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Comment by mceoin | Hacker News Reader

Agree. A general thesis I have is that the API-ification of the web fragmented business information, and with every new SaaS tool we fragment our company's data further. The trend at all company sizes is to be increasingly analytical, but for SMBs it's too hard to get access to your data (mainly due to technical limitations). So it makes sense to centralize data somewhere, and we think that somewhere is inside the data tool that everyone actually uses: the spreadsheet.

Many other advantages of this data centralization too. Data + spreadsheets + compute is a nice application base for agents.

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> So it makes sense to centralize data somewhere

Modelling and integrating datasets that you don't own is extremely hard.

Shopify for example updates their API every 3 months.

How much time and money do you think an SMB can afford to spend on this before the ROI becomes so poor that they abandon it entirely.

Yes some integrations are excellent (hey Stripe : ), some are terrible (no comment on who). We're finding that LLMs increasingly able to fill the gap around organizing data schema for that initial data prep piece where someone has to build the data tables that others consume. To your specific question/problem set, when a schema updates you end up with a "fuzzy schema matching problem"; we are solving that separately anyways for a separate product feature requirement.

Strong note here that the current state of technology is much better for SMB scale data and not enterprise scale data with messy schemas.

There is a separate answer here which is many (most?) SMBs can't afford technical folk, so the ability integrate data at all, talk to it and model it (using SQL or AI), is already a big step forward for them.

My personal use case tends to involve a lot of Postgres data and transaction events for my reporting. We see "simple" businesses like parts manufacturers, print shops, vineyards, etc. all doing something similar.

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