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# Lessons from Benn Stancil of Mode
- URL: https://www.antoinebuteau.com/lessons-from-benn-stancil-of-mode/
- Published: 2025-10-17T03:33:40.000Z
- Updated: 2026-07-18T22:13:39.000Z
- Description: Benn Stancil, Mode co-founder and writer on data and analytics, brings a contrarian, pragmatic eye to the modern data industry. His work questions hype around tools while examining the roles, cultures, and operating realities of data teams.
- Author: Antoine Buteau
- Tags: Profile, Engineering, Business Leaders & Executives Profiles

Benn Stancil, a co-founder of Mode and a prominent writer on data and analytics, is known for his sharp, often contrarian, and widely-read blog, "The Benn Stancil Substack." His essays dissect the realities of the modern data industry, challenging hype and offering a pragmatic perspective on the roles of data teams, the tools they use, and the cultures they operate in.

![Infographic for "Lessons from Benn Stancil of Mode".](https://www.antoinebuteau.com/content/images/2026/07/lessons-from-benn-stancil-of-mode-optimized.webp)

### On the Modern Data Stack and Tooling

1. **"The modern data stack is a solution in search of a problem."** Stancil argues that the explosion of tools in the modern data stack (MDS) was driven more by venture capital and market trends than by a fundamental shift in the core problems data teams face.
2. **"The dirty secret of the modern data stack is that it’s not for analysts."** He contends that the MDS was built primarily for data engineers, often creating more complex workflows for the analysts who are meant to be its primary users.
3. **"For all the talk of being ‘modern,’ the stack has a decidedly dated feel."** Stancil points out that the component-based architecture of the MDS resembles the on-premise systems of the past, just reconfigured for the cloud.
4. **"The modern data stack is a\_ Rube Goldberg machine\_ of BI."** This quote colorfully illustrates his view that the MDS often involves overly complex, pieced-together solutions for tasks that were once more integrated.
5. **"We aren't building the next generation of data tools; we’re rebuilding the last one with new logos and new database connectors."** A critique of the incremental, rather than revolutionary, nature of many new data products.
6. **"The modern data stack is built on a lie... that we, the data people, are the chosen ones, destined to lead our companies to a higher state of being."** He challenges the self-important narrative sometimes found in the data community, advocating for a more humble and business-focused role.
7. **"The biggest lie the modern data stack ever told is that it was a stack at all. It's a puddle."** This metaphor suggests a lack of cohesion and a sprawling, often messy collection of tools rather than a structured, interoperable "stack."
8. **On the "unbundling" of BI:** The MDS unbundled the all-in-one BI tool into dozens of specialized products, promising best-in-class solutions but often delivering integration headaches and higher costs.
9. **"The problem with the modern data stack isn't that it's unbundled; it's that it's incoherent."** The issue isn't just that the tools are separate, but that they don't work together in a logical, seamless way for the end user.
10. **"The next data stack will be a single, cohesive product."** Stancil predicts a "great rebundling," where the fragmented tools of the MDS will be consolidated back into more integrated platforms that are easier for analysts to use.

### On Data Teams, Analysts, and Their Role

1. **"Data teams aren't support staff. They're a product team."** He advocates for data teams to operate like product teams, with their own roadmaps, priorities, and focus on delivering value to the business, rather than being a reactive "help desk" for data requests.
2. **"Stop being a service organization."** Stancil urges data teams to move away from a model where they simply fulfill requests and instead proactively identify and solve business problems.
3. **"Analysts should be businesspeople first, and data people second."** The most effective analysts are those who deeply understand the business context and use data to solve business problems, not just produce reports.
4. **"The job of a data analyst is not to answer questions, but to ask them."** This highlights the importance of critical thinking and curiosity in the analyst role, pushing beyond simple query fulfillment.
5. **"The most valuable skill for a data analyst is not knowing SQL, but knowing the business."** Technical skills are foundational, but true impact comes from applying those skills to what matters for the company.
6. **"Data teams are often caught in a 'tyranny of the urgent,' constantly firefighting and responding to ad-hoc requests, with little time for strategic work."** A common pitfall that prevents data teams from delivering their full potential.
7. **"The ultimate goal of a data team is to make itself obsolete."** By empowering the rest of the organization with the tools and skills to answer their own data questions, the data team can focus on more complex and strategic challenges.
8. **"Data analysts are the translators between the language of data and the language of the business."** This emphasizes the crucial communication and storytelling aspect of the analyst's role.

### On Data Culture and Decision-Making

1. **"Data-driven is a destination, not a state of being."** Becoming data-driven is a continuous process of improvement, not a switch that can be flipped by buying new tools.
2. **"A company's data culture is not defined by its tools, but by its behaviors."** A truly data-driven culture is about how people make decisions, not the software they use.
3. **"The goal of a data team is not to make every decision data-driven. It’s to help people make better decisions."** Sometimes the best decision is not purely data-driven, and the data team's role is to inform, not dictate.
4. **"Self-serve analytics is a myth."** The idea that business users will simply and effectively answer all their own complex questions with a BI tool is unrealistic. It requires guidance, training, and a strong partnership with the data team.
5. **"Dashboards are where data goes to die."** Stancil is critical of the overuse of dashboards, which are often built, looked at once, and then ignored, creating "data graveyards."
6. **"Metrics don't matter if they don't change how you act."** The purpose of measurement is to drive action and improve outcomes, not just to report numbers.
7. **"The CEO is the Chief Data Officer."** The commitment to a data-driven culture must start from the very top of the organization to be successful.

### On Business Intelligence (BI) and the Future

1. **"BI is not a technical problem; it’s a social one."** The challenges of BI are less about technology and more about communication, collaboration, and aligning on what matters to the business.
2. **"The future of BI is not about more dashboards, but about more conversations."** Effective data work is collaborative and iterative, involving a dialogue between the data team and business stakeholders.
3. **"AI will not replace analysts. It will replace the parts of their jobs they hate."** He sees AI as a tool to automate tedious tasks, freeing up analysts to focus on more strategic and interpretive work.
4. **"The future of the data stack is one that is built for analysts, not engineers."** The next wave of tools will prioritize the user experience of the people who are closest to the business problems.
5. **"We need to move from 'data-as-a-service' to 'data-as-a-product'."** This involves building durable, well-maintained data assets that serve ongoing business needs, rather than one-off reports.
6. **"The metric layer is the Holy Grail that nobody can find."** While the concept of a centralized, single source of truth for metrics is appealing, it has proven incredibly difficult to implement and maintain in practice.
7. **"The best BI tool is a conversation."** This underscores his belief that direct communication and collaboration are often more effective than any piece of software.

### On Industry Trends and Hype

1. **"The data world is high on its own supply."** A critique of the industry's tendency to get caught up in its own hype and jargon, losing sight of the fundamental goal of solving business problems.
2. **On the hype around data catalogs:** While useful, he argues they don't solve the core "last mile" problem of translating data into actionable business insights.
3. **"We're all just building spreadsheets, again and again."** A reminder that many complex data tools are ultimately trying to replicate the flexibility and accessibility of the humble spreadsheet.
4. **"The 'data mesh' is a solution to an organizational problem, not a technical one."** He sees the data mesh concept as a way to structure teams and responsibilities, which may not be necessary or appropriate for all companies.
5. **"Stop trying to be Google."** Stancil advises smaller companies to avoid copying the complex data architectures of tech giants, as their problems and resources are vastly different.
6. **"The 'citizen analyst' is a unicorn."** The idea of a business user who is also a skilled data analyst is rare, and data teams should not build their strategies around this exception.
7. **"We confuse 'can' with 'should'."** Just because we can build a complex data pipeline or a detailed dashboard doesn't always mean we should. The focus should be on value and impact.

### Additional Learnings and Insights

1. **The importance of "The Last Mile."** The most critical, and often most difficult, part of analytics is the "last mile"—the process of turning a data insight into a concrete business action.
2. **Pragmatism over dogma.** Stancil consistently advocates for a practical, results-oriented approach to data work, rejecting rigid adherence to buzzwords or trends.
3. **Focus on the "why," not just the "what."** Understanding the business reason behind a data request is more important than simply fulfilling the request itself.
4. **Simplicity is a virtue.** The simplest solution that solves the problem is often the best one.
5. **Data work is a craft.** It requires not just technical skill, but also judgment, creativity, and a deep understanding of the medium (data) and the context (the business).
6. **Narrative and storytelling are key.** The ability to weave data into a compelling narrative is what separates a good analyst from a great one.
7. **Embrace the messiness.** Real-world data is often messy and incomplete, and analysts need to be comfortable working with ambiguity.
8. **The value of skepticism.** A healthy dose of skepticism towards vendor claims, industry hype, and even one's own analysis is essential.
9. **Build partnerships, not dependencies.** Empowering business users is more sustainable than making them dependent on the data team for every request.
10. **"Data is political."** Data can be used to support different agendas, and analysts need to be aware of the organizational dynamics at play.
11. **"The job is never done."** The work of a data team is a continuous cycle of asking, answering, and asking again, as the business evolves and new questions arise.