Business Intelligence Dashboards
Interactive, decision-ready views for sales, ops, and leadership — hosted where stakeholders can actually use them.
Work samples · Selected work
Portfolio
BI systems and operational programs I’ve built — from Justified Analytics (founder practice) to construction analytics, labor forecasting, inventory control, and process automation.
01
Justified Analytics
A BI practice I built — data that works as hard as you do. Historical offerings and delivery model below.
Practice I built · founder-led BI
Custom dashboards, reconciliation workflows, Excel solutions, and data repositories for teams that need clarity without enterprise bloat. Delivery: connect data → define vision (30 min) → build in 1–2 weeks for starter engagements.
Services (Justified Analytics positioning)
Data Reconciliation
Align source systems, clear mismatches, and make the numbers trustworthy before they hit the board deck.
Custom Excel Solutions
Models, automation, and operator-friendly workbooks when a full BI stack isn’t the right first move.
Data Repository Management
Durable structure for sources, refreshes, and ownership so reporting doesn’t restart from scratch every quarter.
Selected practice work (demos / sample layouts — not client-confidential metrics)
Practice demo
Sales & Ops Dashboard — Director
Director-level sales and operations view: YTD invoicing vs target, brand mix, order book, and opportunity pipeline in one glance.
Practice demo
Financial Dashboard — Manager
Manager-facing financial and performance layout for tracking targets, variance, and operational health without drowning in tabs.
Packages (historical JA offering)
- 1 dashboard
- Up to 2 sources
- 1–2 weeks
- 2–3 dashboards
- Up to 5 sources
- 3–4 weeks
- Unlimited dashboards/sources
- Advanced analytics
- 6–8 weeks
- Ongoing support
- Iteration & refresh
- Priority access
BOT
Intake GPT — lead qualification
Conversational intake → package recommendation → summary routed to Justin. Built to qualify leads without a sales funnel tax.
02
Selected case studies
Problem → approach → outcome from career roles across construction BI, ops planning, automation, and inventory.
Illustrative layout
PG&E / Evergreen · Current
Construction BI
- Problem
- Paving / construction ops needed decision-ready visibility across daily work, managers, and cycle time — plus cleaner invoice reconciliation.
- Approach
- Primary BI for WOR/DOR reporting, Construction Manager Dashboard, production / cycle-time analytics, and the PgClean invoice reconciliation tool.
- Outcome
- Analytical frameworks that support operational and cost-related decision-making for PG&E paving operations.
Illustrative layout
Peaxy
BI dashboards for labor cost
- Problem
- Operations leadership needed clearer labor visibility to control cost without slowing delivery.
- Approach
- Partnered with VP of Operations and CEO; built BI dashboards and visualization for large operational datasets.
- Outcome
- Dashboards that contributed to a 15% labor cost reduction.
Illustrative layout
Stuart Event Rentals
Weekly Ops Planning + forecasting
- Problem
- Warehouse, field, and transportation labor needed a repeatable planning rhythm and forecast discipline.
- Approach
- Built and ran a Weekly Ops Planning System; automated labor forecasting and developed a structural load-list calculator.
- Outcome
- 35% average monthly labor cost reduction.
Illustrative layout
MSC · MsClean
VBA automation for vessel reconciliation
- Problem
- Load-list errors in vessel reconciliation workflows were driving costly rework.
- Approach
- Built MsClean VB automation for vessel reconciliation; led Excel help desk and training around the new workflow.
- Outcome
- 88% load-list error reduction (~$2.3M annual savings).
Illustrative layout
CEVA Logistics · Tesla contract
Inventory accuracy & variance control
- Problem
- Inventory accuracy and monthly variance were undermining reliable supply for Tesla operations.
- Approach
- Led ops with 16 direct reports and 250+ hourly associates; drove Six Sigma Kaizen efforts focused on accuracy and variance.
- Outcome
- Inventory accuracy from 75% to 99%; variance to <$5k/mo; maintained zero line-down for Tesla.
