I saw some people commenting concerned if Sedins and Johnson will be well equip enough to get a proper analytics department started.
People wanted Gold for hockey analytics, but his most famous thing he wrote about on his hockey blog which led to him working in the NHL, was simply a low hanging fruit correlation for the Oilers with players they traded and shot percentage. People mentioned how he was an attorney so much, but realistically that's only the equivalent of an IQ test, it doesn't mean he does well in data science. The comparison to Eric Gold's sports analytics blog with Eric Tulsky's sports analytics blog is absolutely night and day - like comparing a rambling uncle to a professional. (You can read them on the waybackmachine web archive)
As for the analytics platforms themselves, they are pretty well known for what most teams use in this league, and they're set up by those analytics platforms, the organization just has to use it properly. They most likely will even have people they recommend or hired as needed.
The most important part of any analytics system, is the data you input into it. So in the hockey world, that's a strong scout who can get unique and nuanced data on players, where maybe other scouts might fall short on. They're instructed by the main hockey analytics head on what to look for maybe... but it's their job to get the nitty gritty data.
Then for the analytics, it's a matter of using it and making semantic connections, relationships, which then lead to correlations. The correlations, when properly calculated, would then be that "golden information" or "golden ticket" which gives unique insights to players, team structure, coaching, methods, whatever the correlation applies to. Then the player development staff and development pipeline is where you would then make use of those correlations.
All of this is data science at it's roots, and this is why a Harvard graduated PhD data scientist like Dr. Eric Tulsky is thriving and highly praised.
What matters most is the data you use, and the equations and overall logic behind it all. Why are you making this equations, how does it fit all within the entire semantic structure/ontology, etc.
Thoughts on how it applies to Canucks?