Data Analysis and Strategy

Identifying problems and opportunities, formulating a precise Problem Statement, and developing hypotheses for further investigation.

Practical workshop: structured problem definition, building Issue Trees, and hypothesis formulation using a real business Case Study.

Applying strategic analysis tools (e.g., CATWOE, PESTEL) to understand the external and internal business environment before initiating data analysis.

Building a data culture within an organization, classifying internal and external data sources, and understanding their role in the decision-making hierarchy.

Data quality criteria, fundamental data cleansing principles, and dataset preparation to ensure reliable analytical results.

Practical workshop: ETL process simulation, data quality dimension checks and cleansing with a real dataset. Outlier identification and missing value treatment.

Distinguishing correlation from causation. Causal analysis methods (including Fishbone diagrams and 5 Whys) to identify the root cause of problems.

Overview of descriptive and diagnostic analytics. How to select the most appropriate analysis method for a specific business question.

Psychology of visual perception and selection of best practice principles (charts, diagrams) for clear data communication.

Practical information design and introduction to modern BI (Business Intelligence) tools for creating interactive reports.

Practical workshop: building an interactive dashboard in a BI tool (e.g., Power BI), including data loading, visual element selection, and user experience optimization.

Data Storytelling principles: transforming analytical results into a compelling narrative. Chart titles as conclusions, providing context, and guiding the audience.

Modeling an organization's Business Capabilities and aligning them with required technologies and digital transformation.

Defining success metrics (KPIs) for new solutions and quantitative forecasting of expected outcomes.

Evaluating and prioritizing alternatives using decision-making matrices and AHP (Analytic Hierarchy Process).

Synthesizing and presenting analysis results using the Minto Pyramid principle to deliver persuasive recommendations to senior management.

Evaluating the organization's analytics maturity (AS-IS), applying maturity models, and developing recommendations for transitioning to a higher level (TO-BE). Linking to data culture

Applying AI/GenAI tools in the data analysis process: automated insight generation, natural language queries (NLQ), anomaly detection, and AI assistants in BI platforms. Ethical and quality considerations.

Conceptual data modeling: entity-relationship (ER) diagrams, understanding data structures and their impact on analytical capabilities. How a proper data model improves report and visualization quality.

Core elements of an organizational data strategy: Data Governance, data architecture, data quality policies, and their alignment with business strategy. A practical framework for creating a strategy document.

Data quality KPIs and automated monitoring. Building a Data Quality Framework, defining quality metrics, and proactive quality management within an organization.

Machine learning fundamentals from a business perspective: classification, clustering, and forecasting. How to evaluate ML solution suitability for a specific business problem without programming.

Systems thinking principles in data analysis: feedback loops, system dynamics, and causal network modeling. Linking to Fishbone and 5 Whys methods.

Full analytics cycle demonstration: students present their course project to management (simulation), applying all learned methods — from problem definition to a structured recommendation using AHP and Minto Pyramid.