Why Data Analytics Matters

Overview

In today’s business environment, data is no longer just an operational byproduct; it is a critical resource for shaping competitive advantage, innovation, and resilience. As organizations continue their digital transformation journeys, managers are increasingly expected to understand not only what data is available, but how it creates value, informs strategy, and accelerates decision-making.

This course is designed to equip managers with the essential frameworks, case studies, and applied activities that demonstrate how analytics, artificial intelligence, and large language models are being integrated into modern organizations. Through interactive labs, reflective exercises, and real-world case studies, you will explore how firms capture value from data, navigate new opportunities in generative AI, and adapt to shifting global business environments.

By the end of this course, you will be able to evaluate the role of analytics in your organization, identify opportunities for data-driven innovation, and develop actionable strategies for managing the risks and trade-offs inherent in a digital economy.

Syllabus

• Course Orientation
• The Role of Data Analytics in Organizations
Data analytics is at the heart of digital transformation, providing the foundation for how organizations create, capture, and sustain value. In today’s competitive landscape, firms that harness data effectively can unlock network effects, develop new business models, and differentiate themselves from rivals. This unit introduces the essential role of data analytics in organizations, guiding you through concepts such as value creation, infrastructure, competitive advantage, and case studies of firms using analytics to transform their industries. You will engage in reflection, activities, and case-based exercises that illustrate how analytics drives both strategic and operational outcomes.
• Value from Data
Artificial intelligence and machine learning are changing the economics of organizations by reshaping how data is processed, interpreted, and applied. From reducing marketplace integration costs to enhancing customer onboarding and support, generative AI and large language models (LLMs) are no longer experimental; they are actively influencing productivity, profitability, and competitive advantage. In this unit, you will explore how AI and ML create value for firms, identify common pitfalls in applying generative AI, and experiment with hands-on labs to understand both the potential and the limitations of LLMs. Real-world cases will illustrate how AI can expand markets, alter cost structures, and shift the boundaries of what is possible for both B2B and B2C organizations.
• Using Data to Generate Value
Data analytics and AI are most powerful when they move beyond pilots and proofs of concept into scalable, value-generating systems. This module explores the organizational capabilities required to make that leap, including infrastructure, experimentation, and integration. It also addresses how global trends such as decoupling and de-globalization create new risks and opportunities for firms managing data across borders. Through case studies, reflections, and an applied assignment, you will practice designing strategies that help organizations respond to shifting digital and geopolitical landscapes.

Taught by

Geoffrey Parker

Subjects

Data Science