HKU-Ivey Executive Leadership Program - Module 1: Big Data and Technology Management
HKU-Ivey Executive Leadership Program - Module 2: Social Media Analytics and Digital Strategy
Why attend this program?
- You want to learn different analytical approaches to strategic business decision-making
- You want to learn how to incorporate uncertainty in decision-making
- You want to learn the fundamental issues and approaches of Big Data
- You want to learn how to summarize, interpret and derive insights from data
Who should attend?
- seasoned with technical backgrounds such as finance, actuary, revenue management
- heading a team of technical experts with mandate to use data analytics for business success
- leading departmental heads with technical background as direct reports
Module 1: Big Data and Technology Management
Faculty: Prof. Ning Su, Ivey Business School
Learning outcomes:
This course is designed to provide an overview of big data and its impact on businesses and societies. Specifically, the course encompasses key concepts, analytical frameworks, real-life case studies, and future trends related to big data, with a focus on managerial decision-making in an increasingly digitized and globalized business environment. Specifically, we will examine the complexity of building technology infrastructure for big data, analyze example of leveraging big data in a competitive industry environment, discuss models and practices for acquiring big data capabilities, explore emerging markets and emerging technologies related to big data, and investigate the impact of big data on the future of global business, economy, and society.
Introduction
- The age of big data
- Understanding key concepts and examples of big data
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
Module 2: Social Media Analytics and Digital Strategy
Faculty: Prof. Shane Wang, 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)
Module 3: Data Mining and Text Analytics
Faculty: Prof. Michael Chau, 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
Module 4: Data-Driven Decision Making under Uncertainty
Faculty: Prof. Haipeng Shen, 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
After completing the program, you will gain insight in:
- methods to analyze data to improve understanding of complex business issues
- the role of data in creating and sustaining competitive advantage.
- recognition of opportunities where achieving an understanding of data or analyzing data can lead to enhanced performance or profitability.
Next intake will be starting in Spring 2018.
Exact dates will be announced later.
