01Leading data teams
Building a data function from zero
Building a central data function from zero: org design, hiring, and centralizing scattered analysts without becoming the bottleneck.
- Org design
- Hiring
- Platform strategy
Anil Thapa
Data & AI Platform Leader
Both halves of the data problem.
The platform, and the decisions it is supposed to serve.
For anyone paying for a data platform, or building a career on one.
- The platform
- It has to produce numbers that agree wherever they appear, from pipelines proven before anyone relies on them, at a cost someone can explain. AI raises the stakes: an assistant answers from whatever the platform holds.
- The people and decisions around it
- Those numbers still change nothing until a decision turns on them. That depends on who owns each definition, whether the data team is asked before the decision or after it, and whether its recommendations get adopted.
A platform is a cost until it changes a decision, and a career in data grows the same way.
- Built from zero
- Central data functions, hired and structured from scratch
- In the room
- Analysis that reaches the decision while it is still open
- Hands on
- Still in the query plan when that is what the problem needs
- The tradeoff, written down
- Every case study names what its decision cost
Selected case studies
All case studies02Data in the business
Data in the boardroom
What changes when data reaches the executive table: leading with the decision, showing uncertainty, and keeping the seat.
- Exec partnership
- Decision support
- Stakeholder alignment
03Agentic & AI systems
What the assistant was allowed to know
Launching an AI analyst against a real warehouse: metrics as tools, a knowledge base of business context, and a feedback loop that became the eval set.
- Architecture
- Governance
- Enablement
04Trust & governance
Earning trust in the numbers
Protecting trust in data: prioritize critical metrics, detect silent failures, and make the incident response visible to the people affected.
- Data quality
- Governance
- Stakeholder alignment
What I write about
Agentic & AI systems What actually happens when you put agents and LLMs to work inside a real data organization, and what the demos leave out.1 case study · 3 essaysLeading data teams Hiring, structure, capacity and the stakeholder work nobody puts on a roadmap. Building a function, not just a backlog.2 case studies · 3 essaysData in the business Executive partnership, strategy, and the difference between reporting on decisions and being present while they are made.1 case study · 5 essaysTrust & governance Quality, definitions, lineage and incidents. How to make reliability visible and rebuild confidence when something goes wrong.1 case study · 1 essayArchitecture & economics Platform decisions with the reasoning shown, including what they cost to run and the tradeoffs I accepted.3 case studies · 1 essay
Recent essays
All essaysAgentic & AI systems
The dashboard stopped one step short
Dashboards show the number and stop. AI briefings add the reason and a next step. How to keep the numbers governed and the reasons checkable.
Data in the business
Recommendations that go nowhere
Why sound analysis produces recommendations nobody acts on, and the rewrite that fixes it: name the decision, offer an alternative, state a condition.
Leading data teams
Too many priorities, none of them yours
A data team whose roadmap is other people’s requests needs a remit, not a better scoring model: one sentence, used to decline one thing.
Agentic & AI systems
An analyst in the chat window
Rolling out an AI data assistant in the team chat tool: why to start narrow, recruit people to break it, and what it exposes about the questions.
Talks and podcasts
Every case study and essay here is a talk I can give, on a stage or a podcast. The contact page says what an invitation should include.