Here is a brief excerpt from an article co-authored by Stijn Viaene and Annabel Van den Bunder for MIT SLoan Managedment Review. They explain why business analytics projects are often characterized by uncertain or changing requirements — and a high implementation risk. So it takes a special breed of project manager to execute and deliver them. To read the complete article, check out others, register to access content, and/or obtain subscription information, please click here.

* * *

Smart use of information technology can allow for frequent and faster iterations between the design and operating environments, improving experimentation efficiency.

Managers have used business analytics to inform their decision making for years. Numerous studies have pointed to its growing importance, not only in analyzing past performance but also in identifying opportunities to improve future performance. As business environments become more complex and competitive, managers need to be able to detect or, even better, predict trends and respond to them early. Companies are giving business analytics increasingly high priority in hopes of gaining an edge on their competitors. Few companies would yet qualify as being what management innovation and strategy expert Thomas H. Davenport has dubbed “analytic competitors,” but more and more businesses are moving in that direction.

Against this backdrop, we set out to examine what characterizes the most experienced project managers involved in business analytics projects. Which best practices do they employ, and how would they advise their less experienced peers? Our goal was to fill in gaps in management’s understanding of how project managers involved in analytics projects can contribute to the new intelligent enterprise. (See “About the Research.”) We found that project managers’ most important qualities can be sorted into five areas:

(1) having a delivery orientation and a bias toward execution
(2) seeing value in use and value of learning
(3) working to gain commitment,
4) relying on intelligent experimentation
(5) promoting smart use of information technology.

This paper assesses issues that were top of mind for experienced project managers involved in business analytics projects, which best practices they used and what advice they had for less experienced peers. We set out to find common denominators and to describe trends relevant to experienced project managers.

[Here is the first common denominator.]

Having a Delivery Orientation and a Bias Toward Execution

As a starting point, it’s important to understand what makes experienced business analytics project managers tick. The vast majority of our interviewees do not consider themselves different from other project managers. Like other focused project managers, they want to deliver their projects on time and on budget, and they have a strong delivery orientation.

But unlike many traditional project managers, they do not have a plan bias. Instead, they have a strong bias toward execution. (See “Learning From Experience.”) Although our interviewees don’t question the importance of initial planning, their focus is on project execution and delivery as opposed to adherence to the plan. In fact, they start with the assumption that the initial plan will have to change as the project progresses. This is what we mean by “a bias toward execution.”

Why do analytics project managers have this execution bias? Many say it is because of the inherent complexity of the projects themselves, and they cite three reasons. First, analytics projects are typically characterized by uncertain or changing requirements. Project sponsors and users will often have a vision of what they seek to accomplish with analytics — for example, to improve direct marketing response, reduce inventory or increase service quality and customer satisfaction while controlling costs. But how they will achieve those goals is often unclear and involves further exploration.

Second, the technology or models for meeting the uncertain requirements are often not known; they may be new to the team, or they may not even exist. This adds to the exploratory nature of analytics projects. Third, users of business analytics applications expect responsiveness, so the applications, by nature, should be highly responsive to user interaction. The challenge, then, is to find a balance between responsiveness and robustness.

Traditional project management methods tend to focus primarily on planning or a priori risk management (as opposed to managing and mitigating risk during execution). However, the uncertainty associated with analytics projects calls for a different approach.4 A growing body of literature on project management emphasizes the importance of adapting management and processes to the project characteristics. So while there may be a set of general-purpose tools for managing projects, different projects call for different managerial approaches. On the one hand,production-oriented and specifications-based approaches emphasize detailed early planning and requirements specification with minimal ongoing change and exploration. On the other, experimentation-based approaches emphasize less-specific early planning, good-enough requirements, and experimental and evolutionary design with significant ongoing learning and change.5 The latter, more adaptive approach, interviewees say, is better suited to analytics projects.

* * *

Here is a direct link to the complete article

Stijn Viaene is a professor at Katholieke Universiteit Leuven, in Leuven, Belgium, and the Deloitte Research Chair of “Bringing IT to Board Level” at Vlerick Leuven Gent Management School in Belgium. Annabel Van den Bunder is a research associate at Vlerick Leuven Gent Management School.