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Anil Thapa
Anil Thapa

Anil Thapa

Data & AI Platform Leader

Both halves of the data problem.

The platform, and the strategy it is supposed to serve.

For anyone paying for a data platform, or building a career on one.

I’m Anil Thapa. Fifteen-plus years across both: query plans and partition schemes early, then data functions hired from zero, warehouses consolidated out of silos and mergers, and ingestion rebuilt and proven before anyone was asked to trust it.

A platform is a cost until it changes a decision, and a career in data grows the same way: on the decisions you can be trusted with. This is where I share the reasoning from that work, including the mistakes, and offer a way to work through similar decisions together.

Advisory & mentoring What I’ve built Read the writing
Hands on
Still in the query plan when that is what the problem needs
Built from zero
Central data functions, hired and structured from scratch
Executive partnership
Connecting analysis to the decisions people need to make
Judgment
The constraints, tradeoffs, and lessons behind the work

Selected work

The full body of work
01Greenfield data function, analysts embedded across departments

Building a data function from zero

No data team. Engineers working in a vacuum, product managers asking developers for numbers, and every department with its own analyst producing a different answer to the same question. The platform was the straightforward part.

  • Centralize the definitions first, the people second, the tools last
  • Analysts join the centre for governance and stay close to their department
  • Org design
  • Hiring
  • Platform strategy
  • Enablement
Read it
02Executive partnership across departments and at board level

Data in the boardroom

Being in the room is not the same as being useful in it. Most of the work is not C-suite set pieces. It is department heads, one at a time, and the argument is never really about metric definitions.

  • Connect the number to a decision, an alternative, and a condition that would change your view
  • Align on the strategy first; metric definitions are the mechanism, not the point
  • Exec partnership
  • Decision support
  • Stakeholder alignment
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03Data quality and reliability as an operating discipline

Earning trust in the numbers

A plausible wrong number can change how people use an entire platform. Prioritizing critical data, exposing uncertainty, and rebuilding confidence through a response people can inspect.

  • Trust can fall quickly; recovery needs visible evidence of a better process
  • Cover the numbers your most skeptical stakeholder uses, first
  • Data quality
  • Governance
  • Stakeholder alignment
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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.

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Leading data teams

Hiring, structure, capacity and the stakeholder work nobody puts on a roadmap. Building a function, not just a backlog.

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Data in the business

Executive partnership, strategy, and the difference between reporting on decisions and being present while they are made.

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Trust & governance

Quality, definitions, lineage and incidents. How to make reliability visible and rebuild confidence when something goes wrong.

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Architecture & economics

Platform decisions with the reasoning shown, including what they cost to run and the tradeoffs I accepted.

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Recent writing

All writing
Agentic & AI systems

An analyst in the chat window

The obvious place for an AI analyst is where the questions already arrive. Putting one there is easy. What decided whether anyone used it a month later was the size of the first step, who was invited to break it, and what it did to the questions.

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Data in the business

Four habits of a self-service analyst

Self-service analytics was sold as an access problem for twenty years. The missing half was never the seat. It was four habits nobody teaches, none of them technical, and they belong to anyone who has been handed a tool and a question.

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Work through the next decision

Advisory for organizations deciding what their data platform should become, and mentoring for the people who want to shape those decisions rather than supply the numbers for them. Bring a specific platform or team decision, or a growth question you want to work on over time. Invitations for talks, podcasts, and team discussions are welcome too.

Advisory & mentoring Speaking topics hello@anilthapa.com