventory, building that engine (or platform) which allows others within the organization to expand on data driven tools/products. The analysts may build notifications on new data sets, the data engineers may build SLA reports on their data pipelines, the data scientists may build dynamic models/relationships, the system owners may build dynamic notifications on relationships between systems, and the data consumers may build metadata management automation
This is where products become platforms (the boys to the men) and what a single product team started morphs into what an entire organization utilizes and drives their day to day work on going forward. This is where you not only influence but also enable the rest of the organization to be "data driven"