Data foundations consultancy
One number.
Everyone trusts it.
Canonica Data builds the pipelines, models, and reporting foundations that make your company's numbers reliable, so meetings are spent making decisions instead of debating whose spreadsheet is right.
10+ hrs
of weekly manual reporting eliminated for a prior client by replacing spreadsheet exports with an automated pipeline.
1
agreed definition per metric, set once during discovery and reused across every report and model.
0
handoffs. You work directly with the person building your system, start to finish.
Does this sound familiar?
Four symptoms, one root cause
Slow reporting, conflicting numbers, distrusted dashboards, and hallucinating AI tools usually trace back to the same thing: the data underneath was never built to be relied on.
Two teams present two different revenue numbers.
Each department built its own definition over time.
Analytics engineering
Someone spends every Monday rebuilding the same report.
The data still moves by manual export.
Data engineering
Leadership doesn't trust the dashboard anymore.
It was built before anyone agreed what it should measure.
Data strategy
Your AI tools give confident, wrong answers.
They inherited unclear definitions and disconnected systems.
AI-ready data
Services
How that gets fixed
Data engineering
The pipeline that moves information from your tools into one reliable place automatically. No more manual spreadsheet exports.
Analytics engineering
Raw data turned into agreed business definitions. One version of revenue, one version of active customer, used everywhere.
Data strategy
Work with your team before any building starts, surfacing the gaps and disagreements that cause bad numbers later.
AI-ready data
Your data prepared so AI tools and assistants give grounded, accurate answers instead of inventing them.
Process
Discovery before a single table gets built
01
Discovery conversation
A structured conversation about how your business actually defines success, revenue, growth, and engagement, before anything is assumed.
02
Align on definitions
Surface the disagreements hiding in plain sight, the kind that cause two departments to report two different numbers.
03
Build the foundation
Pipelines, models, and dashboards built to those agreed definitions. Tested, documented, and built to last past the first demo.
04
Hand off with confidence
Clear documentation and a system your team can maintain, not a black box only one person understands.
Who you work with
You work directly with the person building your system.
Canonica Data is led by Devin Meunier, a data professional who spent years working with the realities of business reporting before moving deeper into data engineering. That shapes every project: understand the business first, then build the technology around it.
Every engagement is hands-on from start to finish. No account manager between you and the work, no junior team learning on your project, no handoffs.
Automates the reporting nobody wants to do by hand
Manual spreadsheet exports replaced with pipelines that run unattended, freeing that time for actual analysis.
Builds systems that hold up after the handoff
Every pipeline is tested and documented, so your team can maintain it without the person who built it on call.
Translates between the business and the data
Sits in the room for the "what does revenue actually mean" conversation, then builds the model that reflects the answer.
Technology
The right stack for the problem
You don't need to know what any of this means. We're stack-agnostic and choose tools that fit your existing environment.
Common questions
What organizations ask before fixing their foundation
Resources
Practical thinking about data foundations
Clear explanations of the ideas behind reliable data systems, from semantic layers and dimensional modeling to canonical data.
DATA STRATEGY · SEMANTIC LAYER
What Is a Semantic Layer, and Why Should You Care?
A practical explanation of how shared business definitions make metrics consistent across reporting, analytics, and AI.
9 MIN READ
DATA MODELING · DATA ENGINEERING
Dimensional Modeling Explained
Why grain, conformed dimensions, and historical modeling matter when building data systems people can actually trust.
9 MIN READ
DATA STRATEGY · DATA FOUNDATIONS
Canonical Data Explained
What it means to establish one trusted definition of a metric or business entity across an organization.
8 MIN READ
Start with the problem, not the dashboard.
A short discovery conversation, no obligation to build anything.