Analytics for Management
a new course textbook from BAI
What do future managers need to understand about data, decision science and artifical intelligence to add value to their organizations and their clients?
In our collective work Analytics for Management, we will discuss in successive chapters the importance of data, decision-making, problem solving, and evaluation. We then turn our attention to the impact of information technology with contributions on algorithms, software packages, machine learning, and big data. Each chapter will be illustrated with real-life business problems, as well as practical exercises and case studies. We conclude the work with a look at the individual skills, team competencies, and employment opportunities available today to students willing to invest in this area.
Faithful to our organization vision, this BAI sponsored work proposes that business analytics is a mindset of looking at business and markets rather than a specialization. We believe that managers wishing to invest in analytics need to understand how the digital economy has influenced how organizations evaluate people, processes, and technology. The end goal of Business Analytics isn’t to make machines smarter, but to improve how people take decisions.
The ubiquity of data has changed the way we look to evaluate our intangible assets. Data isn’t collected to simply describe physical objects, but to feed multi-purpose algorithms that condition the way we model the world around us. Data Science is less concerned with what we do (descriptive) than what we could (predictive) or should do (prescriptive analytics). If data has no intrinsic value, our ability to transform data into individual and/or collective action has become the fulcrum of both business and society
Prof. Lee Schlenker proposes that the concept of “digital economics” evokes the inter-relationships between data, business value and managerial decision making. Business information systems are no longer designed to track tangible goods, but to provide horizontal platforms that leverage the intangible assets of what we as consumers have, know, and do. This chapter will examine how the digital economy has influenced our perceptions of products, services and experiences, as well as talent and enterprise.
The value of data doesn’t come from the data itself, but from its use in enriching how organizations do business. In this chapter, Prof. Mohamed Minhaj explores the distinctions between data, information and information systems. He explores the different forms of data, as well as the types of data stores that facilitate and/or hinder its interpretation. The chapter insists on the importance of data quality, and introduces the basics of data preparation, data governance and data management.
Because people look at value from different angles, they don’t see data in the same light. How do decision-makers use data to illustrate their challenges and opportunities, what types of proof do they use to qualify the problem, what forms of data will be used to judge success? In this chapter, Dr. Kamal Kasmaoui explores individual and organizational decision-making, decision environments, cognitive biases, and the importance of transforming data into action.
Prof. Christian Hitz asks, if we can’t evaluate what we can’t measure, what data do we need to improve the practice of management? We can focus on yield – what are the potential benefits or each potential choice. Alternatively, we can focus on the effort – how much energy, and how many resources, must be mobilized to put the choice into practice? We could focus instead on velocity, given the manager’s other responsibilities, how quickly can he find a suitable decision? Finally, we could evaluate the pertinence of the model itself; does the decision address the heart of the problem at hand?
Big data isn’t a particular kind of data, but a specific approach to certain types of business problems. Prof. Farid Maklouf introduces the subject by defining the contours of this new field of research from both technical and managerial perspectives, and then focuses on what companies and organizations need to know. The chapter makes the distinction between “small” and “big” data and analyzes the importance of each for both predictive and prescriptive analytics. Using real-life examples, the author elucidates the nature of the hardware, software, skills and business use scenarios behind the hype of Big Data.
An algorithm is "a set of rules that precisely defines a sequence of operations." Like operational procedures in manufacturing, or business processes in organizational theory, algorithms in machine learning are used to process data in relation to specific problems. In this chapter, Prof. Davy Cielen explores the foundations of algorithms in machine learning, as well as their applications in sorting, searching, graphs, and context. Specific attention will be given to their role today in game theory, neural networks, predictive and prescriptive analytics.
Hardware and Software
In this chapter dedicated to business analytics platforms, Dr. Grégoire Lassence examines the different technologies that have structured the current use of analytics in business. The chapter opens with an exploration of the technological concepts, algorithms and technologies behind current academic and commercial platforms. The focus underlines the issues and options of data storage, analysis, visualization and process automation. These foundations provide the backdrop for illustrating how analytics platforms are shaping our perceptions of the challenges and opportunities of leveraging machine learning in management today.
Which data science skills and experiences should a student in management develop? The literature in data science speaks of “superstars” “ninja rock star”, and “sexy unicorns” but offers little guidance to understand the specifics of the trade. The changing nature of data, the evolution of the business models, and innovations in both hardware and software are constantly redefining the definition of the data scientist. Based on his recent work, Dr. Yves Mulkers outlines the baseline technologies, decision science and trade skills associated with expertise in data science.
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