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Information Sciences Department

Data Management in Non-Profit Organisations

Practice-oriented course on implementing a data-driven culture and using AI tools in non-profit organisations
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Data is the key to the effective management of organisations. This course provides comprehensive knowledge and practical methods for promoting a data-driven culture. It enables participants to systematically collect and analyse data and make informed decisions on this basis. A special focus is placed on data protection, AI-supported analyses and practical application in non-profit organisations.

Period:

Start: Autumn 2025

Status:
New dates on request
Registration end:
Format:
Certificate Course
Degree:
University certificate
Credits:

120 UE

Requirements:

At least initial professional experience in a field of work relevant to the topic

Participation fee:

TBA Euro (5 % discount for FHP alumni)

At a glance

The course comprises five modules that introduce participants step by step to the development and use of a data culture in social organisations. From the analysis of one's own organisation to the application of data analyses and AI tools, central topics of data processing are taught in a practice-oriented manner. Special attention will be paid to the specific challenges in non-profit organisations so that participants can develop concrete solutions for their day-to-day work.

As part of the course, participants will also develop and implement their own data projects. Peer feedback and practical exercises will ensure that all participants are able to directly apply the methods they have learnt. At the end of the course, they present their results and receive feedback in order to optimally anchor what they have learnt.

Participants develop a sound understanding of effective data management in non-profit organisations. The knowledge they acquire enables them to apply data-based solutions effectively.

Topics:

  • Data culture in social organisations and NGOs
  • Data protection and ethical handling of data
  • Data analysis and visualisation
  • Use of generative AI
  • Development of own data projects
  • Application of data collection and analysis tools

Lecturer

  • Jasmin Rocha, Head of Data Science Hub, DRK e.V. – General Secretariat (German Red Cross)

 

Mode of operation

  • Work with case studies and topics of the participants
  • Small group work and peer feedback
  • Practical examples and exercises
  • Homework, exercises, applications, presentations
  • Feedback and assessment by lecturer

Target group

  • Specialists and executives from the fields of data management, data architect, data governance, data analytics, data strategy, data engineering, data intelligence, business intelligence, application development, IT management, marketing
  • from: Social organisations, charities, social enterprises, non-profit organisations, non-profit organisations, NGOs

Modules

Day 1: Data culture in social organisations

  • What is a data culture? What is it good for?
  • How do data-driven organisations work? Organisation, structure and processes with examples
  • Data culture in non-profit organisations and social organisations: Special features and challenges

Application and discussion: Data readiness in your own organisation: organisational self-assessment

Homework: Reading case studies

 

Day 2: Data protection and security

  • Legal and ethical issues
  • Good (and bad) practice in handling data
  • Anonymisation and pseudonymisation to protect personal data
  • Introduction to open data
  • Compliance, documentation and proof of data protection measures

Application:

  • Case studies of good and bad practice
  • Data readiness assessment and data protection checklists

Day 1: Analysing and collecting data for decision-making

  • Data use cases: governance, public relations/transparency, advocacy
  • Concrete problems vs. existing data as a starting point for the development of a data project
  • Case studies from practice

Exercise: Development of own questions for a data project and discussion in small groups (peer feedback)

 

Day 2: Introduction to the use of generative AI in data projects

  • Basics and special features of data analysis
  • tools
  • Prompting

Exercise: Further development of your own data project with generative AI tools

Day 1: Data entry and collection in non-profit organisations

  • Methods for efficient data collection and avoidance of redundant data entry
  • Utilising existing data
  • Integration of qualitative and quantitative data
  • What data do I need for my research question?
  • Technological tools for data collection

Day 2: Exercise

  • Data collection plan
  • Data inventory
  • Researching open data
  • Application of AI in the development of data collection tools
  • Application of data collection tools

Homework: Data collection

Day 1: Data analysis and reporting

  • Introduction to data analysis methods
  • AI-supported data analysis
  • Deriving insights, key messages and recommendations
  • Basics of data visualisation (with examples)
  • Basics of data storytelling (with examples)

Day 2: Exercise

  • Analysing data from your own project using Excel and AI
  • Development of the main findings
  • Preparation of the data with appropriate (AI-supported) tools
  • Peer feedback

Final colloquium

  • Review of the contents of the course: open questions can be clarified
  • Final presentations
  • Discussion and feedback

Preparation:

  • Participants finalise their data projects for the presentation
  • Participants send open questions to the lecturer (at least two weeks before the final colloquium)

The aim of the final colloquium is to ensure that the participants have achieved the learning objectives of the course and are able to apply the acquired knowledge in practice.

Registration & Information

ZEW – Further Education Unit

weiterbildung@fh-potsdam.de
House 4
Room 1.10

Beatrice Jurth

Coordination of the Further Education Unit (ZEW)
Register now