📈 Data Analytics¶

Data enables visibility, action, and accountability. As a Technical Project Manager, I use analytics to identify delivery risks, expose operational inefficiencies, and improve cross-functional coordination. This page highlights applied analytics across real-world projects and graduate-level training—with a focus on execution, not abstraction.

🛠 Real-World Analytics in Practice¶

✅ Asana AI Reporting for Project Risk¶

• Used Asana’s built-in AI to surface risk indicators, missed deadlines, and task velocity drops across parallel workstreams.
• Automated reports exported for weekly status calls with product owners and technical leads.
• Delivered early warnings and improved sprint closure predictability.

📊 Service Ops Dashboards via Smartsheet¶

• Designed Smartsheet dashboards to visualize NetSuite ticket volume, aging trends, and SLA compliance.
• Enabled Finance, IT, and Ops leaders to triage issues by type and urgency—reducing misalignment and delays.
• Created automated alerts to flag ticket stalling and SLA breaches.

🧾 NetSuite Saved Searches & Reporting¶

• Built and managed saved searches to deliver real-time visibility into finance and operations for 300+ internal and client users.
• Used formula fields to transform raw ERP records into metrics like AP aging, journal mismatch flags, and vendor exception patterns.
• Scheduled report emails and dashboards to reduce ad hoc exports and improve cadence of insight delivery.
• Enabled self-service analytics for Finance and client teams, cutting turnaround time on reporting requests.

📌 Result: Embedded real-time ERP analytics directly into operational decision-making.

🔁 Cross-Platform ETL Automation¶

• Python-based ETL: Automated Box → Vena file ingestion with schema validation and naming convention checks.
• SQL for Ad Hoc Analytics: Queried ERP and operational systems to support reporting needs.
• Power Automate Pipelines:
• Outlook → Smartsheet parsing for onboarding and ticket intake.
• Microsoft Teams post → Smartsheet sync for HR/IT workflows.
• Built structured intake layers to support multi-department collaboration.

📌 Result: Eliminated spreadsheet-driven churn and improved accuracy, traceability, and confidence in cross-platform reporting.

📈 Project Metrics & Governance¶

I embed operational analytics into every program I manage—using data to guide retrospectives, validate assumptions, and surface execution risks early.

🎓 Academic Foundation in Analytics¶

As part of my M.S. in Organizational Leadership, I completed a graduate course in Data-Driven Decision Making, applying real-world forecasting and statistical models to business use cases.

Key Competencies Gained:¶

💊 Capstone Project: PainSmart¶

Objective: Design a workplace wellness strategy to reduce opioid prescriptions through alternative pain management.

• Used public health data to build statistical comparison models.
• Conducted t-tests showing significant improvement (p < 0.05).
• Built regression forecasts with and without the proposed intervention.
• Designed a rollout plan with executive-ready metrics and visualizations.

📁 Workbooks and proposal available upon request.

🔧 Tools & Platforms¶

Here’s the current stack driving my analytics and automation workflows:

💡 Analytics Philosophy¶

“Insight is only valuable when it transforms behavior.”

Whether automating a forecast intake, tracking SLA violations, or guiding leadership through execution risks, my analytics work always drives alignment, clarity, and better outcomes—not just reporting for reporting’s sake.

📥 Workbooks, dashboard snapshots, and code samples available upon request.