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An Acceptable Range Does Not Equal A Baseline

When solving problems the first thing a person needs to understand is where they are starting from.  To do this they have to create a baseline.  A set of data for the current process and situation.  Without a baseline, a person will never know if they improved the process or made it worse.

When I say a baseline, I mean an understanding of data of the current situation.  I do not mean a range of what is considered normal.  A range does nothing but tell a person where they might expect the data to fall when creating a baseline under normal conditions.  A range can hide problems under the guise of being acceptable.  What if something is at a high end of an range and drops to a low end of the range?  This can still create problems.

For example, two parts have to fit together.  If both parts are at the high end of the range of their part variation they snap in perfectly.  Then one part drops to the low end of the range, while the other is at the high end of the range.  Now the parts don’t fit together and people are confused because both parts are within their acceptable range.  The issue is there never was a baseline created to understand both parts were at the high end and this condition created a good result.

The area I have the most frustration with this is in health care.  A person can go to the doctor wondering if they have hearing loss or damage.  The doctor tests you and says you are fine there is no damage or loss.  How do they know?  They never had a baseline from before to understand the person’s hearing is any different.  The doctors just tells the person they are fine because they are in the “normal” range.

The assumption is the range is built on lots of data over time and covers the 80-85% of the normal distribution of data, again assuming the data fits a normal distribution curve.  What if the person is someone at one of the extremes of the curve?  Doesn’t this change things?

I understand doctors need some tools to help them out.  That is what a range is a tool.  If a patient says something is not normal for them, the doctor can’t say they are normal because their test falls in a certain range.

Ranges are nice and can be helpful, but they are not a substitute for a baseline.  The baseline gives a more detailed picture.  Baselines help to problem solve and improve. So before judging if there is a problem, a person should ask, “Where did I start from?” or “What is my baseline?”

Data and Facts Are Not the Same

Last week Steve Martin had a great post about data and going to see what is actually happening over at theThinkShack blog.  It struck a nerve with me because it reflects something I seen happening on a regular basis.  I am tired of people trying to solve problems while sitting in a conference room.

I listen to comments like, “Well they aren’t using the right codes for the defect.” or “People just need to put the coding in the system properly and we could figure the problem out.”

WHAT!?!

Don’t misunderstand me.  Ten years ago you would have heard me say some of the same things.  So, I do have patience with teaching people to go and see.  Once I learned to go and see it became very freeing because I didn’t stress about what the data said.  I spoke to facts.

Data is a good thing.  I am not saying we should ignore data, but we need to know its place.  Data can help point us in the direction of problems.  It can tell us where we should go and look for facts.

Facts to me are what you actually see happen.  What you have observed.  It isn’t the hearsay you get in a conference room.   Facts explain what is actually happening and add deeper meaning to the data.

I lived a great example recently.  In a conference room, managers looked at the data and saw a problem that was happening.  They started talking about what was happening and why.  They asked if I would look into fixing it.  I said I would look at what is actually going on.  I spent 2 hours directly observing the work and realized the one problem they were talking about was actually several different problems out on the floor.  I asked the person actually doing the work to take a couple weeks worth of data based on what was actually happening.  The data showed they actually had 2 big problems that made up 80% of the total errors the original data showed.  I then did another hour of direct observation between an area that had the problem and an area that did not.  I was able to explain the problem with facts that I observed and data to support those facts to add concrete to what I observed.  At that point, there was some obvious ways to correct the situation.

Data and facts are different.  They are not substitutes for each other.  Data and facts can be a very strong combination when used together to understand a problem.

data directional

facts truths – use eyes – go and see

Link to Steve Martin’s Blog Post: http://thinkshack.wordpress.com/2011/03/07/garbage-in-wheat-and-soybeans-out/

Lack of Data Can Drive Poor Decisions

There seems to be a big problem with organizations having a lot of data, but not good data.  Data that can help them make good decisions for the business.

This lack of good data can cause organizations to be concentrating on the wrong opportunities to improve or grow.  Imagine working on the last issue of a Pareto chart and not the biggest issue because of poor data.  You would spend effort that could have been directed towards a bigger issue for the area.

Lack of good data can cause you to start looking for a countermeasure to a problem in an area that is not really where the problem is.

Organizations need to become better at getting useful data and information, not just any data and information.  Computers and automation has made it easier to collect and store data on anything and everything.  This is a form of waste.  Organizations should strive for collecting only useful data and information that can help to make good informed decisions.

The best way to overcome this is by directly observing the work and issues.  When directly observing, you will get a better picture of what is actually happening.  Usually, the data that you need to have in order to make a good decision becomes clearer.

As leaders we need to push to get people to go out and directly observe in order to drive more useful data and information for decision making.

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