In my last post I took a stab at defining, and explaining, the concept of Decision Intelligence. I’m willing to bet you’re going to be hearing a lot about it in the not-too-distant future. So you don’t have to click back and forth, I’ll copy over what I wrote about it in that post:
This is the term Quantellia now uses to describe what it is we do. NB – The term is not “Decision Analytics”; there’s a reason for this. Perhaps it is best understood when one looks at a part of how decision modeling is accomplished. Part of the raw material available today for making decisions is what we call “big data”. There’s an awful lot of attention being paid to the field of predictive analytics, which uses big data as its raw material. We at Quantellia prefer the term predictive intelligence. This is because predictive analytics uses past performance (data) to project trends into the future. We like to think we take the concept a bit further.
While we believe analytics are useful and important, they lack the dimensions of human knowledge and understanding that can more completely predict how the past will play out in the future. A subtle distinction? Perhaps, but I find it a valuable one. Unless we’re talking about the future activity of a machine designed to perform a very limited set of instructions or actions, our activities involve human understanding, emotion, and interpretation. There are times when these attributes can dramatically change the course of an organizational effort, rendering previous decisions moot or, at best, only partially useful or correct.
By providing a method whereby human understanding, intuition, and wisdom can be incorporated into the decision model itself, we believe we can more intelligently predict the future. We are well aware there is no such thing as infallibility. However, we also know the more useful and actionable information and knowledge we have available to understand what has happened − and is likely to happen − the better our decisions will be.
Now, having had some time to think about it – it’s been over a month since that post -and having discussed it a bit with Quentellia’s Chief Scientist, Dr. Lorien Pratt (@LorienPratt), I’d like to add a little something to both the definition and the description of what World Modeler has to offer. Keep in mind, as with many things, perhaps even more so with something truly emergent and reasonably new to my experience, both my understanding and my ability to explain are evolving; developing structure and nuance as I learn more theory and encounter more examples of real-world situations.
I consider systems thinking, or the ability to see systems — and systems of systems — as the most effective way to understand what is happening within any one or more of those systems, as well as have a chance at affecting the outcomes of the ones designed to produce value and realize valuable results or consequences of their workings. The more elements of a system that can be modeled, the more likely you will be able to understand downstream effects of your decisions, and the more likely you are to see the unintended consequences of actions before you take them.
Here’s where Quantellia’s World Modeler™ excels as a decision modeling — and making — tool and enabler. Consider Predictive Analytics, the practice of extracting information from existing data sets in order to determine patterns and predict future outcomes and trends. PA usually returns fairly simple, pairwise relationships, e.g. these customers in this demographic, with this amount of revenue, etc. are likely/not likely to churn or devoting a certain amount of energy to customer retention is likely to affect/not affect customer churn.
World Modeler, on the other hand, allows you to create a highly complex systems model. This means you can look at numerous elements and their interrelationships to see how they work together, e.g. customer characteristics, customer retention efforts, likelihood to churn, total customers, revenues, and even business rules that might have to be taken into consideration if certain levels of activity are reached. Furthermore, when you don’t have data for one or more of these elements, you can use human expertise, the tacit knowledge of your employees or the group to fill in the gaps. When you have real data, if you later are able to gather it, you can then plug it into the model and continue going.
One more thing. World Model is a highly flexible, iterative navigation mechanism. It allows you to predict without complete or perfect knowledge, then pivot and change the model as new and/or different knowledge, information, and data are gathered or encountered. You can do this repeatedly over the course of months or years, whatever’s necessary to help you make the best decisions for achieving your desired outcomes. So success doesn’t depend on long-term predictions. Rather, it depends on navigation and alignment between the organizations systems, processes, and the humans that employ them.
Now . . . having learned all that, aren’t you interested in seeing how this tool works? You can get a free evaluation copy and all you’re giving up is a little contact information. There’s no obligation. Click on this the link to download a fully-functional two-week evaluation copy of World Modeler. Give her a Whirl(d)!