This is the second in my two-post interview series with Gert Laursen and Jesper Thorlund, authors of the book, Business Analytics for Managers: Taking Business Intelligence Beyond Reporting. Part one covered data visualization and the cross-functional purpose of business analytics. Here, we talk about analytic talent, measuring innovation and educating works for decision support roles.
Anne: I loved your chapter on business intelligence competency centers (BICCs). Given the recent interest we have seen in analytic centers of excellence (which we view as complementary to existing BICCs), can you comment on how having an additional shared resource (even if part of a larger BICC) might help organizations better use their scarce analytic talent?
Gert and Jesper: We believe that most analytical talents find that they are doing a lot of routine tasks that others could do just as well. Secondly, it is also important that talented junior executives can be trained and can take over should the senior executives change jobs. This is good for the sake of the junior executives and for the company.
In general, this consolidation of information workers gives analysts a career path different from that of a specialist. This might retain them in the current job for a longer period of time.
Anne: What are some of the best ways you think organizations can measure the effectiveness of their efforts so it's easier to show the value they are creating and get buy-in to do more?
Gert and Jesper: First of all we believe that it is, and will always be, hard to measure the effectiveness of innovation and that is what business analytics primarily generates. business analytics typically is an enabler of the improvement of other processes. When the project is finalized and the new process is up and running, it is often very difficult to assign the success to individual contributors. In general, it is our experience that the analyst will get the most exposure (and thereby analytics can be conveyed as a valuable business means) if analytical competencies are used on a project level with fixed deliveries, a deadline, and an understanding of analytical potential. The alternative—which is to place the analyst between a series of technicians—is that the analyst will never gain an understanding of business that he or she needs in order to go out and make a big difference.
If no projects exist, the analyst should “invent” his or her own, go out and gain stakeholder support, and grow with the successes. Also the analyst should know how to sell what he or she does—create a vision and deliver on it.
Therefore, we believe that the ultimate success criterion for business analytics is to identify how many business processes are based upon or enabled by it. It is not until somebody does something differently and smarter than what they otherwise would have done, that decision support generates some value to the company. After all, if the company—despite all decision support—proceeds how it otherwise would have anyway, information management is something between a nice-to-have and a waste of resources.
Just like in sports, success is when people clap their hands; basically, we believe business analytics departments should be measured the same way. No matter how you twist things around, deliverables from a business analytics department are ultimately judged by feedback from business people using the information. And those users will clap if they like it. If the BI department creates information that increases business people’s process performance and takes them closer to their own targets/bonuses, they will let analysts know and “shout” for more throughout the organization. In that case, analytics is creating value and it’s easy to get buy-in to do more.
Anne: Any other thoughts you'd like to share?
Gert and Jesper: Another element that we could discuss is how education still focuses on statistics, tables, and formulas rather than decision support and process improvements. Today, we have software that can calculate a t-test score. Let’s not waste the business students’ time teaching them this. Let’s teach them information management, how to work smarter and not harder; let’s teach them how to create value rather than numbers.
Keep in mind that organizations hire analysts who can help the company become more efficient and effective; that is, for example, how to use statistical software. We teach students how the formulas in the software work. The equivalent is that companies want to hire drivers of cars, while we train students to be mechanics. Alternatively, you go to ski school in the Alps and they teach you is how to produce skis, not how to use them.
This is why our book is so relevant for education systems: it creates a foundation for competing in the Information Age.
Anne: Your view sounds consistent with what I have heard many others say—that universities should teach applied analysis first and then the underpinning theory. Thank you for taking the time to write the book and to share your views on so many facets of business analytics.