Thoughtful perspectives, practical frameworks, and expert takes on giving every team and AI assistant trusted answers from enterprise data.
Customer Request Type does not land as a column on issue. It lives in issue_field_history under a customfield id that differs per instance, and the obvious grouping returns three buckets, not fifty.
NetSuite's standard reports filter the transaction accounting line to one accounting book automatically. The warehouse copy of the same tables does not, so a naive SUM adds every dollar once per book.
Declare the inheritance join and the missing company FK, sign them off, and the incidents-by-customer chain comes back governed instead of flagged.
Incident extends task, so the join between them is not a foreign key. Introspection has nothing to infer and the governance analyzer never sees the join.
A four-step walkthrough on replicated Salesforce: introspect the schema, correct the grain and the custom fields, sign the definitions off, then verify the breakdown ties to the total.
Claude Desktop launches an MCP server on your machine. Cowork, claude.ai and ChatGPT connect inbound from the vendor's cloud. Which route you need is decided by one question.
The QBR needs eight quarters of pipeline by product. Salesforce cannot produce it, so the data lands in a warehouse. There the same question overstates pipeline 2.5x and reorders the slide.
We dumped Salesforce and ServiceNow into the lake and got a landfill of codes and GUIDs. Why the missing ingredient was always context, and how AI finally makes it affordable.
A fan trap or chasm trap can inflate an aggregate by 3x and nothing errors. Here is how our Analytics Agent detects the hazard from join cardinality and refuses to ship the wrong number.
An honest head-to-head of the three warehouse-native AI products. Where each wins, where each loses, and the architectural blind spot they all share.
A staging Postgres taken down. Wrong customers in dashboards. 4% revenue drift. Three production failures that taught us a data agent is context engineering, not prompting.
A decades-old BI concept just hit its all-time search peak. Every AI data agent in production now sits on top of a semantic model, and most are locked to one vendor's stack. Here is the history, the current landscape, and the open standard that breaks the lock-in.