9 Causes Of Data Misinterpretation - InformationWeek

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Data Management // Big Data Analytics
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7/17/2015
08:06 AM
Lisa Morgan
Lisa Morgan
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9 Causes Of Data Misinterpretation

Data can prove just about anything. Most organizations want to come to the right decisions, but faulty conclusions and bad outcomes can happen. Here's why.
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Insufficient Domain Expertise 
Domain expertise and data expertise are both necessary for accurate interpretations of data. However, business professionals are not data scientists, and data scientists generally do not have the same level of subject matter expertise that some others in the organization possess. Although there are roles in between, such as business analysts, an imbalance of data expertise and domain expertise can result in the misinterpretation of data.
'The data scientist or analytics person often doesn't understand the context of the variables they're looking at, and that happens a lot in large organizations where people are working in silos,' said Carlos Abisambra, founder and CEO of lead-generation consulting firm Vortice Services, in an interview. 'The way the numbers are calculated doesn't get put into the conclusion for the analysis.'
Sometimes a lack of domain expertise can lead to the omission of important variables, which causes strange results.
'Subject matter expertise is paramount to the analysis and interpretation of data. You can aggregate the data in such a way that you see relationships that are the opposite of what they should be. There are some variables lurking around in the background that weren't included in the analysis,' said University of Tennessee's Ken Gilbert. 'When you're analyzing 30 to 40 different variables trying to understand customer behavior, you may leave out something important that causes you to arrive at an erroneous conclusion and you may not even realize it.'
Before Tron Jordheim, chief marketing officer at self-service storage company StorageMart, uses data to examine a situation or to make a decision, his team questions whether the result is possible in the real world, whether the result is likely, and what real-world experience makes them skeptical about the data or makes them think the data makes sense.
(Image: PublicDomainPictures via Pixabay)

Insufficient Domain Expertise

Domain expertise and data expertise are both necessary for accurate interpretations of data. However, business professionals are not data scientists, and data scientists generally do not have the same level of subject matter expertise that some others in the organization possess. Although there are roles in between, such as business analysts, an imbalance of data expertise and domain expertise can result in the misinterpretation of data.

"The data scientist or analytics person often doesn't understand the context of the variables they're looking at, and that happens a lot in large organizations where people are working in silos," said Carlos Abisambra, founder and CEO of lead-generation consulting firm Vortice Services, in an interview. "The way the numbers are calculated doesn't get put into the conclusion for the analysis."

Sometimes a lack of domain expertise can lead to the omission of important variables, which causes strange results.

"Subject matter expertise is paramount to the analysis and interpretation of data. You can aggregate the data in such a way that you see relationships that are the opposite of what they should be. There are some variables lurking around in the background that weren't included in the analysis," said University of Tennessee's Ken Gilbert. "When you're analyzing 30 to 40 different variables trying to understand customer behavior, you may leave out something important that causes you to arrive at an erroneous conclusion and you may not even realize it."

Before Tron Jordheim, chief marketing officer at self-service storage company StorageMart, uses data to examine a situation or to make a decision, his team questions whether the result is possible in the real world, whether the result is likely, and what real-world experience makes them skeptical about the data or makes them think the data makes sense.

(Image: PublicDomainPictures via Pixabay)

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shamika
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shamika,
User Rank: Ninja
8/5/2015 | 10:23:24 AM
Re: statistic lies
I agree. Sometimes it can be the wrong statistical tools that has been used which will lead towards wrong analysis and decision making.  
LisaMorgan
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LisaMorgan,
User Rank: Moderator
7/30/2015 | 8:42:32 PM
Re: statistic lies
Thank you.
kstaron
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kstaron,
User Rank: Ninja
7/30/2015 | 8:19:24 PM
statistic lies
Nothing can lie quite like a statistic. The worst part is that sometimes you don't know when it's lying. Excellent article hitting the main ways statistics can give you bad information. It's an eye opener to know that even with massive amounts of big data, you can still get bad results if you make one of these mistakes.
jagibbons
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jagibbons,
User Rank: Ninja
7/25/2015 | 11:43:25 AM
Re: Excellent summary of statiscally fallacies
Agreed. If you don't understand the context of the data, it is certainly hard to make good decisions based on that data.
shamika
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shamika,
User Rank: Ninja
7/24/2015 | 11:05:14 PM
Re: Excellent summary of statiscally fallacies
@ jagibbons I agree with you. It is always important to use the statistical data analysis to reduce variations and provide accurate reports for decision making.
shamika
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shamika,
User Rank: Ninja
7/24/2015 | 11:02:30 PM
Sources of Variation
This is really important. Otherwise it will lead towards wrong analysis. It is always important to use the statistical data analysis mechanisms in order to avoid those.
shamika
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shamika,
User Rank: Ninja
7/24/2015 | 11:02:26 PM
Re: Excellent summary of statiscally fallacies
This is an interesting article on data misinterpretation. As the first point explains domain expertise is something very important when dealing with data. Lack of this could lead towards wrong statistics and wrong decision making.
LisaMorgan
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LisaMorgan,
User Rank: Moderator
7/24/2015 | 4:09:37 PM
Re: Excellent summary of statiscally fallacies
Amen.  :-)
jagibbons
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jagibbons,
User Rank: Ninja
7/24/2015 | 3:56:13 PM
Re: Excellent summary of statiscally fallacies
Agreed that this level of maturity will take time. The sad fact is that many businesses don't have the time. They must make business decisions quickly, and in many cases they are making decisions on a partial or complete misunderstanding of the very data they are using the make the decision. The best any of us can do is be aware of our limitations and seek assistance to help us move forward and learn.
LisaMorgan
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LisaMorgan,
User Rank: Moderator
7/24/2015 | 3:47:00 PM
Re: Excellent summary of statiscally fallacies
Thank you.  My personal take is that people are really busy and if this kind of critical thinking isn't baked into your DNA yet, then it hasn't become part of the integral thought process yet.

A lot of business majors took statistics courses because they were either required to do it or it seemed less scary than some other math requirement.  In the absence of knowing why statistics matters to your job - which was clearly absent at least when I went to school - unless you consider math fun as some of us do, it's just a requirement you tick off your undergraduate to-do list and move on.  It does not become part of your thinking.  It doesn't interest you, it doesn't intrigue you.

I'm encouraged that colleges and universities are re-examining their curricula because data is becoming so integral to all parts of the business.  If even basic statistics is something your future employer is going to require, it behooves you not to just get an "A" in the class, but internalize what it means in terms of your major.  

Smart businesspeople ask smart questions.  Once people figure out that "smart" behavior includes asking the questions you outlined, more will be motivated to do that.  No one is born knowing any of this stuff.  OTOH, people have to adapt.

Sure, I imagine some people might be lazy about it or consider asking another layer of questions Yet Another Thing to do.  If you're responsible for outcomes, you're wise to ask such questions.  Even boards of directors are starting to ask questions they never asked before such as, "What's your Big Data strategy."

I think it's a matter of industry maturity and professional maturity.  It took time for the general population to become computer literate.  It will take some time for more people to become data literate, although this will happen faster, IMHO.

Love your points.
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