Data & Analytics Reading List 07/2024
Never theorize before you have data. Invariably you end up twisting facts to suite theories instead to theories to suite facts. - Sherlock Holmes
01.07. - From data to decisions: Leveraging Generative AI and data products
02.07. - Why not to build your own data platform
03.07. - Unlocking Business Value and proving the value of data teams
04.07. - Why data-driven product decisions are hard (sometimes impossible)
06.07. - What 10 Years at Uber, Meta and Startups Taught Me About Data Analytics
07.07. - Bubble.ai
08.07. - FineWeb: decanting the web for the finest text data at scale
09.07. - What We’ve Learned From A Year of Building with LLMs
10.07. - The Danger Zone in Data Science
11.07. - The two critical steps to reach domain oriented ownership
12.07. - Musings on building a Generative AI product
13.07. - Five Levels Of AI Agents
14.07. - What is Semantics?
15.07. - Data Activation is not the goal (3 articles)
16.07. - Composable data management at Meta
17.07. - 6 Myths Preventing You from Embracing Real-Time Data
18.07. - Not Just Scale
19.07. - The Ultimate Guide to Making Sense of Data
20.07. - Pipeline in a Container: Docker Essentials for Data Engineers
21.07. - The Seven Habits of the Data Enabled, AI Powered “Intelligent” Enterprise
22.07. - How a Transformation Office will help you to build a data mesh
23.07. - The Agent Compass
24.07. - Roadmap: AI Infrastructure
26.07. - How to Escape Data Flatland
27.07. - Data Transformation Reboot: Use of Copilots in an Agentic Architecture
28.07. - Mastering AI Department Reorganizations: Lessons from the Trenches
29.07. - Why are GenAI products (relatively) harder to compete and profit
30.07. - The Future of AI is Vertical
31.07. - The Data Lifecycle