Executive Education Courses in Data Analytics

Executive Education Courses in Data Analytics

Companies across business sectors are beginning to realize the potential of “big data”, but that has introduced risks such as privacy violations and discrimination

From consumer goods groups leveraging new technologies to repel challengers, to fund managers applying vast data sets to their investment processes, companies across business sectors are beginning to realize the potential of “big data” — shorthand for analyzing large volumes of information to provide valuable insights that guide decision-making.

The exact use cases depend on the companies and their datasets. But generally speaking, big data can improve executives’ ability to make better, faster and more accurate decisions based on data and facts rather than intuition or experience. It can also help with connecting with customers and clients, and streamlining business processes with data and artificial intelligence.

“Businesses and their leaders can use data to understand better, in an un-biased way, how they can create value for their customers,” says Nektarios Oraiopoulos, University Lecturer in Operations Management at Cambridge Judge Business School in the UK.

“This process enables companies to have a more focused version of the truth rooted in data, rather than having conflicting views and counterproductive arguments regarding what is good for the company.”

Demystifying analytics for executives

Interest in executive education programs focused on data analytics — the process of modelling data to discover useful insights, form conclusions and support decision-making — is exceptionally high, often fully subscribed at several business schools offering these courses.

But what has surprised program administrators is the sheer breadth of the interest in in their big data offerings. In less than two years from its launch, there have been more than 3,600 participants in the Business Analytics: Decision-Making Using Data at Cambridge Judge. “What it is even more interesting is that the range spans all industries from finance and technology to life sciences and arts,” says Oraiopoulos.

The course covers all the state-of-the-art tools of data science and analytics. The key objective is to demystify those tools for executives. The course is designed for those who aim to put those tools into action, so initiating implementation is a key aspect of the course.

It’s one of a wide array of course options from business schools such as the Decision Modelling in Business Analytics program at NYU Stern School of Business in New York, and the People Analytics: Driving Strategy with Data program offered by University of Minnesota’s Carlson School of Management.

The interest in these programs is high and rising. “We offer the course quarterly to deal with the demand. It’s often fully subscribed,” says Dr Tan Hong Ming, Lecturer in the Department of Analytics and Operations at NUS Business School in Singapore.

NUS runs the executive education course, Leading with Big Data Analytics and Machine Learning. The key learning outcomes include discovering how big data and analytics can help business accelerate innovation and achieve a competitive and sustainable edge.

Participants will be exposed to the latest ideas and techniques in these fields, understanding and interpreting data to make better business decisions. Importantly, they can also learn to build a data-driven culture across their organization.

“Data has to be collected before it can be used,” says Dr Tan. “Companies have to invest in infrastructure or services to store, extract, load, transform, and analyze their data. On top of that, they have to train or employ staff to have the technical ability to do so.”

Understanding the risks associated with big data

There are risks if companies don’t fully understand the models they use. So, for many participants, an executive course will be about mitigating the risks that data dependency might introduce, such as privacy violations, discrimination and cyber risks.

“For data driven organizations, data is their biggest asset,” says Associate Professor Jussi Keppo, Program Director of the course at NUS. “This implies that there are new opportunities, but datasets also create new responsibilities such as reputation risk, cost of maintaining the data, and possible penalties due to violations of regulations and laws.”

Murat Kristal is director of the Centre of Excellence in AI and Analytics Leadership at York University’s Schulich School of Business in Toronto, Canada. “The biggest drawback is the inherent bias introduced into business decisions via big data,” he says. “Most of the analytics applications are based on existing data. If the data used for the analysis has inherent biases built into it, then the results will reflect those biases.”

The Schulich School offers a Master’s Certificate in Analytics for Leaders. Participants learn the fundamentals of an analytics infrastructure: what’s required to store, access, manipulate and manage data from technical, regulatory, privacy and cybersecurity perspectives.

They also learn, practice and perfect the softer leadership skills, along with learning how analytics can be used to enable digital transformation.

The risks of data dependency are becoming more prominent themes in the program. “The privacy and ethics of big data are becoming a big concern for regulators,” says Kristal. “As a result, we have a session on Data Governance and Privacy, where we cover the privacy and ethics concerns in detail.”

It’s a similar story at ESMT Berlin, which offers the program Analytics for Decision Makers in Germany. “Many companies face legal and regulatory barriers in using the data to, for example, compare the performance of employees, to deploy machine learning for recruitment or internal job placements,” says Jens Weinmann, Program Director at ESMT.

As such, all new digital technologies are inherently neutral and can be used to the benefit of individuals and to their disadvantage.

“Executives have to be aware of data protection laws, and all skills and capabilities have to be embedded in a use case compatible with corporate ethical standards,” Weinmann says. “But in any case, for judging about the impact on privacy and ethical risks, they have to know the possibilities and limitations of the analytics toolkit.”

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Related Business Schools

Cambridge - Judge

NUS

York - Schulich

ESMT

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