10/90 Rule

712 Words2 Pages

There are terabytes upon terabytes of data which are collected by companies to naturally serve their purpose of increasing their bottom line. To make sense of all of the large amount of data which is collected over time, there must be a system in place. Companies who want to be successful must have an effective way of analyzing the massive amounts of data they receive in order to understand the customers and appeal to them more. One way of organizing the raw data is the 10/90 rule. Avinash Kaushik, in his book the Web Analytics 2.0, explains the 10/90 rule. The 10/90 rule states that for every $10 one spends on a web analytics tool, one should be spending $90 on the people needed to analyze the numbers. For example, if a company is spending …show more content…

Even data visualization tools such as Tableau, QlikView or SAP Lumira which convey copious amounts of useful analysis is useless if the analysis it spits out is not analyzed by an analyst. Behind every analysis there is a story that needs to be extracted. This can only be possible if the right people are invested to tell stories from the output. The analytics tools are only one part of the equation for success. A computer system cannot do the full monitoring of the vast amounts of data, a human intellect is needed to look at the results of the analyses, and then recommend what steps to take in order to maximize the strengths and decrease the weaknesses which the data points out. Thus, both effective tools and intelligent human thinkers are needed to reach the final …show more content…

This may be true because different companies have different purposes, and based on the goal of their analysis, they should budget the amount to be spent on different useful aspects of the analysis. However, in my opinion the 10/90 rule serves as a bench mark for almost all the companies that do not even have an idea on how to budget for their needs. One of the primary reasons why I enrolled in the Master of Business Intelligence program was because of an article I read a few years ago. The article talked about how different grocery chains have so much data about their consumers but they have very little idea on how to use that data to maximize their revenues. The amount of data these companies have is just going to grow exponentially as time goes by. The 10/90 rule reminds me of this dilemma since it provides an effective solution for it. If these grocery chains invested in the right amount of analysts to gain insight from the data than they would be able to reach their goal of increasing their bottom line. If they were to hire some analysts, they can figure out what products are being bought the most, or least and keep a better inventory in the store. Doing this small step would greatly reduce the waste of products and the loss of profits on the items which are just not appealing to the majority of the

More about 10/90 Rule

Open Document