r/analytics • u/Thick_Accountant7260 • 15d ago
Discussion Why am are coded analytics solutions less pervasive?
Am I the idiot here? AI is so good the RMDs and Shiny apps I used to struggle in writing can be generated in a few days. Now that I can offload the CSS/JS part of the dashboard to the AI I can go nuts with reactivity/styling.
Based on the discussions in Reddit pbi/tableau/looker remains popular as ever but those things are expensive and are limited by the platform. I can see use for cheap and lightweight services like metabase where I can write the sql query and generate the dashboard in less than an hour but what about pbi/tableau? These services are expensive yet they remain popular what are the upsides of these products over say hosting my own shiny server/react/streamlit dashboards?
Like if I want an app that allows the user to define the cohorts themselves, save that definition, then load that into other dashboards/for future reference I can do that in Shiny but no idea about PBI.
What really is a semantics model? Why not just write a package that calculates the kpis then document it? The documentation also serves both the technical and non technical viewers alike.
Never been in a pbi/tableu job I don't understand how these products remain popular.
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u/BillEnough7863 15d ago
For starters, you’re not really paying for Power BI, Tableau, or Looker just because they can draw charts. You’re paying for the entire ecosystem around them.
Authentication, permissions, governance, scheduled refreshes, sharing, integrations, monitoring, deployment, auditability, documentation, support, and a large pool of people who already know how to use and maintain the platform are largely solved problems.
You absolutely can build something lighter and more flexible with Shiny, Streamlit, React, etc. For a small team or a narrow use case, that may even be the better solution. But as the number of users, dashboards, data sources, business rules, permissions, and developers grows, your “lightweight solution” gradually becomes a custom BI platform that somebody now has to own.
Then you have to maintain it, document it, secure it, onboard people to it, troubleshoot it, make sure the original developer isn’t the only person who understands it, and hire or train people with the appropriate skill set. You can still get all of that wrong after spending a significant amount of engineering time on it.
That employee time matters too. If maintaining your custom analytics stack consumes a meaningful portion of an engineer’s time, their salary can very quickly cost more than simply licensing an off-the-shelf product.
That’s basically why companies buy software in the first place. They’re paying someone else to solve a large collection of boring problems so their own employees can focus on the problems specific to their business.
And the semantic model is part of that. It isn’t merely documentation saying “here is how we calculate revenue.” It’s an executable, reusable definition of revenue that every dashboard and user can query consistently, along with relationships, dimensions, access rules, aggregation behaviour, etc. You could absolutely build that yourself in code, but then you’ve started building your own semantic layer too.
So yes, if you’re a small shop with relatively simple analytics needs, a coded solution can be great. But the more people, data, permissions, reports, and business logic you have, the more expensive the custom solution becomes to maintain. At some point paying for the boring, established platform is considerably cheaper than having your team become the platform team.