If the average company dug into their database, most would quickly discover many data quality errors. Such errors can have a negative impact on marketing, sales, and customer service teams. However, data errors happen all the time and come from all directions:
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Human error during data input from teams;
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Customers sharing incomplete or false data;
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Errors while exporting or importing data, and more.
Companies that don’t have a solution for this issue will soon discover that such errors are starting to overwhelm them and chip away their marketing budgets. In this article, we’ll cover the two most common quality data issues – data duplication and inconsistent data formatting.
Inconsistent Data Formatting
When data formatting is not consistent across your company’s data pools, it can be very time-consuming to process. Even the most robust data mining tool can take longer to complete the tasks. If the data formatting issues are severe enough, it can be impossible to mine and process queries at all.
The most common reason for this issue is the lack of data homogeneity. This is the age of big data, and companies can now aggregate from lots of different sources. The problem with this is that these sources often have very different formats. The good thing is that there are new data standardization tools that can minimize the risk of incompatibility. Unfortunately, companies without reliable data experts don’t understand the importance of investing in these tools. They don’t realize that inconsistent data formatting is why it takes so long for queries to be processed.
Data Duplication
Data duplication happens when a company has multiple copies of the same data source. To a layperson, data duplication sounds like a minor problem that any competent data expert can avoid. In reality, data duplication is a widespread problem. According to one report, 92% of companies admit that they are storing duplicate data!
Why is Duplicate Data Such a Big Problem?
One of the typical explanations is human error on the part of employees. Companies usually rely heavily on their employees to provide data to organizations. But, employees often suffer from fatigue after hours of working on tedious data entry tasks. This often results in them entering several copies of the same data by mistake.
Data duplication also occurs when the company is trying to syndicate data from different sources. This is a common problem with companies that use web page scraping tools to collect data from various sites. This is a typical issue associated with data mining. This is especially evident when a company is trying to collect data on competing businesses.
Data duplication also happens when a company is requesting input from its users. Customers are just as prone to making mistakes as the employees, but for different reasons. Instead of accidentally inputting the same copies of data, customers might intentionally submit different data sets. A reason for doing so might be that they are trying to earn loyalty rewards in exchange for feedback. Customers may also run into technical issues and mistakenly think they have to submit their data multiple times for several reasons, such as believing the system crashed while they were in the process of creating a new account.