Analytics Is at a Crossroads
Summary
Benn Stancil argues that the analytics profession faces a fork in the road, and the direction it takes depends on how we define the relationship between analytics and engineering. The central mental model is two paths for analytics engineering:
Path 1: Analytics engineering as a barrier (liberating). AE becomes a distinct discipline that handles the technical plumbing, freeing analysts to be what they should be — critical thinkers, communicators, and decision-shapers. Data teams can hire historians, sociologists, and political scientists who reason well, rather than mathematicians who code passably. The yardstick for a great analyst is the quality of their thinking and communication, not their engineering chops.
Path 2: Analytics engineering as a bleed (absorbing). AE becomes the next rung on a continuous technical ladder. Analysts “graduate” to analytics engineers once they are technical enough. Technical skill remains the measuring stick. Analytics stays positioned as engineering’s junior sibling.
Stancil’s argument: Path 1 is better for the profession. Analytics is not primarily technical. When someone questions whether analysts are “real engineers,” the correct response is “so what — that is not our job.” We do not call analysts “writers” even though clear communication is essential to their work. Why should we define them by their engineering ability?
This framing is directly relevant to 01-projects/phdata/index and 01-projects/phdata/career-transition. When building consulting teams or advising clients on hiring, the question “what makes a great analyst?” determines job descriptions, interview loops, and career ladders. Path 1 says: invest in AE infrastructure so your analysts can focus on judgment and persuasion.
The idea that analysts should be judged by a different yardstick connects to 06-reference/concepts/skills-as-building-blocks — analytical reasoning and communication are transferable building blocks, while tool-specific expertise is not.
This piece is a companion to 06-reference/2026-04-03-headless-bi and 06-reference/2026-04-03-data-products-taxonomy — all three are Benn Stancil thinking about what the analytics layer should actually do and be. The crossroads framing also extends the maturity conversation in 06-reference/2026-04-03-data-maturity-processes-tools: as an org matures, does the analyst role evolve toward deeper business expertise or deeper technical expertise?
Open Questions
- What is the right yardstick for measuring analysts if it is not technical skill? Decision quality? Revenue influenced? Stakeholder satisfaction?
- In practice, most companies seem to be on Path 2. Is Path 1 achievable without a mature AE function already in place?
- How does this framework change when AI can handle much of the technical plumbing that AE currently does?