Invited speakers and panellists from diverse professional backgrounds contributed their expertise on the data revolution and its implications for business strategies. The Summit was a full day of presentations, panels, and discussions to explore digital business, digital strategy, business analytics, artificial intelligence, digital experimentation and privacy. The Summit concluded with the inauguration of Ting Li as Endowed Professor of Digital Business at RSM.
Prof. Eric van Heck gave a welcome address. He emphasized the interdependence of academic research and the role of businesses in applying it for positive change. “Businesses use innovation, which is found through research and science, for real-world impacts,” he said. As Chair of RSM’s Department of Technology and Operations Management, Prof. Van Heck notices a response to the data revolution among his students. “Years ago, many students would avoid working with data analytics and algorithms. But now, we see a growing trend of students asking for these courses because the job markets are asking them for these skills – it is a growing expertise!”
Data-informed business strategies
Professor Rob Kauffman from Singapore Management University presented his research of the living analytics revolution; how people leave digital traces of what they have been doing, and how data scientists can make meaningful inferences using this information. He illustrated the impact of smart analytics in large data sets, potentially revealing ‘digital canaries in urban data mines’, the subject of one of his research papers.
Prof Kauffman reminded participants that “companies are not the central nodes in society” when referring to how businesses can create better impact. He suggests that there is an increasing demand for data-informed business strategies that reflect society’s needs, likening this to the current need for evidence-based policy-making in EU politics.
Good data science from good data science teams
“Private business is also driving that next wave of innovation,” says senior data scientist Adam Hill of data intelligence lab HAL24K, referring to the opening address. Hill discussed the use of analytics in digital business and what makes good data science. Data science “derives actionable insights to improve business,” he said. He refers to methods such as machine learning and real-time data and decision-making to illustrate their uses in examples like predicting air pollution, optimizing traffic flow, and public bike distribution. At the end of his presentation, he advises that an excellent data scientist should possess “a diverse collection of skills that cover statistics, programming, databases, communication and business domain.” Good data science comes from building data science teams, he said.
The biggest deficiency is in translating
“We are at an inflection point for data-driven management,” says Prof Ravi Bapna of Carlson School of Management at University of Minnesota. His presentation highlighted the challenges of using data analytics in business. He argued that there are three main challenges:
- executive-level awareness of the art of analytics
- a shortage of talent
- organisational muscle and capabilities.
“Analytics ‘translation’ [into business strategy] is by far the biggest deficiency [in companies],” argued Prof. Bapna. To tackle these problems, he offered an organisational learning plan that consists of a test-and-learn culture, gaining experience with large-scale data processing, and moving from point optimization to end-to-end-process optimization using audio-visual and language processing.
Eight weeks from idea to market
John Staunton, CEO of Countr POS, an online retail Point of Sale platform, presented case studies of businesses and their use of AI, machine learning, and behavioural analytics. One case study of supermarket chain Spar’s introduction of cashier- and POS-less purchasing system showed that relying on mobile, self-serve, and digital payments, machine learning learns to personalize the customer’s in-store experience. It can take as little as just eight weeks from idea to market, Staunton suggests, and this is how digital business should embrace the analytics revolution. Echoing previous speakers, he underlined the importance of collaboration within business analytics as “nobody is an expert at everything [and] closed ecosystems do not work.”
Learn to manage new technologies
Senior Associate Dean of Fox School of Business at Temple University, Prof. Paul Pavlou highlighted the foundations of digital business strategy and offered an illustration of its trajectory. Referring to recognizable phenomena such as digital platforms, AI, and the Internet of Things (IoT), Prof. Pavlou offered a prediction of the rise of Future and Emerging Technologies. As digital business advances into the Fourth Industrial Revolution, “sophisticated technologies are likely to emerge, such as nanotechnology, new materials, biometrics, and quantum computing.” He advised that learning to manage these technologies would allow for “sustained competitive advantage.”
The ethics of data analytics for privacy and data sovereignty
Moderated by RSM’s Prof. Peter Vervest, the Digital Economy Summit concluded with a panel of data experts from business and academia: Jan-Kees Buenen of Synerscope; Martijn Imrich of Xomnia; Dr Nelson Granados of Pepperdine University; and Prof. Rajiv Garg, of University of Texas at Austin. The interactive discussion addressed questions from participants, and insights and opinions from audience members were welcomed.
The panel tackled the debates surrounding co-creation in the era of big data. An intricate discussion towards the end of the session addressed the ethics of data analytics for privacy and data sovereignty. Prof. Garg dove deeper into this question and raised issue of bias of machine learning and AI for social groups such as nationality, race, and socio-economic status. An argument offered was that it is not the tools themselves that carry these biases but how practitioners program them. Panelists, supported by various participants in the audience, agree that such flaws do not invalidate the pursuit of data science; on the contrary, it is motivation to direct further cross-sector research to confront this issue.
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