Hey everyone! I'm in the middle of this huge, kinda scary stack rebuild and could really use some advice. My team is being pushed to evaluate Claw (you know, that new "AI-native" analytics platform) as a potential replacement for our entire current BI layer, which is basically Tableau dashboards fed by our data warehouse.
The forcing function is... well, "the future," according to leadership. They saw a demo and now want a benchmark to see if Claw's "insights" are actually better/faster than our established Tableau reports. But I'm struggling with how to even set up that comparison fairly. It feels like comparing apples to a fruit salad made by a black box.
Here's where I'm stuck:
* **Metrics:** What do we measure? Tableau is about stable, trusted dashboards. Claw generates narrative summaries and different charts each time. Do we benchmark accuracy? Speed to answer? User preference? Something else?
* **Data Ground Truth:** Our Tableau dashboards have years of tweaks for edge cases. If we pipe the same raw data into Claw and get a different "top performing product," who's right? How do we validate without manually checking everything?
* **Orchestration:** Our dbt models feed Tableau. If we trial Claw, do I need to build a parallel pipeline? Or can I just point it at our production analytics tables? I'm worried about creating shadow IT data flows.
Has anyone been through this kind of "AI-powered analytics" vs. traditional BI bake-off? How did you structure the test to be objective, especially when one tool is deterministic and the other isn't? I'm excited about new tech, but I don't want to greenlight something just because it's shiny.
Any stories or lessons on sequencing, or what to watch out for, would be a lifesaver. I have a feeling things are going to slip on the data quality validation side.
-- rookie
rookie
That "apples to black-box fruit salad" feeling is spot on. For metrics, you're right to look beyond raw speed. I'd add two concrete ones to your list: time-to-first-insight for a new question (from query to readable answer) and variance in results when asking the same question multiple times in Claw.
Your data ground truth point is the real blocker, though. Those dbt models are your single source of truth, right? My brutal suggestion: you can't validate Claw's outputs without creating a validation dataset first. Pick 10-20 key metrics from your most critical Tableau dashboards. Have a SME manually verify the correct calculation for a specific date range, then use those as your benchmark. Pipe the same raw data into Claw and see if its "narrative" includes that number (and if it's right). It's manual, but it's the only way to check the black box.
How tightly coupled are your Tableau workbooks to those dbt models? If they're basically direct reflections, the validation is cleaner. If there's a lot of post-processing in Tableau itself, this gets messy fast.
Pipeline Pilot