Finance and accounting professionals striving to be data-driven must distinguish between informed judgment and data overload to improve decision quality and defensibility. David Schlosser, Senior Advisor at Velocity Advisory Group, outlines a decision-first framework that clarifies options, assumptions, and guardrails, emphasizing bias awareness and ?good enough? data aligned to risk. He also highlights accountability, governance clarity, and structured documentation to strengthen outcomes without sacrificing control.
Learning Objectives
- Identify the differences between a data-driven and data-heavy approach, and how a decision-first framework improves clarity and defensibility
- Recognize common cognitive biases that influence professional judgment and indicate strategies to reduce their impact in finance and audit settings
- Distinguish between a good decision and a good outcome, particularly in environments subject to hindsight evaluation and regulatory scrutiny
- Select appropriate success measures, early signals, and guardrails to support sound decisions while maintaining internal control and risk management standards
