Business Analytics at the Strategic Level
This is the first of five chapters that describe the business analytics (BA) model. The chapter focuses on the strategic level and is primarily written for those who deliver or request information in connection with the development of business strategies.
In this chapter, we will present a number of scenarios that have different degrees of coordination between the development of strategies in a company and the role of BA. While reading these scenarios, you may reflect on your organization's position based on these perspectives. Similarly, consider where in this context your strategy fits. It also makes sense to consider whether the organization has understood and achieved the full potential inherent in BA and, if not, whether more effort should be put into driving the deployment of BA. Other angles from which to read this chapter are: Where are my competitors today? What kind of market will we be operating in five years from now? And, if the market is significantly changed compared to today, do we intend to lead or follow the competition on the information front?
The focus of this chapter is therefore not on how to develop a business strategy;many other books describe this very well. Instead, our aim is to demonstrate important relationships between overall business strategies and the information that the BA function can deliver in this context. Behind all the discussions, there are always two key questions: How can the BA function influence the overall strategy process in the organization? How does the overall business strategy subsequently influence the BA function?
The "Internet of Things" (IoT) allows devices and vehicles to be remotely controlled over the Internet via electronic sensors. IoT has made it possible for physical entities to interact directly without human interventions, and, together with the increased digitalization of processes, analytics has become increasingly relevant. First of all, digitalization means that human salary costs can be removed from processes. But second, it also means that whenever there is active human decision-making in a process, analytics can be used to make these decisions—and the quality of these decisions is a key factor to be optimized.
To briefly touch upon the decision quality, research from MIT shows based on the overall learning from 136 studies that in 6 percent of studies, humans make the best decisions over time; data-driven decisions in 46 percent of the cases will yield the best results;and there is no clear winner in the remaining 48 percent of studies. This means that analytical optimized processes will yield the best results in 94 percent (46 percent + 48 percent) of cases, where as human optimization will yield the best results in 52 percent (6 percent + 46 percent) of cases. Hence, analytics is not only about automating processes to reduce costs, but also about automated decision processes yielding a higher return when it comes to which customer to contact with offers, how to do pricing, what route to plan for trucks, when to restock inventories, and the like.
This has also meant a great change to how organizations see analytics today. Ten years ago, analytics was an exotic decision support that some organizations considered nice to have. Today, it has become a well-understood need that drives the digitalization agenda.