9 Causes Of Data Misinterpretation - InformationWeek

InformationWeek is part of the Informa Tech Division of Informa PLC

This site is operated by a business or businesses owned by Informa PLC and all copyright resides with them.Informa PLC's registered office is 5 Howick Place, London SW1P 1WG. Registered in England and Wales. Number 8860726.

IoT
IoT
Data Management // Big Data Analytics
News
7/17/2015
08:06 AM
Lisa Morgan
Lisa Morgan
Slideshows
Connect Directly
Twitter
RSS
E-Mail
100%
0%

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.
Previous
1 of 10
Next

(Image: Geralt via Pixabay)

(Image: Geralt via Pixabay)

Data is misinterpreted more often than you might expect. Even with the best intentions, important variables may be omitted or a problem may be oversimplified or overcomplicated. Sometimes, organizations act on trends that are not what they seem. And even when two people view the same analytical result, they may interpret it differently.

"Statistics can tell you 'this versus that.' The real questions are, 'Is the difference worth worrying about?' and 'Have we collected enough data to allow us to make a decision?" said Ken Gilbert, professor emeritus of the department of statistics, operations, and management at the University of Tennessee, in an interview.

It is entirely possible for business leaders to obsess about something that is statistically insignificant, or for data scientists to omit important variables, simply because they do not understand the entire context of the problem they are trying to solve. In short, the path to valuable insights can include a number of obstacles, some of which may not become apparent until after the fact.

Some individuals and groups take a top-down approach to data analysis, meaning that they focus on the business problem they are trying to solve and they make a point of identifying variables that have been relevant in the past in a same or similar context. Others take a bottom-up approach, meaning that they attempt to correlate variables with that which they are trying to improve (such as website conversions or sales). The danger of the latter approach is a high probability that some correlations are statistically significant but are an artifact of the way the data has been analyzed, versus being an accurate indicator of underlying relationships, Gilbert said.

There are a lot of ways data can be misinterpreted, and business leaders need to understand how and why it can happen. Here are nine examples.

Lisa Morgan is a freelance writer who covers big data and BI for InformationWeek. She has contributed articles, reports, and other types of content to various publications and sites ranging from SD Times to the Economist Intelligent Unit. Frequent areas of coverage include ... View Full Bio

We welcome your comments on this topic on our social media channels, or [contact us directly] with questions about the site.
Previous
1 of 10
Next
Comment  | 
Print  | 
More Insights
InformationWeek Is Getting an Upgrade!

Find out more about our plans to improve the look, functionality, and performance of the InformationWeek site in the coming months.

News
Remote Work Tops SF, NYC for Most High-Paying Job Openings
Jessica Davis, Senior Editor, Enterprise Apps,  7/20/2021
Slideshows
Blockchain Gets Real Across Industries
Lisa Morgan, Freelance Writer,  7/22/2021
Commentary
Seeking a Competitive Edge vs. Chasing Savings in the Cloud
Joao-Pierre S. Ruth, Senior Writer,  7/19/2021
White Papers
Register for InformationWeek Newsletters
Video
Current Issue
Monitoring Critical Cloud Workloads Report
In this report, our experts will discuss how to advance your ability to monitor critical workloads as they move about the various cloud platforms in your company.
Slideshows
Flash Poll