The key to success is to have a value-driven approach. Start by identifying the different ways data can be used to to drive value in your business, then you work backward to identify what data is needed and how you can use it to drive that value. Keep your focus narrowly on that until you've delivered that value to that end user, to that line of business, but don’t stop there. Go the extra mile and make sure that the line of business understand the value and your business clearly understands the value.
The warehouse is where the data lives, so it performs a storage function, but it's also where all the computation happens. These technologies provide organizations with the ability to store and compute on data at unprecedented scale in ways that are much more economical than ever before. If you vizualize the modern data stack as a diagram from left to right, on the left you have the data and specialized ingestion tools that bring the data to the warehouse in the middle. Then within the data warehouse, the you are provided this computation so your team can transform the data that comes in into different derived data sets that can be used by different teams.
Then on the right you've got your different BI tools for visualizing and consuming that data push it back into your different SaaS applications or different production systems. Those are, on a broad level, the core different components of the modern data stack. What we've seen over the last few years is more and more vendors and more and more boxes appear is as this kind of ecosystem has exploded around the cloud data warehouse to help organizations execute more and more use cases on that architecture.
Data teams need to stay focused on ensuring they are creating clear, measurable value for the business through how they implement the modern data stack.
A value-driven approach to the modern data stack starts with identifying the ways data can be used to drive value and working your way backward to identify what data is needed and how to use it to drive that value.
Organizations should always be open to optimizing the technology for the use case rather than forcing the use case to fit the technologies and the architecture that they want to adopt.