Your data moat gets weaker the moment interpretation gets cheap.
Your data moat gets weaker the moment interpretation gets cheap. So what happens when your "advantage" was just owning the spreadsheet?
In a lot of SMB and mid-market operators, the safe bet has been accumulation: more tickets, more transactions, more clicks. Centralize it in Salesforce, pipe it through Snowflake, add a dashboard, call it "insight".
That works when the hard part is collecting and correlating.
But a quiet shift is happening: the scarce asset is no longer the dataset. It is the ability to turn messy signals into decisions that hold up under pressure. When tools can infer, summarize, and propose actions from the same raw inputs, correlation stops being defensible.
Incumbents built on "we have more history" start discovering their history is mostly unlabeled. The system can find patterns, but it cannot tell you which ones matter, which ones are causal, and which ones will break trust when operationalized.
Then the network effect moves. Not "more data", but faster judgment loops: what you decided, why you decided it, what happened next. That layer rarely lives in ServiceNow or Jira. It lives in meetings, exceptions, and workarounds.
The teams that win will not be the ones with the most data. They will be the ones who can explain their business back to themselves, consistently.
Most organizations only notice this after the automation ships and the edge cases become the product.