Example:
Let's say a company collects customer data from different sources, including their online purchases, in-store transactions, and customer service interactions. They decide to store all this data in a single database, without proper segregation or identification of the sources.
Now, imagine a customer named John who recently had a negative experience with the company's customer service. As a result, he decides to return a product he purchased online. John contacts the customer service team, and they initiate the return process.
Unfortunately, due to the commingled data problem, the customer service team fails to identify the source of John's purchase. They process the return based on his account details but mistakenly apply it to an in-store purchase made by another customer with the same name.
As a result, the wrong customer's purchase is refunded, creating confusion and frustration for both John and the other customer. Furthermore, the company mistakenly believes that the product sold in-store is experiencing a higher return rate than usual, leading them to investigate the wrong area for potential issues or quality problems.
This scenario demonstrates some problems caused by commingled data. The lack of proper segregation or identification of data sources can lead to inaccurate decision-making, customer dissatisfaction, and confusion within the company. It becomes difficult to trace back and rectify errors, resulting in wasted time and resources.
Illustrate with an example the problems caused by commingled data.
1 answer