Data Driven Decision Making

The Data-Driven Decision Making course is designed to equip learners with the knowledge and tools required to make effective and strategic decisions powered by data. In today’s competitive business environment, intuition alone is no longer sufficient—organisations increasingly rely on data to drive performance, innovation, and growth. This self-paced course, with a recommended duration of 20 hours, provides practical insights into how data can be leveraged to optimise decisions across industries.

Course Structure and Content

The programme is organised into ten comprehensive chapters, building progressively from foundational concepts to advanced applications:

1. Introduction to Data-Driven Decision Making Understand the principles and importance of using data as a foundation for business decisions and the shift from intuition to evidence-based strategies.
2. Data Collection and Integration for Decision Making Learn techniques for gathering reliable data from multiple sources and integrating it into coherent datasets that support effective decision-making.
3. Data Analytics Techniques for Decision Making Explore statistical and analytical methods, including descriptive, diagnostic, predictive, and prescriptive analytics, tailored to decision-making contexts.
4. Data Visualisation for Decision Making Discover how to present data insights clearly through visualisation techniques such as charts, dashboards, and infographics, enabling stakeholders to act with confidence.
5. Decision Making Frameworks and Models Study established decision-making frameworks such as rational models, bounded rationality, and multi-criteria analysis, and learn how to apply them to real-world challenges.
6. Real-Time Data and Decision Making Examine the role of real-time data in enhancing agility, responsiveness, and operational efficiency within organisations.
7. Ethical Considerations in Data-Driven Decision Making Address the ethical and legal challenges of using data, including issues of bias, fairness, transparency, and compliance with data protection regulations.
8. Predictive Analytics for Data-Driven Decision Making Learn how predictive models and techniques can forecast future outcomes and guide strategic planning and risk management.
9. Machine Learning for Data-Driven Decision Making Explore how machine learning algorithms can automate analysis, uncover patterns, and support complex decisions in dynamic environments.
10. Future Trends in Data-Driven Decision Making Gain insights into emerging technologies and trends shaping the future of decision-making, including AI, big data, and advanced analytics platforms.

Learning Outcomes

By the end of the course, learners will be able to:

• Apply data-driven decision-making frameworks to enhance business outcomes.
• Use data analytics techniques to support strategic and operational decision-making.
• Present complex data insights effectively through visualisations and communication strategies.
• Assess the ethical implications of using data in decision-making processes.
• Explore emerging trends and technologies that are transforming the future of data-driven decision-making.

Learning Materials

To ensure a practical and engaging learning experience, the course includes:

• Recorded video lectures to explain key theories and methods.
• Visual slides to support conceptual clarity and retention.
• Case examples demonstrating the application of data-driven decision-making in real-world scenarios.
• Practical exercises that allow learners to practise and refine their decision-making skills.

Assessment

Learners are assessed through an online quiz at the end of the course, comprising 20–30 multiple-choice questions (MCQs). The quiz is designed to reinforce knowledge, evaluate understanding, and test the application of key concepts. Unlimited retakes are provided to support mastery.

Mode of Study

The course is delivered fully online in a flexible, self-paced format. Learners can engage with the material anytime, anywhere, making it ideal for working professionals and students seeking to enhance their decision-making capabilities.  

Sample Lecture: