The Data Studio Concept

Here are the notes, via NoteGPT.

It just occurred to me that the best videogames I’ve ever played are all downloadable to my console. The endgame is the kind of gamification of the best sort with data. It’s not going to be that big - even though we can navigate through for hours, days, weeks. Think about it. How long will it take until we have the equivalent of cross-platform single player campaigns that will get us to the historically accurate representations of whatever it is we individually want to know? This is what I see in the future - the encapsulation of every navigation we need, well-tuned for expert consumers.

What I also forgot to mention was that I have finally got my hands on a copy of Roland Fryer’s data. But I didn’t have a Stata license, but I did get an open-source Stata reader and converted it quickly into DuckDB, but

TLDR

Summary
Mike Bowen reflects on his career shift from big data to small data, emphasizing depth over breadth and the need for personal insight in data curation.

Highlights

- 🧑‍🦳 Career Transition: Bowen moves from big data to small data for deeper insights.

- 📊 Data Depth: Emphasizes the importance of meaningful data collection and analysis.

- 📝 Personal Perspective: Advocates for unique insights over mainstream narratives.

- 🎥 Multimedia Use: Plans to incorporate video and critical analysis into data intelligence.

- 🔧 Tool Preferences: Prefers Rust and specific data management tools for effectiveness.

- 📚 Knowledge Curation: Utilizes various platforms for organizing and accessing data.

- 🤔 Proprietary Concerns: Questions the quality of publicly available big data resources.

Key Insights

- 📈 Shift in Focus: Bowen advocates for a shift from big data architectures to small, focused data sets. This reflects a growing recognition that not all data is valuable, and smaller, well-curated data can provide more actionable insights.

- 🎨 Artistic Approach: He likens data management to artistic creation, suggesting that true value comes from personal interpretation and creativity rather than algorithmic processes. This underscores the need for human insight in data analysis.

- 💡 Critical Analysis: Emphasizing the necessity of expert knowledge, Bowen highlights the importance of critical analysis in interpreting data, especially when countering mainstream narratives that may lack depth.

- 🔍 Privacy and Control: He expresses a desire for data management that ensures privacy and control over the information, advocating for a more individual-centric approach to data curation.

- 🛠️ Tool Utilization: Bowen’s preference for specific tools (like Rust and DuckDB) indicates a trend towards more efficient, manageable data solutions tailored to individual needs. This reflects a broader industry shift towards personalized data tools.

- 📖 Knowledge Management: His extensive use of various platforms for knowledge management illustrates a methodical approach to information curation, emphasizing the importance of organizing data for effective access and analysis.

- 🌐 Proprietary Issues: Bowen raises concerns about the quality and bias of big data resources, suggesting a potential need for proprietary solutions that prioritize quality over quantity. This reflects ongoing debates in the data community regarding data ethics and reliability.