Plenty of Indian SMBs have bought Power BI or Tableau licences. Very few use them. The tools are excellent — the mismatch is the assumption baked into them: that you have someone whose job is to model your data and build every chart. Most SMBs don't, and never will. Here's why the generic-BI model breaks for a 60-truck fleet or a ₹40 Cr job shop, and what fits instead.
Power BI and Tableau are canvases. Out of the box they know nothing about consignments, lanes, PODs, OEE or schedule adherence — someone has to teach them, chart by chart. That someone is a data analyst, and the licence is worthless until they've spent weeks modelling. For an enterprise with a BI team, that's fine. For an SMB owner, the project stalls at 'we'll get to it' and the licence goes unused.
Generic BI assumes reasonably clean, structured inputs. Real Indian SMB data is Tally and Excel exports with misspelt column headers, mixed date formats, numbers stored as text, and the same customer spelt three ways. Before you can build a single chart you need a mapping-and-cleaning layer — which is another project most SMBs can't staff.
Even once it's built, a generic dashboard formats money in millions, not lakhs and crores; it doesn't know about GST or e-way bills; it has no concept of a daily WhatsApp digest, which is how an Indian owner actually consumes information. You end up fighting the defaults instead of using them.
The alternative is to ship the dashboards already built for one industry, on the data that industry actually keeps. The domain knowledge — what a lane is, why POD blocks invoicing, how OEE decomposes — is baked in, so there's no modelling project. The owner's job shrinks from 'hire an analyst and build BI' to 'upload last year's data and read the answers'.
The trade-off is honest: a domain product is less flexible than a blank canvas. You can't build literally any chart. But an SMB doesn't need any chart — it needs the fifteen that run its business, working on day one.
From cost/km to value-at-risk — the numbers that actually decide whether a fleet makes money, and how to read them.
How a mapping layer turns real-world spreadsheets into a canonical model you can trust.