Data sources disagree
Definitions, populations, dates, and calculations do not produce a consistent account of what occurred.
Utility, regulatory & complex-data analytics
Defensible analysis for decisions that must withstand review.
Analytics and Forecasting provides founder-led economic, forecasting, data, and model-review consulting for utilities, consulting teams, legal matters, and other data-intensive organizations.
Founder-ledServing clients since 1995San Rafael, CaliforniaRemote and on-site
- 01Source evidenceOperational, regulatory, financial, or discovery records5.5M customers
- 02ReconciliationDefinitions, populations, timing, missing values, and controls11+ years
- 03Model and testingForecasts, scenarios, recalculations, and sensitivity tests8,760 hours
- 04Reviewable resultDocumented findings others can reproduce and explain20-year horizon
Scale figures summarize separate engagements and areas of experience.
Experience measured in systems, records, time horizons, and continued use.
5.5MUtility customers represented in a historical regulatory-data system
11+ yearsRegulatory-data system in continuing use
8,760Hours available for annual load planning
20 yearsLong-term electricity-load forecasting horizon
MillionsVehicle-auction transactions used in production forecasting
When the process is no longer good enough
Recognize the analytical problem before choosing the method.
Engagements often begin when an organization has an important question but cannot yet rely on the data, model, forecast, or process behind the answer.
A critical model is difficult to review
Assumptions are hidden, formulas are fragile, or the process depends on undocumented knowledge.
Planning needs a dependable forecast
Existing methods cannot readily support detailed time patterns, scenarios, updates, or sensitivity testing.
Another analysis must be tested
The data, calculations, methods, or conclusions require disciplined independent review.
Primary services
Analytical services organized around the decision.
Methods are selected after the question, available evidence, required output, and level of review are understood.
Utility and regulatory analytics
Support regulatory reporting, customer and usage analysis, rate questions, program measurement, and data-intensive planning.
Practical result
See what is included Consistent analysis that remains explainable as data and questions change.
Forecasting and scenario analysis
Build forecasts around visible assumptions, operational drivers, alternative scenarios, and practical update procedures.
Practical result
See what is included Forecasts that decision-makers can review, test, and continue to use.
Data reconciliation and analytical systems
Resolve conflicting sources and replace fragile, labor-intensive processes with controlled analytical workflows.
Practical result
See what is included Documented data and systems that produce repeatable results.
Independent model and process review
Examine the data, assumptions, formulas, methods, logic, and outputs behind an important conclusion.
Practical result
See what is included A clear account of what works, what does not, and what needs further attention.
Specialized experience
Utility analytics built for continuity, review, and regulatory use.
Utility work requires more than technical accuracy. Data definitions, historical records, assumptions, calculation methods, and reporting processes must remain consistent and explainable over time.
01
Historical architectureRegulatory-data systems that preserve comparable customer and usage histories.
02
Interval and load analysisPlanning tools capable of representing all 8,760 hours of the year.
03
Long-range forecastingControlled platforms supporting 20-year load forecasts and scenario analysis.
04
Review and reporting controlsReproducible calculations, KPI checks, visible assumptions, and documented outputs.
Meter historyCustomer recordsProgramsWeather
00:0006:0012:0018:0024:00
Historical baseline8,760 hourly values
Scenario horizon20 years
Sources are reconciled before the baseline, scenarios, and documented planning result are produced.
Transferable discipline
The subject matter changes. The analytical discipline transfers.
Utility and regulatory work demands reconciled evidence, controlled calculations, documented assumptions, and results that remain understandable under review. Those requirements recur in other data-intensive settings.
| Analytical method | Utilities & regulation | Economic consulting | Legal & expert review | Finance & transportation |
|---|---|---|---|---|
| Reconcile inconsistent evidence | ● | ● | ● | ● |
| Define populations and measures | ● | ● | ● | ● |
| Build forecasts and scenarios | ● | ● | — | ● |
| Test models and assumptions | ● | ● | ● | ● |
| Create reproducible processes | ● | ● | ● | ● |
| Document review-ready findings | ● | ● | ● | ● |
Transferability means that the analytical methods apply across settings; it does not imply identical subject-matter expertise in every industry.
Selected professional work
Different settings. The same commitment to usable, reviewable analysis.
These examples reflect Joe Barber’s consulting and professional experience. Organization names identify the setting in which the work was performed and do not imply endorsement.
A controlled history for approximately 5.5 million customers.
Built a regulatory-data system that replaced an ad hoc process and improved reproducibility, speed, and accuracy.
- More than 11 years in use
- Historical customer and usage data
- Faster, more reliable analysis
Planning across every hour and a 20-year horizon.
Replaced a manual spreadsheet process with a controlled platform supporting load forecasting for planning and regulatory work.
- All 8,760 hours represented
- 20-year forecast horizon
- Repeatable scenario process
Production forecasts built from millions of auction transactions.
Developed vehicle-value models supporting closed-end lease pricing, contracts, residual-value risk, and portfolio planning.
- Millions of transactions
- Production forecasting models
- Contract and portfolio decisions
Independent model and process review
When another analysis must withstand scrutiny.
Models, damages calculations, expert analyses, reports, and inherited processes can be examined independently for data consistency, methodological soundness, calculation accuracy, sensitivity to assumptions, and reproducibility.
Explore independent review- 01Reported conclusionIdentify the claim and required standard of reviewScope
- 02Source populationReconcile records, definitions, inclusions, and exclusionsVerify
- 03Assumptions and calculationsReproduce formulas, logic, methods, and alternativesTest
- 04Independent findingExplain agreements, differences, uncertainty, and correctionsReport
Founder-led consulting
Senior analytical judgment stays close to the work.
Joe Barber is the founder and principal consultant of Analytics and Forecasting.
His work combines economics, mathematics, forecasting, econometrics, statistical analysis, data architecture, and hands-on system development. He frames the question, works directly with the evidence, builds or reviews the analysis, and documents the result for continued use.
Graduate educationUniversity of Pennsylvania
M.A. in Economics; completed Ph.D. coursework and dissertation research, ABD.
Undergraduate educationWashington University in St. Louis
B.A. with majors in Economics and Mathematics.
RoleFounder and Principal Consultant
Founder-led analytical consulting serving clients since 1995.
Start with the decision
What model, evidence, or analytical process needs attention?
Initial inquiries do not need to contain confidential information. Project details can be discussed after appropriate arrangements are in place.