Generative AI does not forgive messy data; it amplifies it. The Verdict Data Management Strategy at Microsoft is not a beach read. It is a survival guide for the algorithmic age. It argues that in the race to be data-driven, most companies bought the race car (the AI) but forgot to pave the road (the data infrastructure).
By mastering data management first, Microsoft was able to layer AI on top safely. They can use LLMs to write SQL queries because they know the metadata is accurate. They can use AI to summarize sales calls because they know the governance rules regarding PII (Personally Identifiable Information).
Before you can predict the future, you need to trust the past. Microsoft’s internal hiring spree wasn’t for AI PhDs; it was for data librarians who understand SQL and communication.
For decades, Microsoft was a federation of warring fiefdoms. Excel teams, Azure engineers, LinkedIn data scientists, and GitHub developers all spoke different data languages. The result was the modern corporate nightmare: siloed lakes, conflicting KPIs, and dashboards that told five different versions of the truth.
Generative AI does not forgive messy data; it amplifies it. The Verdict Data Management Strategy at Microsoft is not a beach read. It is a survival guide for the algorithmic age. It argues that in the race to be data-driven, most companies bought the race car (the AI) but forgot to pave the road (the data infrastructure).
By mastering data management first, Microsoft was able to layer AI on top safely. They can use LLMs to write SQL queries because they know the metadata is accurate. They can use AI to summarize sales calls because they know the governance rules regarding PII (Personally Identifiable Information). data management strategy at microsoft book
Before you can predict the future, you need to trust the past. Microsoft’s internal hiring spree wasn’t for AI PhDs; it was for data librarians who understand SQL and communication. Generative AI does not forgive messy data; it amplifies it
For decades, Microsoft was a federation of warring fiefdoms. Excel teams, Azure engineers, LinkedIn data scientists, and GitHub developers all spoke different data languages. The result was the modern corporate nightmare: siloed lakes, conflicting KPIs, and dashboards that told five different versions of the truth. It argues that in the race to be