Ian Parent
Messyoperationaldata.Cleandecisions.
Analytics architecture, AI-native workflows, and the patterns between them.
Five engagements. Five different patterns seen.
Each one started with fragmented data and ended with leadership-grade infrastructure. Select a node to explore the pattern.
Finance and CS leadership had no single source of truth during peak season. Decisions came from stale Excel exports pulled from three disconnected systems.
The data existed — it just hadn't been mapped. Three source systems, no defined grain, no conformed dimensions. The gap wasn't technical; it was architectural.
Finance Scorecard with rolling 7-day and YTD variance vs. Plan/CY24/CY25. Customer Success Executive Summary with 6 time-horizon comparisons, sparkline trends, and WoW/MoM/YoY signal. Semantic layer in Snowflake. Genesys→Five9 migration with full schema mapping and YoY bridging.
200+ production dashboards across the organization. 22% marketing lift. 15% retention improvement. 30% increase in traffic analysis accuracy.
BI craft. AI speed. Non-linear pattern recognition.
Three layers that compound into something the market doesn't have a clean name for yet.
Cross-domain synthesis — telephony, finance, product, CRM, operations — seeing the architecture beneath the problem before most teams finish writing the requirements doc.
AI baked into every stage of the analytics workflow — SQL generation, documentation automation, dashboard scaffolding, stakeholder prep. 3–5× faster delivery without sacrificing structure.
Twenty years of enterprise BI — semantic models, star schemas, KPI frameworks, data dictionaries — the foundation that outlasts the person who built it.
The thinking behind the dashboards.
Proof over prose. Hover, click, and explore the models and patterns beneath the dashboards.
The throughline is pattern recognition.
A career that doesn't fit neatly into one lane — which is the point.
Eight years across pharma, telecom, automotive, and inside sales — learning how decisions actually get made and what data operational teams actually need.
CT International and Mindbody — first SQL queries, first dashboards, first time turning operational chaos into something a VP could read.
Cal Poly Mathematics. Published astronomy research (JDSO) — found systematic error in Heintz's published values, invited to collaborate with Naval Observatory. Pattern recognition as a discipline.
Sylvan Road Capital — entire BI function from zero. Power BI, ML models, data catalog, KPI frameworks. Employee Innovator Award. Then Cal Poly Facilities — Power BI semantic model over a locked datalake.
Taxwell (TaxAct) — 200+ production dashboards, Genesys→Five9 migration, Snowflake/dbt modernization, AI baked into every layer of the analytics workflow.
This portfolio was rebuilt eight times — not because the work wasn't good enough, but because nothing shipped until it fully reflected the thinking behind it.
San Luis Obispo, CA — Remote. Deep work, asynchronous communication, outcomes over office theater.
Parachute in. Build the right foundation. Leave the team better.
Available for select fractional and contract engagements — typically 4–6 month transformation projects where data infrastructure needs to move fast without cutting corners.
Semantic model design, star schema, data dictionaries, KPI standardization, and cross-system field mapping. The foundation that makes everything else trustworthy.
Executive scorecards, operational dashboards, and self-serve reporting — built on a properly modeled data layer with documented logic and refresh schedules.
Audit an analytics workflow, identify the mechanical bottlenecks, redesign it around AI tooling. Measurably faster delivery with the same (or higher) quality bar.
Building or modernizing a data function? Need to move faster than the current team can? Let's find out if there's a fit.
Exploring the right conversation.
Analytics architecture, data modernization, AI-augmented workflow design. No intake forms — just a direct email.