Guest post from Kurt Peckman, Program Director, IBM Predictive Analytics
Rules are meant to be broken.
No one likes restrictions, to be controlled or be told what to do. In reality, however, rules are broken so better, stronger, and more appropriate rules can be created.
In other words, an established rule is often a starting point (or some critical point) of a rule’s “evolution.” Good rules evolve so better actions and decisions can be made.
For instance, some rules are about governance. Traffic rules govern some of the largest, most complex systems in the world. Motorists are surprisingly (mostly) cooperative thanks to these “rules of the road.”
And, what’s even more interesting is the apparent global rules of the road (that apply everywhere) in contrast to parts of the world have “local” rules – due to geography, culture and necessity. For example, making a right-hand turn in the United States is very different then Australia’s “hook turn.”
Rules are also about policy. For example, never go in your mom’s purse, never call someone after 9:00 p.m. or before 9:00 a.m. (Yes, I’m showing my age. I realize that nowadays we text each other 24/7).
Speaking of texting, ALL CAPS – as a rule – means you are screaming at someone. Oh, and never, ever text an image that will come back to haunt you later. Don’t be a Weiner. When you are on the golf course, there’s a rule that you shouldn’t talk about business before the 3rd or 4th hole – and try to finish up by the 15th or 16th.
I once had a psychology major tell me the vast majority of interpersonal behavior can be explained by two rules: birds of a feather flock together and opposites attract.
Think about the rules that apply to reviewing and selecting candidates for a job opening from hundreds of applicants, quickly building a large world-wide team for a last-minute project, or even during a round of speed dating.
These show the fine line between governance and policy and demonstrate how “rules” become important in guiding decisions. Specifically, they become a necessary component of a Decision Management solution – especially when the volume of decisions increases and the time to make decisions dramatically decreases.
Decision Management allows users to automatically deliver high-volume, optimized decisions at the point of impact, such as in a call center, on a website, in a store, etc.
Overall, rules help link day-to-day execution to organizational objectives. Consider sports. Every rule book for every sport has a catch-all rule that enables an official to make a “judgment call.”
In basketball a referee has discretion when determining if someone is being malicious on a foul. There are criteria (e.g., a set of rules) to determine if a foul is flagrant – was the player really going for the ball, did the foul seem to have the right balance of aggression and sportsmanship, was the foul committed during a breakaway.
Finally, let’s discuss gaming. In casinos, each game has its own set of rules. I like this as an example because there are global rules about gambling (the house always has the edge) and local rules (in the US you have to be 21 years old to gamble). The local rule in my house is that I always win.
Consider all the systems I mentioned – traffic, sports, gaming – and consider the complexity of these systems, then think about how a good set of evolving rules helps establish structure, policy and governance.
But, rules can be inflexible and limiting to good decision making. Decision Management solutions must have rules, but they also can’t rely entirely on these rules. After all, a good process might be bad if it speeds up bad decisions or outcomes.
Rules must be balanced with business analytics for optimal decisions. I’ll cover that in part 2 of this discussion.
By the way, what is your favorite rule that you like to break?
For more information:
· Learn more about IBM Decision Management by registering for the upcoming IBM Innovations in Smarter Analytics Virtual Summit (June 19, 2012 at 10:30 am ET)
· Watch a video of industry analyst James Taylor discussing the benefits of Decision Management