Course Code: datalitbspk
Duration: 3 hours
Prerequisites:

None

Audience

  • Managers from the Sustainability team
Overview:

The Sustainability team is tasked with providing insights and recommendations based on data from Puma factories.  

The aim of the session is to foster in managers a data driven mindset  and to make better use of data and analytics within their function  The latter can be achieved through self-developed analyses and closer collaboration with the central data team.
 

Constraints:

  • Not hands-on; focused on concepts, mindset and examples
  • Fixed date, time and duration
  • Unlikely to be able to break the cohort up into anything smaller
Course Outline:

Initial Overview

  • Introduction: Importance of Data Analytics in Sustainability
  • Brief overview of the current landscape in sustainability and how analytics play a role.

Part I: The Why

  • Why Invest in Data and Analytics
  • Discussion on the value drivers
  1. Deeper insights and better understanding of hidden drivers
  2. Speed of decision-making and process efficiencies
  3. Regulatory drivers (especially in ESG) and risk management
  4. Sustainable ROI – Measuring the return on investment from sustainable initiatives using analytics

Part II: The What

  • Overview of Types of Analytics
  1. Theory and examples covering descriptive, predictive, diagnostic, and prescriptive analytics
  2. Case Studies: Real-world applications in sustainability efforts
  • Orientation on Analytics and AI
  1. How machine learning and AI can be applied in sustainability projects
  2. Ethical Considerations: AI and data bias, data privacy, etc.

Part III: The How

  • Deep Dive into the Data Lifecycle
  1. From data creation to insights

- Data collection methods
- Data cleansing
- Storing and cataloging
- Data Security: Ensuring the integrity and confidentiality of data

  • Data Visualization and Insights Deep Dive
  1. Importance of clear data visualization
  2. Tools for effective data visualization

Reflections and Next Steps

  • Challenges in Fostering a Data Culture
  1. Governance, tooling, and skilling
  2. Change Management: How to get buy-in from various stakeholders
  • Conclusion and Wrap-up