Course Outline

Foundations of Data-Driven Thinking

  1. The role of data in an organization
  • Data as support for operational and strategic decisions
  • Data-driven vs. intuition-driven organizations
  • Common pitfalls in working with data

2. Data sources and characteristics

  • Transactional, operational, and reference data
  • Internal and external data
  • Structured and unstructured data

3. Data quality and its impact on decision-making

  • Completeness, consistency, timeliness, and accuracy
  • Typical data quality issues
  • The impact of flawed data on analytical conclusions

4. Fundamental data analysis techniques

  • Descriptive analysis
  • Comparative analysis
  • Trends and seasonality
  • Data segmentation

5. KPIs and business metrics

  • The difference between a KPI and a metric
  • Selecting the right indicators
  • The traps of KPI overload (excessive metrics)

 

Insights and Business Recommendations

  1. Interpreting analysis results
  • Business context of data
  • Distinguishing correlation from causation
  • Identifying patterns and anomalies

2. Advanced analytical approaches

  • Scenario analysis
  • Root cause analysis (RCA)
  • Inference based on incomplete data

3. Data visualization

  • Principles of clear visualization
  • Choosing the right format for the data type
  • Common mistakes in presenting results

4. Formulating insights

  • Defining a business insight
  • From data to conclusion
  • The structure of a logical insight

5. Data-Driven recommendations

  • Bridging data with business context
  • Risks and limitations of recommendations
  • Communicating recommendations to decision-makers

 

Requirements

  • Basic proficiency in Microsoft Excel or similar spreadsheet tools.
  • Experience working with business or operational reports.
  • Basic knowledge of descriptive statistics is an advantage.

    Audience

  • Business and System Analysts.
  • Consultants and Strategy Specialists.
  • Managers and Team Leaders making data-backed decisions.
  • Reporting and BI (Business Intelligence) Specialists.
 14 Hours

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