lookr is probably my favorite. Mode was strong as well, but they seem to be losing their way. once you get down to like tableau or something though, you may as well just use google sheets.
1. Much of the context is abstracted away when charts and summaries are crammed into a (usually) single page. This makes it hard for people to interpret quickly and requires a high degree of data literacy
2. Data is presented that fits retrospective summaries, or what I’ve learned to know I need to look at. This usually means the dashboard is not suitable for answering new questions, putting strain on the data model or falling back to custom queries
3. Combining 1 and 2 means that the people who build the dashboards are the ones who end up using them the most. So I find them good for ops teams and similar, but fall short of the coming promise of self-service data exploration yadda yadda.
...Which means the VP still needs a monthly hand-holding meeting to review these “self-service” dashboards (or worse - you screenshot and put it in a PowerPoint)
An existing/published dashboard of course does not offer the capability to discover new queries/freely explore the data, but you can always create a new dashboard using those BI tools, right? Somebody has to learn to use it and then share the results. And to be fair the learning curve isn't really high. Plenty of effort out there on democratizing visualization authoring, but still BI tools are the ones with the highest accessibility.
Agree with you in principle. I think the point I'm trying to make (and I could be communicating it poorly) is that these tools allow for building a summarized, retrospective, and opinionated view of data. This often doesn't meet business team needs as it does ops teams. Sample of 1, but in my 15y of industry experience dashboard usage doesn't often make it past middle management.
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[ 3.3 ms ] story [ 28.0 ms ] thread1. Much of the context is abstracted away when charts and summaries are crammed into a (usually) single page. This makes it hard for people to interpret quickly and requires a high degree of data literacy
2. Data is presented that fits retrospective summaries, or what I’ve learned to know I need to look at. This usually means the dashboard is not suitable for answering new questions, putting strain on the data model or falling back to custom queries
3. Combining 1 and 2 means that the people who build the dashboards are the ones who end up using them the most. So I find them good for ops teams and similar, but fall short of the coming promise of self-service data exploration yadda yadda.
...Which means the VP still needs a monthly hand-holding meeting to review these “self-service” dashboards (or worse - you screenshot and put it in a PowerPoint)