To equip strategists and business leaders with the frameworks to leverage data, business analytics, and AI/ML for informing strategic decisions, identifying opportunities, and measuring execution success:

• Strategy and Planning Professionals.
• Business Intelligence (BI) and Data Science Leaders.
• C-Suite Executives and Senior Managers overseeing digital transformation.
• Marketing and Customer Experience Strategists.
• Heads of R&D and Innovation.
• Managers responsible for defining strategic KPIs and performance dashboards.

• Case studies of companies that successfully used data to achieve strategic disruption.
• Group exercise in framing a strategic business question as a predictive analytics problem.
• Workshop on designing a strategic KPI dashboard and identifying lead/lag indicators.
• Discussions on data ethics and managing biases in strategic algorithms.
• Introduction to basic data visualization techniques for strategic communication.

Unit 1: Data as a Strategic Asset

• Defining data-driven strategy and its role in competitive differentiation.
• The strategic importance of data accessibility, quality, and governance.
• Identifying the key strategic questions that data must answer (e.g., pricing optimization, churn prediction).
• The strategic relationship between data strategy and IT infrastructure.

• Differentiating between Descriptive, Diagnostic, Predictive, and Prescriptive Analytics.
• Understanding the business value and strategic application of each type.
• Assessing the organization's current analytical maturity and capability gaps.

Unit 2: Analytics for Strategic Formulation

• Using web scraping, social listening, and alternative data sources for strategic insight.
• Applying predictive models to forecast market trends and competitor moves.
• Leveraging customer journey analytics to identify strategic white space opportunities.

• Using advanced financial modeling and simulation to risk-adjust strategic investment decisions.
• Applying optimization models for strategic resource allocation.
• Data-driven approach to M&A target identification and valuation.

Unit 3: Analytics for Strategic Execution

• Designing strategic performance dashboards that integrate data from multiple sources.
• Selecting high-impact leading and lagging KPIs for the Balanced Scorecard (BSC).
• Using diagnostic analytics to quickly identify the root cause of execution shortfalls.

• Embedding prescriptive models into core business processes (e.g., automated pricing, personalized marketing).
• Managing the strategic trade-offs between model accuracy, explainability, and speed.
• The role of the Analytics Translation function in bridging the gap between data and business action.

Unit 4: Data Governance and Ethics

• Establishing a data governance framework: quality, standards, and ownership.
• The strategic and ethical implications of data privacy and regulatory compliance (e.g., GDPR).
• Strategies for creating a unified "single source of truth" for strategic data.

• Strategies for building data literacy and a culture of inquiry across the organization.
• Addressing organizational resistance to data-driven strategic change.
• The importance of combining quantitative insight with qualitative strategic intuition.

Unit 5: Emerging Technologies and Future Strategy

• Identifying strategic applications of Generative AI and Large Language Models.
• Developing a roadmap for integrating machine learning into core strategic processes.
• Strategies for data monetization and creating new data-enabled business models.