9e9ae249bc
Add anonymized architecture, governance examples, diagrams, and interview materials.\n\nCo-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
44 lines
2.0 KiB
Markdown
44 lines
2.0 KiB
Markdown
# LinkedIn post draft
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Sales teams should not need an email chain to answer a routine quotation question.
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In a proof of concept, I designed a governance-first semantic analytics flow for quotation data:
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**SAP → Snowflake → dbt → Semantic Views → Cortex → Chatbot**
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The business problem was simple: more than 30 salespeople could need help validating a quote, discount, or product classification. A question that looked small could take 5–30 minutes because the answer depended on manual lookup and internal communication.
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The solution was not “put an LLM on top of raw tables.”
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I first created a semantic contract:
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- curated quotation dimensions and facts;
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- business-friendly synonyms for natural-language questions;
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- centralized discount calculations;
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- explicit policy-exception flags;
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- lineage from source data to the consumer-facing model.
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Then Cortex applied the controls around that contract:
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- data freshness warning for the daily batch;
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- role-based access;
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- protection of sensitive information;
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- escalation when a discount exceeded policy;
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- audit logging for queries and decisions.
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The semantic model was consumed in two ways:
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1. Snowflake's interface for analytical exploration.
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2. An internal chatbot workspace for direct questions, with prompt engineering to refine the response experience.
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The estimated user outcome was reducing routine quote research from 5–30 minutes to less than one minute. That is an estimate based on the existing human workflow, not a production benchmark—and that distinction matters.
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The main lesson: **governance belongs in the data and semantic layers, not only in the prompt.**
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The POC also created a reusable delivery pattern for future use cases: define the business contract, expose only the right data, add guardrails, then choose the lightest useful interface.
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I documented the anonymized architecture, decisions, and lessons learned here:
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[GitHub repository link]
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#DataArchitecture #Snowflake #dbt #DataGovernance #EnterpriseAI #TechnicalDelivery
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