9 Causes Of Data Misinterpretation - InformationWeek

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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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jagibbons
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jagibbons,
User Rank: Ninja
7/24/2015 | 2:38:58 PM
Excellent summary of statiscally fallacies
These are the topics covered in most any business statistics course, but many still fall victim to them. I see the correlation versus causation problem a lot. Not sure if this is due to lack of education or experience or some sort of logical laziness that prevents some from digging further and asking critical questions. What is the old adage? 90% of statistics are made up... Don't make important business decisions without the right level of and the right kind of analysis.
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