Data Engineering Is Evolving, but Most Data Engineers Aren’t §
Highlights §
- Most data can’t be used for analytics and training machine learning models (View Highlight)
- Most data has utility for digital use cases, but only data with context has utility for data and AI use cases. (View Highlight)
- A growing body of research supports the thesis that data quality and structure impact model reliability and generalization more than the quantity of data or model complexity (View Highlight)
- Businesses that adopt a data and AI-centric approach to data engineering have faster delivery times and more opportunities to automate the data lifecycle/workflow. (View Highlight)
- a data monetization catalog. It connects data sets and models to use cases. The data monetization catalog is my starting point for changing the assumption that all data is valuable. (View Highlight)
- Just because data is used doesn’t mean that the data is a value creator. Usernames and passwords are common enterprise data types that support login functionality. For a login popup or screen, the digital functionality is the primary value creator, not the username and password. The data has digital utility and value. (View Highlight)