Saturday, July 21, 2012

Relevant versus related data

Ok, here’s a memory jogger.  Think back to your high school algebra class and see if you can answer the following question.  You should be able to answer it in less than ten seconds.

A train leaves from Boston at 10:00 am travelling at 300 miles per hour.  Two hours later, a train leaves Los Angeles travelling at 150 miles per hour. The distance from Boston to Los Angeles is 2,982 miles. When the trains meet, which one is closer to Los Angeles?

If you are reaching for a pencil and paper or firing up your spreadsheet, stop.  It’s not necessary.  To solve this problem you only need one data point – the fact that the trains have met and are therefore equal distance from LA.

This problem is a simple example of the difference between relevant and related data.  The time, speed, and distance are all related to the problem.  However, none are actually relevant. With the abundance of data available these days, it’s increasingly important to separate the two.  

Having more data isn't necessarily better.  In fact, more data can actually make your decisions worse.  

At best, extraneous but related data is a distraction.  You spend time looking at them and making sense of them only to find that they don't inform your decisions.  Perhaps this is why so many leaders feel that they are constantly behind – they are spending too much time looking at data that don't matter.

Worse yet, related but irrelevant data can negatively impact the quality of your decision by causing you to miss the data that actually matter.  Recent research in psychology and neuroscience has shown that we can only process a limited amount of information at once.  As a result, our sub-conscious brains regularly filter information before we become aware of it, leaving certain things in the background.  Too much extraneous data can cause you to miss the actual data that you need to make your decision.

Even worse, sometimes too much data create an illusion of understanding which further diminishes your decision making.  For example, if you visit the roulette table at most casinos you’ll notice a lighted board showing all of the numbers that have recently won.  Why is the casino providing this data?  Does it help gamblers make more informed decisions?  Of course not.  It does the opposite.  Every spin of a roulette wheel is independent.  Therefore, every number has an equal chance of coming up on each spin.  If the number one comes up ten times in a row, the odds of a one coming up on the eleventh spin are the same as the odds for any other number.  So why provide the data? The answer is simple – to get people to bet more.  Most people who see these numbers don’t understand (or believe) the statistics behind the game.  As a result, when seeing the board of numbers, they believe that they have an edge in knowing which numbers may (or may not come up next).  And, when gamblers become more confident, they bet more.  The numbers on the lighted board are related to the decision that the gambler is making, but they are completely irrelevant.

Relevant data are the pieces of information that are specific to and drive a decision.  While all relevant data is related to the decision, not all related data is relevant.  Once you learn to differentiate the two, you will find that you spend less time collecting and looking at data, and more time thinking about data and decisions.

As a leader, if you want to speed up your decision making, get rid of related data and start focusing on relevant data.
Brad Kolar is an Executive Consultant, speaker, and author.  He can be reached at