I refer to myself as a “ready-fire-aim” kind of guy. Although this is an exaggeration, it makes the point that I stop analyzing when the information is good enough to gain insights or make decisions.
I am an advocate of the Pareto principle that is also known as the 80–20 rule – the law of the vital few versus the trivial many. It states that for many events, roughly 80% of the effects come from 20% of the causes.
I refer to myself as a “ready-fire-aim” kind of guy. Although this is an exaggeration, it makes the point that I stop analyzing when the information is good enough to gain insights or make decisions.
I am an advocate of the Pareto principle that is also known as the 80–20 rule – the law of the vital few versus the trivial many. It states that for many events, roughly 80% of the effects come from 20% of the causes.
My concern is that analysts using statistics and analytics require excessive detail, accuracy, and precision. These types of analysts are perfectionists. Too often organizations over-plan and under-execute. During the investigation phase of a problem or opportunity, they can have brain freeze.
Can you read this?
I can’t blveiee that I can aulaclty unsdnaterd what I am rdanieg. The phaonmneal pweor of the hmuan mnid, aoccdrnig to a rscheearch at Cmabrigde Uinervtisy, is it dseno’t mtaetr in what oerdr the ltteres in a word are. The olny iproamtnt tihng is that the frsit and last ltteer be in the rghit pclae. The rset can be a taotl mses and you can still raed it whotuit a pboerlm. This is bcuseae the huamn mnid deos not raed ervey lteter by istlef, but the word as a wlohe. Azanmig huh? I awlyas tghuhot slpeling was ipmorantt!
Time to results versus fact-based information
Today speed and agility in analysis and decision making trumps slow and deliberate study. You were able to read the paragraph above. The message here is that it is OK to make mistakes early and often when in pursuit of learning something. It is OK to start small while thinking big.
In a recent webcast broadcast by the International Institute of Analytics titled “What Makes a Great Analytic Professional” presented by Bill Franks, Chief Analytics Officer with Teradata, Bill described the characteristics of a data scientist. An important one is for analysts to not get hung up in the details. They need to quickly get to usable results.
Bill was not suggesting that the analysis be flawed, misleading, or defensible. The point is to move quickly. Act fast. When you are in the slow lane, others will pass you by.