Parallel Parking and Other Critical Decisions

Are you confident that you have the information you need to make critical business decisions?  Are you confident that the data is of sufficient quality to guide your decision down the right path?  Are you confident that the data is timely enough to reflect the current business scenario?  Are you willing to act on information compiled from your current sources of data or, even further, allow an automated action to be triggered without your involvement in the decision-making process?  It’s that last question that gets to the heart of it.  You may answer “yes” to any of the preceding questions, but if you aren’t willing to take action or let an action be triggered, then it speaks volumes about your confidence in the data that you have and your organization’s maturity when it comes to data based decision-making.

This isn’t about processing speed and it isn’t about how much data you are able to collect.  It’s about a fundamental shift from gut feel, intuition and control to trust in your data assets.  This shift isn’t an easy one for a lot of us.  Most people that I have spoken with about this have a difficult time giving up control when it comes to making critical decisions.  They aren’t willing to streamline the process despite the fact that they know it is slowing them down.

I see it as clear evidence there isn’t a sufficient level of trust in the quality and completeness of data on hand to offset the need for speed in making decisions despite the additional cost (including opportunity cost) of delaying that decision.  It’s the classic trade-off;   good, fast, and cheap – pick any 2.

Trust in automation is built over time and cannot be immediately granted or assumed.  Have any of you had the experience of parallel parking a car that does it by itself?  Now, I don’t consider myself a control freak, but “No way, no how, not going to happen” are my first thoughts (though I recall having those same feelings of trepidation when I first started driving a car that had cruise control).

Over time, an appropriate amount of data points will be built up, the level of confidence that the right decision can be made based on the information available will increase, and when a sufficient pattern of success has developed we may even go so far as to feel confident in automating the actions which must take place based solely on the information generated.  This progression through the cycle of trust takes time.  It also takes proof of repeatedly reliable outcomes and a pattern of success before we are willing to give up control.

So as you consider the critical decisions that you must make, especially those that are time sensitive, begin to understand the whole process (including the outcome) that goes into making those decisions.  What information do I need to take action, confidently?  When do I need data to formulate the necessary information and exercise judgment?  If data is of consistently good quality, can information be synthesized more quickly and in a more automated fashion?  What patterns emerge given decisions made in the past (both good and bad decisions), and what are the critical factors that triggered those decisions?

A thorough understanding of how decisions are made, how to remediate risk and how to reduce variability (factors that give us pause in the decision process) are the first steps in the evolution of data-driven, automated decision-making.  So, when will you be willing to take your hands off the wheel when squeezing into that tight parking spot?


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