Professor of Management Science and Michael Diekmann Chair in Management Science
Big Data Analytics | Faculty Expertise
Selected Research | Big Data Analytics
Where do you begin with your (big) data initiative?
The European Business Review May/June: 19–25
Joe Peppard (2016)
While “big data” has garnered a lot of attention over the last number of years, many managers struggle in deciding where to begin. They can often be mistakenly seduced by technology companies with the promise of an IT solution to the (big) data problem. By first distinguishing between the two different ways that data can be leveraged, this article suggests a route to navigate the terrain. It introduces the QuDa model as the foundation from which a (big) data initiative can be mapped. Its fundamental premise is that it is managers not technology that give meaning to data.
Inducing environmental disclosures: A dynamic mechanism design approach
Operations Research 64(2): 371–389
Shouqiang Wang, Peng Sun, Francis de Véricourt (2016)
This paper studies the design of voluntary disclosure regulations for a firm that faces a stochastic environmental hazard. The occurrence of such a hazard is known only to the firm. The regulator, if finding a hazard, collects a fine and mandates the firm to perform costly remediation that reduces the environmental damage. The regulator may inspect the firm at any time to uncover the hazard. However, because inspections are costly, the regulator also offers a reward to the firm for voluntarily disclosing the hazard. The reward corresponds to either a subsidy or a reduced fine, depending on whether it is positive or negative. Thus, the regulator needs to dynamically determine the reward and inspection policy that minimizes expected societal costs in the long run.
Selling with money-back guarantees: The impact on prices, quantities, and retail profitability
Production and Operations Management 22(4): 777–791
Yalçın Akçay, Tamer Boyaci, Dan Zhang (2013)
In this paper, we consider a retailer adopting a “money-back-guaranteed” (MBG) sales policy, which allows customers to return products that do not meet their expectations to the retailer for a full or partial refund. The retailer either salvages returned products or resells them as open-box items at a discount. We develop a model in which the retailer decides on the quantity to procure, the price for new products, the refund amount, as well as the price of returned products when they are sold as open-box. Our model captures important features of MBG sales including demand uncertainty, consumer valuation uncertainty, consumer returns, the sale of returned products as open-box items, and consumer choice between new and returned products and possibility of exchanges when restocking is considered.
Why IT fumbles analytics
Harvard Business Review 91(1): 104–111
Donald A. Marchand, Joe Peppard (2013)
In their quest to extract insights from the massive amounts of data now available from internal and external sources, many companies are spending heavily on IT tools and hiring data scientists. Yet most are struggling to achieve a worthwhile return. That’s because they treat their big data and analytics projects the same way they treat all IT projects, not realizing that the two are completely different animals.
Harnessing the growth potential of big data: Why the CEO must take the lead
James Petter, Joe Peppard (2012)
For too long corporations have been unable to extract the fresh insights they need from their transactional data, never mind the increasing deluge of unstructured and externally sourced data. Today, finally, the technologies are commercially available to surmount these challenges. But these technologies are useless without vision and leadership from the top. At the heart of this challenge lies a new approach to thinking about information and strategy. So we’ve prepared this paper specifically for CEOs because we are convinced that the benefits of Big Data will not be realised unless the CEO takes charge of their organisation’s information strategy.