[baconner]

As much as I like Few's books and have relied on them heavily in my time building SSBI tools he has a tendency to take something I basically agree with and blow it way out of proportion into an all-or-nothing type declaration. Seems he's true to form today again with a basically correct point that many SSBI tool vendors over-promise and mislead with their marketing and turned it into "self service bi is a lie" which it absolutely is not.

Self Service BI tools are not intended to take the place of analytic skill any more than they take the place of domain expertise and that has never been the meaning of the term. The promise of SSBI is to reduce the incredible friction domain experts traditionally had to deal with to get their key business questions answered. Yes, your users need to develop other analytic skills to go along with their domain expertise! Turns out most of us have stronger and weaker points and have to learn and evolve our skills to get our jobs done well. Taking on SSBI means exactly that for your users who most likely have at least one of the key skills (domain expertise) already and maybe more.

Using an exploratory type ssbi tool is a conversation with your data via an interactive tool. One question leads to another leads to another and if the alternative is having to stop and ask another department to put each follow up question on their backlog the conversation is basically broken and often business users just stop asking and revert to pure gut feel decision making. I think most of the progress made over the past 20 years in BI has been about making this kind of process more agile in the same sense as iterative development. SSBI is part of that. The inversion of analytic process in big data systems is part of that as well. ML can also play a role in that with the right circumstances.

What we can do, as BI vendors, is build tools and documentation that guide users who start with only part of the skills they need into learning the rest while using the tools we provide. We can present defaults that guide the user towards visualizations and views that are easier to interpret. We can embed analytic skill building into our applications in tutorials and hints. We can build metadata up as users inform us about the data as they use it rather than requiring them to do it up front in a big-bang DW modeling session. We can inspect the data with simple heuristics to try and hint the user how to use it with the tool or apply better defaults. We can build better cleansing munging and data consolidation tools. We can build tools to let data analyst teams also turn things around more quickly. And yes, perhaps we can try using machine learning to suggest possible avenues for the user to explore which _of course_ must be interpreted by a user with the right skills because ML is going to be wrong a lot of the time and users need to understand false positives. It's all part of the process.

Bailing out on SSBI because of marketers being marketers just isn't pragmatic. Better that we just keep evolving our products from both the data science team end and the business user ssbi end.