What Is the Definition of Data Inconsistency?

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Businesses, scientists, and researchers worldwide use databases to keep track of information. Databases can be useful for everything from sending a postcard to all of your customers to discovering results in a scientific study. 

However, data becomes less valuable when it is not reliable. Data inconsistency is one of the most common threats to reliable data. What is data inconsistency, and what problems does it cause?

What Is Data Inconsistency?

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To use data, it has to be recorded in a format that makes it easy to read and track. Many businesses use electronic databases to track and store large batches of data. Especially for large businesses or extensive studies, the size of the information to track may be much larger than can fit in one file or even on one computer.

Data inconsistencies arise when the data that should be in one database ends up in multiple files, each with a different version of the same information. The same entries could be in the database multiple times. There may be multiple versions of the same database where one version includes fields that another version is missing. The result is a set of data that is not accurate or easy to use. 

What Causes Data Inconsistency?

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Although technology makes data easier to track, improper use of technology is often the culprit for data inconsistency. Several people can collaborate to make the same data set, but it is important to make sure that all of the people edit the same file. Any changes have to be visible to all other collaborators in real-time. There also needs to be a consistent, reliable source of data to enter into the database. It would cause data inconsistencies if different individuals were pulling data from the same sources. It would also lead to redundant and inconsistent data if one or more of the individuals working on the databases could not see or keep track of the updates made by others. 

For example, suppose that four coworkers are creating a database of the customer email addresses for a large business. Some emails come from a sales funnel. Others come from a coupon opt-in, and the rest of the emails come from three different contests. If one coworker is updating a file that is only saved to his hard drive, the rest of the team will not see the changes he makes. The final database will be missing any email addresses he finds.

If the rest of the employees add to a database stored online where changes are visible in real-time, that’s a step in the right direction, but what about their data sources? It is possible that some customers signed up for all three contests. Simply using a list of emails from each contest would result in some email addresses being listed multiple times. The database needs programming rules to prevent duplicate entries.

Whether logistical or technological, the problems that can result in data inconsistencies have easy solutions. However, you have to be aware of the potential issues and develop a plan that works. For large sets of data that multiple people work on, it takes careful planning to remove data inconsistencies from the process. 

Why Is Data Inconsistency a Problem?

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Here’s a real-life example of data inconsistency on a much smaller scale. Suppose Jack, Ann, and Sheldon are all working on a group project, and they need to write an essay together. They worked together in the library, and they needed to finish the last page of the essay over the weekend. Jack typed up the original file on his laptop. He emails the file to his project partners as a Word document. 

Jack continues editing his Word document after emailing his partners. Ann uploads the information to a Google Doc, which she and Sheldon edit in real-time. At the end of the weekend, there were two different papers. Jack has one version of the paper that he worked on. Ann and Sheldon have another version of the paper. Both papers have three of the same pages, but the fourth page is different. Now, both of the documents are missing information. The group will have to meet again to decide which information from both papers to use. 

Data inconsistency is far more serious in business and science than doing a little extra work on a paper. Data inconsistency is a huge problem because people make decisions based on data. Inaccurate data results in poor decision-making. Suppose that a database collects responses in a study on a new medicine. If inconsistencies count 1,000 positive results twice, a medicine that does not actually work could go to market. If a company uses an inconsistent database to mail catalogs to customers, the company could waste thousands of dollars sending multiple catalogs to the same household. 

How to Prevent Data Inconsistencies

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There is a term in technology that says, “garbage in, garbage out.” If you put bad information into a database, the database can only give you bad information in return. One of the simplest ways to prevent data inconsistencies is to build rules into the spreadsheet or other database software that is being used to track data.

Data inconsistencies usually result in one of two problems: duplicate or missing data. Planning and project management can prevent missing data. For example, a business can set a policy that all employees use the same online software that updates in real-time. This will prevent employees from saving dozens of iterations of the same database on their own computers. Database rules help identify data inconsistencies and remove them before they influence results and decisions. Industry-specific software has highly-sophisticated methods of recognizing duplicates. Even the most basic spreadsheet software can be programmed to detect errors. 

Understanding what data inconsistencies are is the key to understanding and preventing them. As the saying goes, an ounce of prevention is worth a pound of cure. It is much easier to fix the causes of data inconsistency than to improve the wide variety of problems resulting from it. 

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