Founders and CEOs are wondering if their data function is bloated and if they should replace everyone with AI agents. Data Leaders are scrambling to defend why they need a 15-people data team in a 200-people startup. And then there’s the one analytics engineer in another company - managing everything, secretly wondering: “Shouldn’t there be more of us?” (View Highlight)
Founders and CEOs are wondering if their data function is bloated and if they should replace everyone with AI agents. Data Leaders are scrambling to defend why they need a 15-people data team in a 200-people startup. And then there’s the one analytics engineer in another company - managing everything, secretly wondering: “Shouldn’t there be more of us?” (View Highlight)
I often run data audits for CEOs and CTOs of VC- or PE-backed companies. These are typically fast-growing businesses with complex data needs: e-commerce, insurance, or consumer subscription products. But I’ve also run data audits for multinational behemoths with 15,000 employees and 750-people in data. (View Highlight)
I often run data audits for CEOs and CTOs of VC- or PE-backed companies. These are typically fast-growing businesses with complex data needs: e-commerce, insurance, or consumer subscription products. But I’ve also run data audits for multinational behemoths with 15,000 employees and 750-people in data. (View Highlight)
These Executives have usually built a team, invested in infrastructure, and then… something doesn’t feel right. Maybe the data team is too slow. Maybe decisions are still being made on gut feeling. Maybe the impact isn’t “tangible”. Or maybe the CEO just looked at the org chart and asked: (View Highlight)
These Executives have usually built a team, invested in infrastructure, and then… something doesn’t feel right. Maybe the data team is too slow. Maybe decisions are still being made on gut feeling. Maybe the impact isn’t “tangible”. Or maybe the CEO just looked at the org chart and asked: (View Highlight)
You don’t start by asking how many analysts you should have. You start by looking at how many real data users you have inside the company: people who engage with data to make decisions, use dashboards, run reports, or use data tools. (View Highlight)
You don’t start by asking how many analysts you should have. You start by looking at how many real data users you have inside the company: people who engage with data to make decisions, use dashboards, run reports, or use data tools. (View Highlight)
Let’s say there are 200 people in the company and let’s assume that all of them will work with data (a simplified assumption). (View Highlight)
Let’s say there are 200 people in the company and let’s assume that all of them will work with data (a simplified assumption). (View Highlight)
Generally, a data team size of 5% relative to the org size is something that I have observed to be relatively common across many projects and conversations with other data leaders. (View Highlight)
Generally, a data team size of 5% relative to the org size is something that I have observed to be relatively common across many projects and conversations with other data leaders. (View Highlight)
If your data foundation is weak, you’ll need more people to babysit it. (View Highlight)
If your data foundation is weak, you’ll need more people to babysit it. (View Highlight)
More foundational debt = more humans doing janitorial work. (View Highlight)
More foundational debt = more humans doing janitorial work. (View Highlight)
Forget vanity metrics like “one analyst per 50 employees” or “every team needs a dedicated data person.” (View Highlight)
Forget vanity metrics like “one analyst per 50 employees” or “every team needs a dedicated data person.” (View Highlight)
If your data team is bigger than 5% of total headcount, something is probably off. It’s worth asking if your team is sized for reality… or for past decisions that no longer hold up. (View Highlight)
If your data team is bigger than 5% of total headcount, something is probably off. It’s worth asking if your team is sized for reality… or for past decisions that no longer hold up. (View Highlight)
How big should a data team be?
It’s a simple question. But in today’s age of AI agents and vibe coding, the answer has become emotionally and politically charged. (View Highlight)
• 173 Users = business decision makers who only consume data & analytics content
• Ca. 17 Super Users = business decision makers who can independently create simple analyses and reports → 1 Super User serves ca. 10 Users
• Ca. 6 Analysts / Data Scientists = data people who can create complex analyses and reports) → Ratio Super Users to Analyst of 3:1 - 2:1
• Ca. 2 Analytics Engineers = data people who can transform raw data into data that’s useable for analytical use cases → Ratio Analysts to Analytics Engineers of 3:1 - 2:1
• 1 Data Engineer / ML Engineer = data person who provides the technical platform for the analytics engineers and data scientists → Ratio Analytics Engineers to Data Engineers of 2:1 - 1.5 - 1
• 1-2 Data Product Managers = data person who acts as the glue between analytics engineers and analysts or between analysts and super users, depending on how the team is structured (View Highlight)
about data needs.
A 70-person consumer startup might need more data support than a 300-person B2B enterprise.
Indicators of high complexity:
• Multiple acquisition channels
• High customer volume
• Advanced funnel tracking
• Ongoing experimentation
• Pricing and LTV optimization (View Highlight)
Companies with M&A history or fragmented orgs often have multiple data cultures and stacks.
This means:
• Parallel systems that haven’t been merged
• Different teams using different definitions
• Historical teams who are still deeply embedded and politically untouchable
In these cases, even if you should be able to consolidate, the reality is messier. You’ll likely carry a higher headcount, at least temporarily. (View Highlight)
build from the ground up:
Count the real data users in your company
Define Roles
Apply Ratios per role
Adjust the ratios based on:
• Technical debt & Foundational Strength
• Business complexity
• Organizational legacy
• AI tooling maturity (View Highlight)