Define the outcome.
What should be different when the work is done? What does success actually look like, and what decision are we trying to improve?
DATA · AI · DECISION SYSTEMS
Start with the decision.
I turn complex data, ambiguous business questions, and emerging technology into systems that help people make better decisions.
Define the outcome. Find the constraint. Measure what matters. Simplify the system. Then automate.
PD / 01
PETER DOYLEATLANTA · USA
QUESTIONEVIDENCEDECISIONIMPACT
The technology changes.
The thinking doesn't.
10+Years in data & analytics
$250K+Documented annual business impact
~300%Query efficiency improvement
19SQL conference presentations
POINT OF VIEW
Every metric has to earn its place.
I've seen organizations begin with the dashboard. I prefer to begin with the decision. What are we trying to accomplish? What would tell us we're succeeding? What information would cause someone to act differently? Data becomes valuable when it changes what we do—not simply because we can measure it.
“The goal isn't more data. The goal is greater clarity.”
BEFORE I BUILD ANYTHING
Understand the system before choosing the tool.
I don't begin with a dashboard, a complex query, or an AI solution. I begin by understanding the outcome, the constraint, the evidence, and the people doing the work.
THE RULE
Technology can make a good process dramatically better. It can also make a bad process dramatically faster.
Understand it. Simplify it. Then use automation or AI where it creates measurable leverage.CONTEXT MATTERS
The people closest to the problem are part of the data.
A database can tell me what happened. It does not always tell me why. Some of my most useful work starts with conversations—with operators, analysts, managers, and leaders who each understand a different part of the system.
I want to know what they are seeing, where work gets stuck, which assumptions may be wrong, and what decision they actually need to make. Then the data can test the story.
CONVERSATION → CONTEXT → EVIDENCE → DECISION
SELECTED WORK
Business problems first. Technology second.
SQL, BI, data engineering, visualization, and AI are tools. Their value is measured by what improves because they were used.
01
Fiserv
Turned a backlog into a decision system
Reconstructed an assignment workflow through stakeholder interviews, then built SQL-driven routing logic around certifications, schedules, time zones, completion status, and reassignment rules.
$250K+ estimated annual impact
02
The Home Depot
Reduced processing while improving performance
Refactored legacy SQL and indexing logic while supporting supply-chain network strategy, returns analysis, logistics, store expansion, and delivery optimization.
75% less volume · ~300% higher query efficiency
03
Kaiser Permanente
Measured the process, not just the outcome
Mapped operational workflows and SLAs to expose bottlenecks leaders could act on, including hiring-cycle differences across clinical roles.
Billion-row healthcare & financial datasets
04
ACRIBEX
Building for trust in the AI era
An AI-assisted document-processing architecture focused on source fidelity, structured transformation, validation, lineage, provenance, and defensible downstream analytics.
Applied R&D
SPEAKING & TEACHING
19 presentations. 10 states. One principle: make complexity useful.
I've presented SQL, optimization, data modeling, standardization, and analytics practices at PASS Data Community Summit and SQL Saturday events, while also mentoring analysts on translating business requirements into scalable solutions.
Connect on LinkedIn →
WHAT I'M BUILDING TOWARD
Applied AI and decision systems that work close to the business.
The next generation of valuable technical professionals will move between executives, users, data, systems, and models. They will understand the problem before selecting the technology, identify the real constraint, and build—not merely recommend. The objective is not more AI. It is a better system and a better decision.
LET'S TALK
Have a difficult question hiding inside your data?
Those are usually the interesting ones.