7 Warning Signs You’re Analyzing Your Data Wrong

Guest Post from Michele Nemschoff, VP Corporate Marketing, MapR

 

 

It doesn’t matter how much data you can store or process if your analysis isn’t yielding valuable business insights. Yet it seems many businesses tend to put all of their time and resources into storing and maintaining their data rather than taking meaningful action based on insights from the data analysis. These are seven warning signs that you may have fallen into the data collection trap and are not gaining the insights that you need and want.

 

1. Poor Data Quality

 

If the data you have is incorrect, incomplete or formatted badly, your data analysis is going to be incorrect. According to a survey by Harris Interactive, 75 percent of “Information Workers” said they had made bad decisions due to incorrect or incomplete data.[1] Common reasons for this include pages that aren’t tagged, campaigns that aren’t tracked consistently, key onsite behaviors that aren’t being tracked and open field texts that allow varying answers for the same category type. Failure to prevent bad data and fix bad data that gets through may not always affect your analysis, but it’s a huge gamble to allow and one you will regret eventually.

 

2. Justifying Current Practices Rather than Seeking New Insights

 

Business leaders have to prove that their efforts are effective and positively contributing to the company, so many sort through old data to look for “evidence” that their work has been a success. This kind of analysis doesn’t yield valuable business insights because it doesn’t show how to improve or change any processes that the company is already using. This problem also tends to occur when analysts are not informed of or understand the business problem needing to be solved. Data can be analyzed and placed into graphs all day, but unless there is a real business question being asked, the analysis is pointless.

 

3.  Analysis Requirements are Determined by Low-Level Employees

 

For business analysis to be successful, the business requirements need to be defined by senior executives.[2] Unfortunately, this assignment is often relegated to low-level employees who don’t have the information or experience to make the decision effectively. This generally leads to analyses that are focused on improving current processes based off of the problems low-level employees see. These problems are rarely the questions that need to be answered to make changes and move the company forward.

 

4. Employees Lack Analytical Skills

 

While Hadoop and Big Data have a huge potential to give companies new insights, failure to train employees in the skills they need to conduct the analysis effectively and to communicate actionable steps based on insights essentially makes the data worthless. To be effective, employees need to be familiar with scientific experimentation as well as mathematical reasoning, among other skills, all while continuing to see the big picture.

 

5. Lack of Key Metric Definitions

 

It seems that running an analysis of how many leads a form on the website creates should be relatively easy, but it turns out measuring this can be varied, and the answer will vary depending on who you ask. Some may say it is when a customer submits the form. Others will say it’s only when they are qualified. Make sure every metric used has a clear definition. Otherwise comparing last quarter’s metrics to this quarter’s will be like comparing apples to oranges.

 

6. No Test for Causation

 

There are a lot of events that correlate in the summer time: more drowning incidents, higher ice cream sales and families spending more hours on the road. However, none of of these events are the cause of any of the other events. This seems fairly obvious, but when it comes to business analysis, it can be difficult to determine if a relationship is due to causation or if random. Was the dip in sales due to the release of a new product or because a competitor is  having a huge sale? Failing to step back and determine if there truly is causation - or not -  can lead to poor business decisions.

 

7. Focus on Substance not Process

 

In business analysis, companies rarely have all of the pieces. On top of that, they generally don’t know which pieces may be missing or which pieces are irrelevant to the business question. Due to this, following a methodological process to analyze data, rather than conducting analysis in order to generate a result that you are looking for, will be much more accurate in the end.

 

Getting business analysis right is tricky, and one misstep with the data and the analysis can mislead business decision makers. However, watching for these warning signs can help you get back on the right path, so you can start achieving results from insights derived from your data.

Labels: Big Data| MapR
Comments
Dilyana Valchinova(anon) | ‎01-21-2014 07:08 AM

Hi Stephen,

 

Thanks for the good article!

 

Poor data quality and inadequately defined business requirements have always been obstacles for proper analysis.

Stephen_Spector | ‎01-21-2014 09:15 AM
Dilyana: Thanks for the comment. The MapR team does a great job in Big Data and I often post blogs from them. This blog is the third in my latest series. Here are the previous two weeks blogs: http://www.hpcloud.com/blog/offloading-and-accelerating-data-warehouse-processing-using-hadoop http://www.hpcloud.com/blog/how-move-hadoop-deployment-test-phase-full-production-system
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About the Author
Stephen Spector is a HP Cloud Evangelist promoting the OpenStack based clouds at HP for hybrid, public, and private clouds . He was previous...


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