We spend our lives, both personal and professional, making decisions, all day long; some without consequences, and some with long-lasting and even perhaps game-changing ones.
Should I eat some Thai food for lunch, or some Japanese food?
Do we make targeted offers to customers that have been with us for more than 2 years, or to those that have been with us for more than 5?
How do we reduce the time it takes us to fix defective devices?
Although sometimes not making a decision is worse than making the wrong one, we all strive to make the best decisions possible. And to make the best decisions, we rely on experience and whatever information is at hand. With experience in the subject matter, decisions can be made very quickly; when the matter is new or information is scarce, we usually require more time to evaluate a number of possibilities, to make a few computations, to balance the pros and cons.
All this is part of our daily lives. But when a large number of decisions need to be made in a short amount of time, or when the data available to us is limited, or on the other hand enormous, automation can come to the rescue. But how can we make informed decisions at a large scale?
No historical data at our disposal
This product is brand new; should we have more stock in Massachusetts or in California?
In the case where there is no historical data, one can count on intuition, or experience, or analysis of the situation.
Intuition basically corresponds to “gut feeling”: most of the time, this is how I do it, and I get the desired outcome, more often than not. Intuition, if correct, can be formalized in the form of procedures, or in the form of business rules, that can be verified and continually improved as the procedure or set of rules are used in recurring situations.
Experience corresponds to a number of facts that have been acquired over time, and that in the end form a consistent body of knowledge that can be applied in situations similar to those previously experienced, to hopefully obtain the best outcome. This body of knowledge sometimes resides in people’s heads, or in policy manuals (in which case formalization already took place).
Analysis of a situation: without any prior knowledge, some temporary decisions can be made by carefully looking at the information currently available. Such temporary decisions can also be formalized and then refined by applying them on test situations or even real situations, each time assessing their actual value against the desired outcome.
To formalize the way the decisions are made in these three cases, we would use some common way to describe them, be it a simple text document, one or more decision tables or decision trees, or use the help of a common notation, such as the DMN (Decision Model and Notation, an OMG standard).
Simulating these decisions on test scenarios, applying them on real ones, and continually monitoring their outcome to improve them will increase their general business performance.
Historical data is available
Our strawberry muffins sold quite well over the past year, but not as well as our blueberry ones. Should we entice our customers in buying both at a lower price?
When a small amount of historical data is available, it may be quite easy to derive all the steps of the decision that could lead to the proper outcome. But more commonly, and also when the amount of data is large or very large, BI (Business Intelligence) tools, will help understand what has happened, and why. This is thanks to their built-in graphical representations and manipulation facilities that allow to “slice and dice” the data.
Using such models of the historical data can help users formalize the decisions to be made, in ways similar to the case where no historical data was available.
But such models may also be used as input to predictive analytics or even prescriptive analytics. We will look into that in part 2.
Learn more about Decision Management and Sparkling Logic’s SMARTS™ Data-Powered Decision Manager
