Most data analyses die in silence. According to industry research, 87% of data science projects never make it to production, not because the analysis was wrong, but because no one asked the right question in the first place.
Ask any analyst about their workflow and you'll hear about tools: "I use SQL in Snowflake, then Python in Jupyter, then export to Tableau." But tools aren't workflow.
Phase 1: Question Formulation
The most expensive analyses answer the wrong question perfectly. Coca-Cola surveyed 200,000 people before launching New Coke in 1985. The data showed a clear winner, but they asked the wrong question.
As Cassie Kozyrkov puts it: "In decision-making, it's crucial to avoid the pitfall of correctly solving the wrong problem."
Phase 2: Exploration & Discovery
45-60% of analyst time goes here. The problem isn't just that exploration takes time, it's that exploration work is often impossible to reproduce. A study of 1.4 million Python notebooks found only 4.03% could be successfully replicated.
Phase 3: Analysis Refinement
The gap between "it worked once in a notebook" and "it's production-ready" is larger than most teams acknowledge. Tristan Handy, founder of dbt Labs, has observed that data as a profession is probably two decades behind software engineering.
Phase 4: Communication & Decision
This is the biggest gap in existing frameworks. 42% of data scientists say their results are not used by decision makers. Benn Stancil frames it directly: "If the point of analysis is to help people make decisions, this measures that goal most directly."
Phase 5: Operationalization & Monitoring
The 87% failure rate for ML projects reaching production isn't a technical problem, it's a workflow problem. Treat operationalization as a core part of the process from day one.
The Ideal State
A good workflow should feel natural: progression from question to insight, with tools supporting rather than interrupting thought.
Join the Veritly waitlist to see how we're building that coherent end-to-end system.
