Data is a critical component that helps drive business operations. Customer data, sales trends, stock figures, and demand graphs are just some of the information that a company uses to identify goals and make critical decisions. As much as possible, data needs to be processed and analyzed to come up with the best strategies and solutions to pressing operational concerns. But while information is useful, not all can be used for analysis. Organizations must find a way to filter bad from good data. Then again, what exactly is bad data?
What is Bad Data?
Bad data is anything considered inapplicable for a business. In terms of operations, that means inaccurate information. Inaccuracy, however, does not mean the data is false. In fact, even correct information can also be bad data.
The term “bad data” indicates that a particular piece of information lacks critical elements that are useful for an organization. It merely means that the data is irrelevant to the business’s use. In some instances, the information could also have been compiled incorrectly or duplicated. And so, using bad data can have detrimental effects on an organization’s operation.
What Makes Data Bad?
The primary purpose of using data is to derive conclusions and guide decision-making processes. That is why bad data needs weeding out of inputs for analysis. Here are some factors that turn data “bad”:
Organizations use data to get in-depth insights into their industries and consumers. And so, utilizing old information would not present an accurate picture of what companies want to measure at present.
If a business owner wants to see the latest industry trends so he or she can adjust the advertising budget, he or she needs to use the latest statistics. So, if he or she used outdated information, the company could come up with outmoded marketing campaigns, resulting in missed opportunities and reduced revenue.
Organizations should keep in mind that using incomplete data is like putting a puzzle with missing pieces together. They won’t see the whole picture, making it harder to arrive at accurate conclusions.
A company that wants to tailor-fit products to specific audiences needs complete data. So if it runs a survey but forgot to ask respondents to include their age, it can’t create complete user profiles. As such, while it may know where consumers come from or if their gender, it has no way to see if its products are doing better with a particular age group.
When it comes to dealing with data, organizations need to make sure that what they use is relevant to their needs. It’s all about context. All their data-gathering efforts would be for nothing if they can’t use the information for their purpose.
A fashion store catering to U.S. buyers, for instance, may not need data from online shoppers from Europe unless it plans to expand to the region. Until then, though, it would make more sense to focus on local shoppers when they pool and analyze data.
Data is always going to be a big part of any organization’s operations. As such, companies must find ways to filter out bad data so it would not negatively affect their business.