Director of Strategic Finance

Leading platform selection, defining the enterprise-wide planning architecture, and ensuring the platform meets the strategic finance team's evolving needs. You'll oversee the team's use, not necessarily be in the weeds daily.

Influencing ERP configuration to ensure it provides the necessary data for strategic analysis, partnering with IT to resolve complex data integrity issues, and understanding the underlying data structures for advanced querying.

Defining the BI strategy for the finance function, ensuring executive dashboards deliver the 'so what' to the C-suite, and guiding your team on best practices for data storytelling through visuals.

Partnering with Data Engineering to design schemas and data pipelines that robustly support finance's analytical needs, ensuring data accessibility and quality for strategic models.

Focusing on model review and assumption validation rather than building from scratch. You'll push the team to reduce reliance on spreadsheets where possible, but still understand how to audit and troubleshoot complex models.

Managing the entire board book creation process, ensuring secure communication and governance, and overseeing the timely and accurate submission of all board materials.

• CFO and other C-suite executives (CEO, COO, CTO)
• SVP and VP-level business unit leaders (e.g., Sales, Marketing, Product, Operations)
• Heads of Corporate Development and Investor Relations
• Data Engineering and IT teams

• External auditors and financial advisors
• Investment bankers (for M&A or capital raises)
• Board of Directors (especially the Audit Committee)
• Potential investors or lenders

As businesses grow, the complexity of financial planning outstrips what spreadsheets can handle. You'll need to be the visionary for our EPM system, ensuring it scales with our ambition and provides the insights needed for complex strategic decisions.

Multi-Dimensional Model Design · Integration Strategy · Driver-Based Planning Logic · Performance Optimisation

• This month: Deep dive into the advanced features and capabilities of our current EPM system (e.g., Anaplan).
• Next quarter: Network with EPM solution architects or consultants to understand best practices for enterprise-level deployments.
• Month 4-6: Lead the design of a new module or a significant enhancement within our EPM system, focusing on scalability and user experience.
• Month 7-9: Evaluate alternative EPM solutions in the market, understanding their architectural strengths and weaknesses for future needs.

Quick win: Identify one manual, spreadsheet-based process that could be migrated to our EPM system, and lead your team in building a proof of concept.

Traditional forecasting often relies on historical trends and human judgment. Integrating basic predictive analytics and machine learning (ML) can provide more accurate baseline forecasts, identify subtle patterns, and flag anomalies, giving your team a significant edge.

Time Series Forecasting Models · Regression Analysis for Driver Identification · Anomaly Detection Algorithms · Model Interpretability (Explainable AI)

• This month: Take an online course on basic predictive analytics or time series forecasting (e.g., on Coursera or edX).
• Next quarter: Work with a Data Scientist (if available) to apply a simple ML model to one of your team's existing forecasting challenges.
• Month 4-6: Lead a pilot project to integrate an ML-generated baseline forecast into your LRP process, comparing its accuracy to traditional methods.
• Month 7-9: Develop a framework for when and how your team should use predictive analytics versus traditional driver-based models.

Quick win: Explore the forecasting functions available in Excel or your BI tool (e.g., Power BI's built-in forecasting) to generate a quick predictive baseline for a simple metric.