Data Quality · Governance · AI Integrity
Garbage In, Garbage Out is one of the oldest principles in computing.
It is also the least respected — at the moment when its consequences are largest.
GIGO Data™ works with organisations that cannot afford to discover the problem
after the system has already run.
"Before GIGO Data™ had a name, it had a problem — and I had been watching it accumulate for forty-five years."
Adrian Wise Santos — Founder, GIGO Data™01 — What We Do
Most data problems are discovered after the fact — in a meeting, in a report, in a costly resolution. GIGO Data™ works upstream: on the intake, the structure, and the governance frameworks that determine whether a system's outputs can be trusted before they are acted upon.
This is not data cleanup. This is data architecture for accountability — the design discipline that sits between your organisation's questions and its systems' answers.
The work applies across AI governance, enterprise platforms, regulatory compliance, and any environment where the cost of being confidently wrong is one the organisation cannot afford.
Coined in 1957. Confirmed on every ramp, trading floor, enterprise platform, and AI deployment since.
In 1957, US Army Specialist William D. Mellin explained to a syndicated newspaper that computers cannot think for themselves — that sloppily programmed inputs inevitably lead to incorrect outputs. The phrase he reached for was Garbage In, Garbage Out.
The principle has never failed. Systems have failed to apply it. The failure mode is consistent: inputs are accepted without validation, scaled without accountability, and the consequences accumulate in the data until they surface somewhere expensive.
In the age of AI, the stakes are no longer operational. They are reputational, regulatory, and consequential for the people whose lives the systems govern.
Governance is not the audit that happens after the output. It is the design discipline that determines whether the output is trustworthy in the first place.
02 — The Pattern
03 — The Intake Standard
These are not philosophical questions. They are operational governance standards for any system that shapes access, opportunity, or consequence for real people. A system that cannot answer them has no business making such decisions.
04 — Founder
"What I was doing, during the years between the observations and this series, was stress-testing the frame — carrying it across different conditions, continents, domains, and disciplines — and asking whether it held."
The lens came from forty-five years of watching the same structural failure repeat across aviation, financial markets, consumer technology, and European governance — and asking, in each domain, the same question: how do you know that what went in was fit for purpose?
At America West, bad inputs surfaced on the ramp, in real time, with an aircraft that needed to push back in six minutes. At Wells Fargo, the same failure was denominated in dollars, francs, and deutschmarks. At Apple, the AntennaGate resolution in three days instead of six weeks was not a speed story. It was a data quality story.
In Paris, a different regulatory culture asked the question at a systemic level: the state, the institution, the organisation — these owed the individual legibility. Opacity required justification. What GDPR would later formalise was already the cultural contract.
GIGO Data™ was founded on the conviction that this standard — built across enough domains, over enough time, to be stated with precision — belongs in the room where AI systems are designed, deployed, and governed.
05 — I Have a Voice
06 — Contact
If your organisation is navigating AI governance, data quality accountability, or regulatory compliance — and needs a perspective built from forty-five years of watching what breaks — this is where that conversation begins.
San Francisco, CA · gigodata.com
