[greggyb]

Dimensional modeling, though often associated with a traditional waterfall methodology, is not tied to this delivery methodology.

It remains the most understandable model to the largest population of end users. Analytics is a very broad term, as I mentioned in my original post, and the audience is huge. If "analytics" to you implies an audience of primarily data savvy end users, then dimensional modeling may not hold as much value.

I tend to find that the data scientists at our clients still do a lot more data wrangling than data science when they don't have clean models to work with as a baseline.

Additionally, there's a big difference between exploratory analysis of new data sources where access and low latency are key, and well-known domains that have fairly predictable needs. The former have a habit of transforming into the latter. Dimensional models remain one of the most efficient physical structures of data for a read-dominant workload.

Long story short, I think it's worthwhile to both of us to look outside our bubbles. I work for a BI and data science consultancy. My focus specifically is in core BI workloads, and so I'm definitely overexposed to more traditional modeling techniques. I can guarantee you, though, that the cycle time for a usable pilot that includes a fully realized dimensional model is more on the order of a handful of weeks than months in a typical delivery.

What's your bubble?