Building without a diagnosis is guessing
We assess 6 dimensions of your data infrastructure and tell you, with evidence, how ready your company is to scale.
What almost no one diagnoses before investing
The symptom is never the real problem
Dashboards nobody uses
The visualization tool was built before fixing the quality of the data feeding it. The result is a pretty report nobody checks because nobody trusts the numbers it shows.
Every area has its own version of the truth
There's no single source of truth, so meetings turn into debates over which number is correct instead of conversations about what decision to make.
AI projects that fail silently
Investment goes into models or automation on top of data that was never ready to support them — and the project fails because of the foundation, not the technology.
Nobody really knows how bad the problem is
There's a general sense that "the data is a mess," but no objective evidence of where the problem is or how urgent it is to fix.
6 dimensions of your data operation
It's not opinion, it's evidence
Data sources
The origin and reliability of where the information comes from.
Infrastructure
How information is stored and moved today.
Quality and consistency
How reliable and clean the data is.
Governance and access
Who controls what, and under what rules.
Consumption and visibility
How dashboards and reports are used today.
Team and documentation
How prepared the internal team is to sustain all of this.
What you get
An action plan, not a PDF of opinions
An executive report with scoring — 0 to 10 on each dimension, with critical blockers identified.
A technical remediation plan — what to fix first and why, with owners assigned.
A visual roadmap for the First Build Cycle — what to build first, in an executive format for the board.
Proof of rigor: DAMA-DMBOK
You're not hiring a technical opinion. You're hiring an international data management standard, adapted to the Central American reality.
Zero-friction logistics
A structured process, with focused interviews and workshops — not an ongoing audit.
No invasive access to critical systems — we work with existing documentation and direct conversation with your team.
Who this is for
Does this sound like you?
CTO / Head of Data
Suspects there are deeper problems but has no objective evidence to justify a bigger investment.
COO
Wants to invest in analytics or AI, but needs to know first whether the foundation can support it.
A company that already tried a data project and it failed
Wants to understand what went wrong before trying again.