Analytics consulting for complex decisions
Turn complex data into better decisions.
I help organizations frame difficult questions, build trustworthy analysis, create decision-support tools, and modernize the data systems and workflows behind them.
My work spans analytics strategy, statistical modeling, applied research, dashboards and R/Shiny applications, data architecture, workflow automation, and practical AI-assisted analytics.
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Data & Analytics Consultant · Utah
POINTRELATIONSYSTEMDECISION
Analytics should help someone decide what to do next.
The useful part of analytics is not the model, dashboard, database, or AI workflow by itself. It is the clearer decision, more reliable process, or better-designed system that those tools make possible.
I work with organizations that have important questions, fragmented data, recurring manual work, or analytic products that need to become more useful, repeatable, and maintainable.
Five consulting practices, combined as the problem requires
Analytics Strategy & Solution Design
Clarify the decision, stakeholders, data requirements, analytic approach, and implementation path before investing in the wrong solution.
Analysis, Research & Modeling
Answer consequential questions with statistical analysis, research design, evaluation, workforce and policy analytics, and transparent modeling.
Dashboards & Decision Support
Build tools people can actually use—from executive reporting and interactive visualization to custom R/Shiny applications.
Data Systems & Automation
Create cleaner pipelines, reproducible workflows, practical data architecture, and automated recurring analytics.
Analytics Modernization
Move from brittle spreadsheets, disconnected scripts, and manual reporting toward maintainable systems and AI-assisted workflows.
Experience across public analytics, consulting, and data products
Reusable client dashboard framework
Led development of reusable R/Shiny components, standardized data preparation, validation workflows, and deployment patterns for recurring client analytics.
Workforce projection systems
Led and supported multi-source supply-and-demand modeling used by state agencies, associations, and healthcare organizations for planning and policy.
Practical analytics architecture
Evaluated lightweight approaches using R, SQL, DuckDB, object storage, documentation, and validation to make recurring analytics easier to maintain.
The tool comes after the problem.
01 · Start with the decision
Define what someone needs to know, decide, or change before deciding what to build.
02 · Design with stakeholders
Understand how people work, what they trust, and what will make a solution usable in practice.
03 · Build for repeatability
Prefer transparent, documented workflows that can be validated, updated, and handed off.
04 · Make implementation part of the work
A technically correct solution that never gets adopted is not a successful analytics project.
Have a data problem that does not fit neatly into a job description?
That is often the right kind of consulting problem. We can start with the decision you are trying to make and work backward to the analysis, product, or system you actually need.