Bas van Gils ,
Partner at Strategy Alliance
My studies have led me down some interesting paths lately, and I've written a few posts on LinkedIN with reflections. My exploration of systems theory and brief review of the new Cynefin book are most prominent. I try to stay on top of data management developments also, of course, and I hope to write more about this exciting field soon. The pile of things to read only keeps growing. Several readers have kindly suggested new avenues for research which makes me a happy camper. So much to read, so little time!
I'm currently in the process of reading the last two issues of Harvard Business Review (HBR) and Sloan Management Review (SMR) that were still on the pile. In HBR, I stumbled across the following article:
This is an interesting topic. A quick peek at Google Scholar shows that there have been many articles on this topic over the years. Two aspects of this specific article caught my eye. First, in the opening paragraph there is a statement that reads "Researchers have identified three broad approaches to getting work done, and what they’ve learned can help managers respond more effectively to highly changeable environments". Second, the article presents a toolkit/ framework that shows which approach to try when. For reference, this framework is as follows
These two topics got me thinking: what can I learn from these three approaches/ from this framework in the context of data management challenges that I often work on with customers?
Reflection on the premise of the article
The premise of the article is that there are three broad approaches to getting work done. This made me think of the Cynefin framework, which distinguishes between
All-in all, there seems a fair fit between the two. I am inclined to follow the more meaningful/ detailed distinctions of the Cynefin framework but the three approaches listed here (routines, heuristics, improvisation) is a workable distinction that may be of use to teams in the field.
Relation with data management
Data management is a pretty big field that comprises many areas. In another HBR article, two scholars give an interesting overview of what data management is all about. The article is:
DalleMule, L., & Davenport, T. H. (2017). What’s your data strategy. Harvard Business Review, 95(3), 112-121.
In essence, this article says that there are two perspectives on data management, and the trick to being successful with data is to balance between the two perspectives. These are:
The functional areas and capabilities of the DAMA DMBOK - the industry reference for data management - can easily be plotted on these two perspectives.
As a thought experiment, I have tried to apply the three approaches to the two perspectives. Here is what I have come up with:
Data management offense:
Data management defense:
The first take-away is a personal one. I think the combination of sense-making (what kind of situation am I in, and how does that guide my action moving forward) is an interesting research topic. I'll have to revisit this when the articles and books about Cynefin are published. A better understanding in this area will also lead to changes to my book (Data management: a gentle introduction) and will find its way into the trainings that we (Strategy Alliance) offer. In this light, I am also interested in thoughts of other scholars in this field.
The conclusion from this brief analysis is for professionals in the field. I am curious to hear if the 2x3 thought experiment from the previous section makes sense. Would it help to think along those lines in your day to day work? Do you have good examples and cases to share?
The final thought is for leaders. Many organizations that I talk to have "something with data" in their strategic ambitions: data-driven decision making, monetizing data, develop sustainable data infrastructure. It would help if leaders are aware of not only the complexity of the challenges of their professionals, but also the fact that different types of challenges require a different approach and therefore also a different leadership style. As before, I am eager to see more examples and hear your perspective.