Sustainability is increasingly becoming a decisive factor for the long-term success of a company. In this workshop, employees learn how to develop green strategies and integrate them into business processes. The focus is on ecological, social and economic aspects of sustainability. Companies that implement sustainable practices strengthen their brands, improve their competitiveness and build trust with customers and partners. A sustainable business model not only contributes to environmental protection, but also promotes innovation and efficiency.

Data analysis helps companies extract valuable information from large amounts of data and make well-founded decisions. By using analytical tools, companies can identify trends, identify opportunities, and solve problems early on.

Benefits for you as a company:

• Optimizing business processes and resources through data-based decisions
• Improving competitiveness through early identification of trends and patterns
• Increasing efficiency and profitability through precise analyses
• Promoting a data-driven corporate culture
• Increasing customer satisfaction through a better understanding of their needs and behaviors

1. Introduction to data analysis

• What is data analysis and why is it essential for companies?
• Basic concepts: raw data, structured and unstructured data
• The analysis process: From data collection to interpretation of results
• Data quality and integrity: How to ensure that the data used is reliable

2. Data visualization: Communicate insights through graphics

• The importance of data visualization for better decision making
• Tools to visualize data: Excel, Power BI, Tableau, and more
• Choosing the right form of visualization: bar vs. line charts, heat maps, scattercharts
• Best practices for effective and understandable data visualizations

3. Descriptive analysis: What does the data say?

• Introduction to descriptive statistics: means, median, standard deviation and other indicators
• How to identify patterns and trends in data
• Key figures for companies: turnover, costs, conversion rates, customer satisfaction
• Identification of seasonal and long-term trends

4. Diagnostic analysis: Understanding causes

• Why is something happening? Identification of causes and correlations
• Using statistical tests to analyze relationships (e.g. regression analysis, correlation)
• Case study: Why is customer satisfaction falling? — Root cause analysis through data
• Insights for solving problems and optimizing business processes

5. Predictive analysis: future forecasts based on data

• How historical data can be used to predict future trends and developments
• Introduction to predictive models: linear regression, decision trees, machine learning
• forecasts for sales figures, market developments and other areas of business
• Predictive analysis tools and their application in an enterprise context

6. Prescriptive analysis: derive recommendations for action

• What should be done to achieve future goals?
• Introduction to prescriptive modeling and optimization techniques
• Decision-making tools: optimization algorithms, simulations
• Practical examples: How to use prescriptive analytics for supply chain management or marketing

7. Big data and advanced analytics: Working with large amounts of data

• What is big data and how can it be used in companies?
• The difference between traditional data analytics and big data analytics
• Big data tools and technologies: Hadoop, Spark, NoSQL databases
• Dealing with unstructured data (text, images, social media)

8. Data-based decision making

• How data can improve corporate strategy and decision-making processes
• Data-driven vs. intuition-based decision-making: advantages and disadvantages
• How to build a data-driven corporate culture
• Case studies: Companies that successfully make data-based decisions

9. Data analysis in marketing and sales

• How to use customer data for segmentation and targeting
• Analysis of buying behavior: Who buys when, what and why?
• How to optimize the sales process through data analysis
• Customer satisfaction and feedback: What does customer behavior tell us?

10. Data analysis and data protection

• Legal framework: GDPR and its impact on data analysis
• Data protection regulations and ethical responsibility when analyzing personal data
• Best practices for maintaining data security and privacy
• How to ensure that data analysis is carried out transparently and responsibly

‍