Faculty: Prof. Haipeng Shen

Associate Dean (Executive Education), Professor, Faculty of Business and Economics, HKU

Learning outcomes:
Firms are creating and consuming vast amounts of information, which are leaving behind a trail of digitized (big) data. To be successful, firms need to use the data to drive their decision-making, often under uncertainty due to incomplete information. However, much of the promise from the information-rich digital recording of firm activities has failed to materialize as managers often find it difficult to translate (big) data into actionable policies. This module offers discussions at both strategical and operational levels. We will discuss the analytics triangle of Question – Data – Analytics, and the right balance among them needed for effective data-driven decision making. To start the analytics adventure, we will first demonstrate how to ask “crunchy questions with cases and problem sharing from the participants, and then introduce analytics techniques using examples from operations analytics, predictive analytics, and healthcare analytics. Finally, we will conclude with a problem workshop discussion about the real-world problems offered by the participants.  Guest speakers will share their experience and perspectives as well.

• Strategic Considerations of Data Analytics
• The Analytics Triangle: Question, Data, Analytics
• How to Ask Crunching Questions
• Pitfalls of Big Data
• Analytics Techniques
• Operations Analytics
• Predictive Analytics
• Healthcare Analytics/Precision Medicine
• Problem Workshop Discussion

Pre-requisites: basic knowledge of data analysis (such as linear regression)
Software used in class: JMP

Faculty: Prof. Xin (Shane) Wang

Assistant Professor, Marketing and Statistics, Ivey Business School

Learning Outcomes:
The first decade of twenty-first century is the era of business analytics and social media. The increasing popularity of social media has resulted in astounding growth in the amount of digital data available, Managers keep up to date on emerging digital information relevant to business. We are now determining how social media platform can be leverage to gain competitive strategy. This module will help you understand online behavior, firm advertising strategy and market structure. The most important, the course identifies data sources that allow you to define and track performance indicators for your digital activities.  The goal is for students to learn frameworks and methods that will allow them to generate valuable and actionable business insights from common social media sources.

Web and Search Engine

• Search engine optimization
• Online advertising (Pay-per-click advertising)
• Measurement and prediction of ad effectiveness

Online Social Networks

• Structure of social networks
• Leverage online social networks
• Engagement on social networks

Online Reputation

• Managing and analyzing social interactions
• Online word-of-mouth
• Platform strategy

Mobile Strategy

• Leverage mobile internet
• Location-based targeting
• Omni-channel marketing

Pre-requisites: basic knowledge of spreadsheet software (e.g., Microsoft Excel)

Faculty: Prof. Michael Chau

Associate Professor, Faculty of Business and Economics, HKU

Learning Outcomes:
Massive volumes of data are available to organizations for analysis to gain managerial and strategic insights. Such data include online transactions, mobile applications, sensors, video-capturing systems, and most notably, social media. It is important for organizations to know the various state-of-the-art technologies that can manage and analyze these “big data”, and know the power and limitations of such technologies. In this module we will look at what platforms are necessary for handling massive data and how data mining and text mining technologies have been applied in various business settings for gaining competitive advantages. We will also have a hands-on session on data visualization and business intelligence dashboards.

• Technologies and platforms for processing big data
• Data analytics and mining
• Customer segmentation using data classification and clustering
• Basket analysis (association rule mining)
• Other example applications of data analytics
• Text analytics and natural language processing
• Sentiment analysis on online opinions
• Monitoring business performance through BI dashboards
• Visualizing data using Tableau

Pre-requisites: basic knowledge of spreadsheet software (e.g., Microsoft Excel)
Software used in class: Tableau

Faculty: Prof. Ning Su

Associate Professor, General Management, Strategy & Information Systems, Ivey Business School

Learning outcomes:
This course is designed to offer an executive perspective on big data and related emerging technologies, and their impact on both private and public sectors. The course encompasses key concepts, analytical frameworks, real-life case studies, and future trends related to big data and digital technologies, with a focus on managerial decision-making in an increasingly digitized and globalized business environment. Specifically, we will examine the complexity of building technology platform for big data, analyze examples of leveraging big data in a competitive industry environment, discuss models and practices for acquiring big data capabilities, explore emerging markets and emerging technologies, and investigate the impact of big data on the future of global business, economy, and society.

Building technology infrastructure for big data

• Analyzing the complexity of building large-scale data infrastructure

Acquiring big data capabilities

• Examining how to utilize big data to improve performance in changing industry environments
• Sourcing big data capabilities
• Examining how to obtain big data capabilities through strategic sourcing

Developing a big data program

• Applying learning to creatively utilize big data for value creation

Exploring emerging markets

• Exploring China’s emerging “big data” economy and its impact

Managing data and technology projects

• Simulation: Experiencing rapid prototyping
• Understanding managerial issues in data and technology projects

Exploring emerging technologies

• The future of big data