"Offering managers didn’t have technical backgrounds and sometimes came up with ideas for new products that were simply impossible." Sounds like they drank their own kool-aid, e.g., "Products That Enhance and Amplify Human Expertise," rather than understand the actual limitations and possibilities of ML. And it seems to me that they're still doing it with this nonsense about a human-level "AI" debating stack. The ove…
There's a pretty good podcast interview with Eugene Dubossarsky that has relevant discussion about issues with management and data science in general. https://www.datafuturology.com/podcast/1 Here are a few of my notes (my words not the interviewee's): - in order to use data science, you have to have creative people thinking about data on the front end - they don't have to be data scientists, but they need to be crea…
whenever I'm asked to design a database for an early-stage system (I work in early stage tech ventures), I ask the following:
- what are the questions that this database should answer for you? How are those questions supporting your business goals 3,6,12 months out? (I'm trying to get to the business requirements here)
- who will be asking those questions (I'm trying to put together some user personas in my head)
- how frequently will they be asking these questions? corollary: how often will historical data be needed? (I'm thinking hot vs cold and complexity of retrieval, minimally required performance)
- how much data to we anticipate is needed to answer the questions (this is really tricky in new ventures - often the answer is more data than what will actually occur in practice in the first year)?
- finally, what systems & tools are people using to ask the questions and be notified of events? (I'm thinking about interfacing, apis)
its all an attempt to stay very focused on the questions and business drivers and the people who use the answers.