Jerry R. Napier

Information governance · Data analytics · Workflow automation

I turn complex systems into trusted ones.

I build data tools, reporting, and automation around the people using them—with clear rules, traceable evidence, and practical next steps.

View selected work Connect on LinkedIn

Open to information governance, data analytics, business intelligence, and operations opportunities

Professional portrait of Jerry R. Napier
Jerry R. Napier Information Governance & Data Operations
10+years across information & operations
Built for confident change
See the evidence

01 · Evidence in context

From fragmented project data to one decision-ready view.

To show how scattered project data can support clearer decisions, I built an account-analysis view using an anonymized synthetic test sample: 54 projects and $60.2M in modeled spend. The model highlights $20.2M in potential savings for further review—not realized savings.

54

sample projects organized into one account view

$60.2M

modeled spend structured for review

$20.2M

potential savings identified by the model, not realized savings

Anonymized synthetic test sampleDocumented business rulesRow-level traceabilityExecutive-ready review

Review the full account-analysis case study

02 · Selected systems

Work you can inspect.

The interactive samples need JavaScript. You can still read the case notes and explore every project below.

Each build connects a business problem to a usable tool—with clear rules, visible evidence, and accountable next steps.

DATA CONTRACT / 01 Public alpha · 0.3.4
01

Data quality · Python · CI

Data Contract Monitor

A data-quality engine that turns business expectations into repeatable checks. Exact cross-file validation, readable reports, and run comparisons help teams catch unreliable inputs and explain what changed before those inputs reach reports or models.

  • Exact cross-file checks
  • Run-to-run comparison
  • One shared rule engine
Open case note
Need
Catch quiet data failures before they become reporting, modeling, or operational problems.
What I built
A local-first validator for CSV, Excel, JSON, JSON Lines, and optional Parquet. One rule engine serves the dashboard, command line, API, package, and GitHub Action; streaming keeps supported checks exact across batches.
Result
Compare runs and contract versions, trace findings to readable rules, and retain the same evidence across local review and CI. A failed data contract stays distinct from a program failure.
Proof
The v0.3.4 source qualification records 71 passing tests. A documented Linux benchmark exercised exact streaming checks on a synthetic one-million-row CSV; its timing is specific to that environment.

Public alpha v0.3.4 · Synthetic demos · Trusted-workstation dashboard · Privacy signals require human review · Native Windows and endpoint-protection qualification remain separate release checks.

Explore Data Contract Monitor
BOTOPS / BROWSER DEMO Ready

Systems overviewReady for review

Simulated review · no access to your computer

Launcher auditReady

Health evidenceReady

Support exportReady

Browser-only sample

Python 3.10+ · Standard library · Windows

Ready to review three simulated operational signals.

02

Operations engineering · Python

BotOps Manager

A Windows operations console for independent automations. It brings health signals, launcher checks, and support evidence into one view, then rechecks project and process identity before approved actions—keeping control precise and recovery reviewable.

  • Project-scoped control
  • Identity checked at action
  • Redacted support exports
Open case note
Need
Make separate local automations easier to oversee while keeping each project’s controls clearly defined.
What I built
A Python console that brings launcher reviews, health signals, verified project controls, and redacted support reports together.
Result
Operators can review status and supporting evidence in one place. BotOps rechecks project identity before an approved action.
Proof
The repository documents the controls and includes repeatable tests using sample projects and simulated process records.

Python 3.10+ · Windows · Standard library only · No cloud service or installer required.

Explore BotOps Manager
CHI_ANALYSIS / 0313,333 records
Recorded failure share2010–2018
Highest Risk 1 share among classified60607 · 82.70%
03

Data analysis · Reproducibility

Chicago Food Inspection Outcomes

A reproducible analysis of 13,333 public inspection records. Separating inspection outcomes from facility-risk classifications reveals why the same data can lead to different conclusions—and gives reviewers a clear, traceable basis for follow-up.

  • Explicit denominators
  • Reproducible analysis
  • Documented sources
Open case note
Need
Compare inspection outcomes and facility risk across three Chicago ZIP codes while keeping conclusions aligned with the study’s descriptive scope.
What I built
A bounded official-data snapshot, explicit study parameters, validation checks, a reproducible notebook, and a documented refresh path.
Result
The analysis shows why outcome and facility-risk measures can lead to different follow-up questions.
Proof
60622 has the highest recorded failure share at 21.54%; 60607 has the highest Risk 1 share at 82.70% among classified records. Offline tests verify the data query and date boundaries.

2010–2018 study period; source snapshot retrieved in July 2026. Records represent inspections, not unique restaurants. Risk 1 is a facility classification, not a failed inspection. This study does not rank restaurant safety.

Review the analysis
operations-evidence.log
04

Operations intelligence · Analytics

Operations Intelligence & Automation Platform

An operations platform that connects validated data to explainable findings, scenario planning, and accountable follow-up. Assumptions stay visible, blocking data defects stay out of trusted measures, and each improvement can be tied to an owner.

  • Validated measures
  • Explainable scenarios
  • Accountable follow-up
Open case note
Need
Bring separate trackers, dashboards, alerts, and case queues into a shared view where definitions, evidence, and ownership remain clear.
What I built
A service-operations workspace with validation, process analysis, six-week backlog scenarios, owned cases, playbooks, and outcome tracking. Blocking defects remain visible but are excluded from trusted KPIs.
Result
Review the evidence behind a finding, compare capacity assumptions, and turn a proposed improvement into assigned work with a recorded outcome.
Proof
The public v0.3.1 case study records 33 application tests, 32 platform checks, and 40 HTTP checks. Synthetic demonstrations expose scenario assumptions, uncertainty, and a held-out backtest.

Public source v0.3.1 · Synthetic demonstration · Findings are associative, not causal proof · Demo authentication · External operational write-back disabled by default.

Explore the operations intelligence source
GOVERNED CATALOG / 05Public case study
DefinitionsOwnershipEvidenceImpact
05

Data governance · Lineage

Data Governance & Lineage Portal

A connected catalog for data meaning, ownership, quality, and lineage. Trace a business definition through source fields and dependent reports, then review what a proposed change could affect—with the supporting evidence in view.

  • Business-to-field context
  • Visible change impact
  • Explainable trust
Open case note
Need
Help owners, stewards, analysts, and technical teams answer the same questions about meaning, trust, ownership, and impact.
What I built
A local-first product that brings catalog records, glossary terms, classifications, quality controls, source fields, lineage, impact analysis, and permission-aware change review into one shared model.
Result
Users can trace a governed concept from its definition and accountable people through source fields, transformations, consuming reports, quality evidence, and recent changes.
Proof
The public v0.3.0 case study records 84 source tests, 140 managed-file checks, and a field-confirmed Windows-working release. These are documented qualification results, not a claim of production deployment.

Public case study · Version 0.3.0 historical qualification · Synthetic examples · Proprietary implementation and operational records remain private · Enterprise deployment is not claimed.

Review the governance portal case study

03 · Operating model

Build confidence from intake to outcome.

My builds combine business context with operator discipline: clear requirements, visible exceptions, explainable results, and a practical path to recovery.

Explore the operating model

Confidence at
every step.

Trustworthy system operating model A four-step path connecting govern, observe, verify, and sustain. 01Govern 02Observe 03Verify 04Sustain

01 / Clear rules before automation

Agree on the information required, where it comes from, and who reviews it before work begins.

Why it mattersEveryone starts with shared expectations and clear responsibilities.

StandardsProvenanceOwnership

Working principles: governed, observable, adaptable, and human-centered.

04 · Experience

Builder range.
Operator discipline.

My career began in film and post-production, where the right information has to reach the right people at the right time. I bring that same care to data governance, reporting, and automation—connecting the details to the people who rely on them.

2018 — Present

Gateway Information Group · Consultant

Help clients improve information governance, reporting, and everyday processes. Build process maps, practical procedures, and Excel- and SQL-supported tools that make responsibilities clearer and information easier to use.

2022 — 2025

Visa · Marketing Manager, Production Operations

Built a production database and Power BI reporting to track agency work, spending, and deliverables. Established naming, classification, and metadata standards that made production records easier to find, reconcile, and report on.

Earlier career

Advertising Production Resources (APR) · The Digital Archive Group · Amazon · Harpo Productions

Delivered financial, asset, and operational reporting; managed database and metadata operations; and supported migrations, training, and cross-functional project delivery.

05 · Foundation

Creative roots.
Information rigor.

Creative production and information management come together in how I organize information, design clear experiences, and support the people using them.

2019

M.S. in Information Management

Management Information Systems · Dominican University

2019

Certificate in Digital Curation

Dominican University

2006

B.A. in Film / Video

Post-Production Management · Columbia College Chicago

View résumé (PDF)

A small gift for curious builders

Two projects. One hands-on lesson.

I’m sharing these learning projects for builders who enjoy seeing an idea take shape. They explore complementary sides of a hypothetical trade: evaluating a 1¢ entry from a sample market snapshot and modeling a partial sale at 2¢. Both public projects are read-only, so this is a learning pair—not a live-trading system.

01 / BUY Kalshi 10 × 1¢ Planner

Checks a sanitized market snapshot for exactly ten contracts at 1¢ each—10¢ before modeled fees. Stale, incomplete, or conflicting evidence prevents a plan. The public source is offline, with no credential support or order authority.

Review the buy project 02 / SELL Kalshi 15-minute 2¢ Sell Preview

Models a 40% exit target at exactly 2¢ from a sanitized position snapshot. Fee, route, and current-status checks gate each plan. Offline and read-only; it cannot connect to Kalshi or place orders.

Explore the sell preview

A worked example · before fees

Recover part of the entry cost. Keep a residual position.

Suppose ten contracts were bought at 1¢ each. Selling four at 2¢ would return 8¢ and leave six open. Selling five would return the original 10¢ before fees. Only the 40% target is modeled by the public sell preview; 50% and 60% are comparison examples, not selectable modes or expected returns.

40%8¢ gross6 remain

50%10¢ gross5 remain

60%12¢ gross4 remain

Educational, MIT-licensed projects—not a performance promise. Both projects are offline planning tools with no network client, credential support, or order authority. Examples assume completed 2¢ fills before fees. Liquidity, fees, and settlement affect results, and retained contracts remain at risk. Review current eligibility and platform terms before use. Independent projects; not affiliated with or endorsed by Kalshi.

06 · Start a conversation

Make the next system easier to trust.

If your team needs stronger data quality, clearer governance, more reliable reporting, or smoother operational workflows, I’d be glad to talk.

jerryrnapier@gmail.com
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