Press Release - Toronto Analytics Expert Releases New Business Case Demonstrating the Power of the IQR Rule in Modern Data Strategy

 

FOR IMMEDIATE RELEASE

Toronto Analytics Expert Releases New Business Case Demonstrating the Power of the IQR Rule in Modern Data Strategy

Toronto, ON — January, 20 2026 — Toronto‑based analytics professional and educator Tom Wolfer has released a comprehensive new business case demonstrating how the 1.5×IQR statistical outlier rule can dramatically improve data quality, operational efficiency, and decision‑making across business and public‑sector environments.

The business case, now published on Tom’s Interactive Statistics Education Add‑In platform, outlines how organizations can use the IQR rule to detect anomalies in financial, operational, customer, and civic datasets — without relying on complex machine‑learning models or costly new systems.

“Organizations are overwhelmed by data, but they’re still missing the anomalies that matter most,” said Tom. “The IQR rule gives analysts and leaders a simple, transparent, and scalable way to identify unusual values that distort KPIs, forecasts, and operational planning. It’s one of the highest‑ROI tools available because it works anywhere — Excel, Power Query, SQL, Python — and it’s easy for teams to adopt.”

The business case highlights real‑world applications across multiple sectors:

  • Financial analytics: detecting abnormal expenses, revenue spikes, and vendor irregularities

  • Operations and supply chain: identifying outlier delivery times, production delays, and equipment anomalies

  • Customer analytics: flagging unusual churn patterns, fraud indicators, and high‑value segments

  • Civic and public‑sector analytics: improving service planning, infrastructure monitoring, and resource allocation — informed by Tom’s work analyzing Toronto’s 311 and urban safety data

The release also emphasizes the growing role of AI in interpreting outliers and accelerating insight generation. “AI doesn’t replace the IQR rule — it amplifies it,” Tom explained. “Once anomalies are identified, AI can help explain what they mean and what actions leaders should consider next.”

The new business case is part of Tom’s ongoing effort to bridge statistical education, practical analytics, and civic data literacy in Toronto and beyond. His two blogs — We Protect T.O.R.O.N.T.O and the Interactive Statistics Education Add‑In — collectively reach analysts, educators, planners, and public‑sector professionals across Canada.

The full business case is available now at: https://interactivestatisticseducationaddin.blogspot.com/p/business-case-applying-iqr-rule-in.html

About Tom

Tom is a Toronto‑based senior analytics and business intelligence professional specializing in scalable workflow automation, scenario modeling, and data‑driven decision support. He is also an educator and technical communicator focused on making analytics accessible to both business and civic audiences. His work spans financial analysis, public‑sector data, and the development of interactive tools for analysts and students.

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