Non-Brand Data

“Non-Brand Data” delves into the realm of enterprise data management and AI applications, focusing on the challenges and solutions related to leveraging data beyond traditional branding contexts. The document explores topics such as the importance of understanding and utilizing diverse data sources, the pitfalls of treating data as static, and the necessity for AI systems to comprehend and interact with dynamic, living data. It also discusses the significance of secure knowledge base infrastructure, multi-tenancy architectures, and the role of technologies like Generative AI, Vector Embeddings, and Semantic Search in enhancing data understanding and retrieval processes.

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Module 1: Getting the question right before writing SQL

 Non-Brand Data

Learn how to define the population, metric, and time window before you write a query.

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SQL and Analytics course

 Non-Brand Data

Certificate Available! Learn how to define metrics, check your analysis, and explain the result in the era of AI.

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3 Things I Learned Testing One Market Data API Across 9 Markets

 Non-Brand Data

Building a live price alert dashboard across stocks, forex, futures, and crypto

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Stock API: What it is and Why it Matter

 Non-Brand Data

How I decide on a data provider before writing the integration, and why that choice outlasts the code.

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Manager Memo: Does Your Team Still Need SQL when AI writes it?

 Non-Brand Data

For managers evaluating AI-assisted analytics and data-team operating models

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Building a Minimal Agent Harness in Python

 Non-Brand Data

A beginner-friendly guide to running agent code in another process and stopping it when it takes too long.

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Why Valid SQL Returns Wrong Numbers

 Non-Brand Data

Three ways a query runs without an error and still returns the wrong number, with the query and the fix for each.

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My book is out: Python Data Analysis, Fourth Edition

 Non-Brand Data

Co-written with my co-author, and what the year of writing it was like.

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Fix it or throw it away: deciding what to do with an AI output

 Non-Brand Data

The harder part is knowing which problems you can fix and which ones mean starting over.

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Why Most GenAI Workflows Need a Review Loop

 Non-Brand Data

A Better Prompt Is Not Enough

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Manager Memo: Reviewing GenAI Output Before It Reaches Stakeholders

 Non-Brand Data

A guide for managers who approve AI-assisted work: what to check, when to reject, and how to talk to stakeholders about it.

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What Most GenAI Evaluation Workflows Get Wrong

 Non-Brand Data

Reliability is not a property of the final answer. It is a property of the whole system that produced it.

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