What frameworks or suggestions would you use/give to someone who is interested in assessing cost-benefit ratios for analytics, AI/ML, or data science initiatives?
Do all C-suite leaders in your organization wield similar levels of authority, or is there variance between traditional C-level roles (CIO, CFO, CHRO) and newer, specialized positions (CDO, CAIO, etc)?
All C-suite leaders have similar authority level33%
Authority levels differ between traditional C-level roles and the newer ones54%
There is a variety of authority levels among the C-level roles, but it is not directly related to traditional vs newer roles12%
ROI Framework: Establish a clear Return on Investment (ROI) framework with well-defined KPIs for each initiative.
Cost Tracking: Maintain meticulous cost records, including data acquisition, infrastructure, talent, and ongoing maintenance.
Benefits Mapping: Quantify benefits like revenue increase, cost reduction, or efficiency gains attributed to analytics, AI/ML, or data science.
Risk Assessment: Evaluate potential risks and their financial implications, factoring in model inaccuracies, data quality issues, and compliance.
Scenario Analysis: Consider multiple scenarios to assess the range of possible outcomes, providing a comprehensive view of cost-benefit ratios.
Benchmarking: Compare your initiative's performance against industry benchmarks and competitors to gauge effectiveness.
Iterative Optimization: Continuously refine your assessment model as you gather more data and insights, ensuring accuracy in cost-benefit analysis.