Before knowing the impacts of poor data quality, we must know what poor data quality is. So, poor quality data is actually data that includes incomplete, inaccurate, outdated information, misspellings, or data that is not fit for purpose. Bad quality data can cost you time and money.
Briefly defining, data must be reliable, up to date, well organized, and accurate. Data quality is important. Errors in the data prevent from using the data to reach customers effectively. To read further about the poor data quality, scroll down and check this post out.
What are the Risks of Poor-Quality Data?
Bad quality data risk losing money. It weakens business activities, for example, prospecting or running email campaigns. The inaccurate data can adversely affect the performance of your entire organization. Poor quality data leads to lower performance, lower engagement, or lower sales of any organization. The organization will start losing money in the projects. It will make the financial side of any organization week too.
These risks can eventually lead to the downfall of your company. Therefore, it is important to identify what is wrong with your data. Try to prevent the negative business impacts makes you loose important business leads also. Your data should be secured and up to the mark.
What Causes the Poor-Quality Data?
Now, we will outline how data quality problems arise. Several factors can destroy the value of your data. Some of them are following:
HUMAN ERRORS
We people are prone to making mistakes. When you are hand keying data, errors are surely expected. Data errors include typos, data entered in the wrong field, and missing details, misspellings.
LACK OF COMPLETE INFORMATION
While compiling the data, you can run into the problem of not having complete information available for every entry. Such as, a database of addresses may be missing the zip codes for some entries because the zip codes could not be determined through the method which was used to compile the dataset.
NOT KEEPING DATA UP TO DATE
One of the reasons for poor quality data is using outdated information. Keeping the data such as employee information, vendor, product names, email addresses, and other information up to date by ensuring there is no incorrect or duplicate information is also an important aspect of quality data.
NON-INTEGRATION OF RECORDS
Using a variety of databases that do not integrate with one another leads to poor data entry habits, including duplicate records or having to re-key information with the resultant loss of time and effort. Duplicate data are an important challenge to face, as these mistakes will skew the quality.
OCR ERRORS
Not only humans, but machines can also make mistakes when entering data. For data entry, some organizations rely on Optical Character Recognition (ORC) technology. This technology scans images and extracts texts automatically. If you are ORC’ing hundreds of lines of texts, then it is obvious that you are going to have some words or characters that are misinterpreted, such as sixes are interpreted as eights. The same kinds of errors arise with other types of automated machines.
DATA TRANSFORMATION ERRORS
Converting data from one format to another leads to errors. Unless your data conversion tools are smarter, they will not know the difference between the conversions. Things get much more difficult when you perform complex data conversions.
The Impacts of Poor Data Quality
Every successful business organization relies on data that results in high-value outcomes. Not only data entry is important, but also the information gathered helps you arrive at meaningful conclusions. However, poor data quality can affect your organization negatively. Let’s see some general impacts of poor data quality on business organizations.
LESS PRODUCTIVITY AND GROWTH OF ORGANIZATION
Bad data quality makes it difficult for the business to grow and reduces productivity across the whole organization. Once the mistakes are done, it requires a lot of effort to neutralize the negative effects.
POOR CUSTOMER RELATIONSHIPS
Low data quality means that you are giving customers incorrect answers. If you are not able to help them deal with their problems, then it will be quite difficult to maintain the relationship you are trying to establish with them.
BIGGER FINANCIAL COSTS
Bad data quality is equal to bad business. Not only poor data quality causes mistakes or errors, but also it will lead to an increase in costs. It is estimated that the average annual costs companies suffer due to low data quality is approximately $9.7 million. Another research shows that companies lose around 30% of revenue on average due to low data quality.
SPOILED REPUTE
Assumptions about the data accuracy led to bad customer support and inefficiency. These risk customer satisfaction, which reflects the reputation of companies. For instance, if customers are unsatisfied, they will express their reviews on social media and websites. This will cause a poor reputation in both the physical and digital world.
Correcting the Data Quality Mistakes
These all-above-mentioned data quality errors are difficult to avoid. It is very hard to even for the best-run data operation to avoid this kind of mistake. If you are struggling with these issues, there are various ways that you can address the problem. A few of them are:
This can be solved by cleaning up the original source. For instance, if there is incorrect source data, then databases will surely be corrupted. Fixing data in the original source is the best way to ensure effective customer experiences.
To avoid duplicated data, data duplication tools are necessary. These use algorithms to find and identify duplicate records through very large data sets.
Human errors can be corrected by using automation tools. These reduce the amount of manual work and the risk of mistakes by tired or bored workers.
Conclusion
Data quality of any organization is extremely important to maintain its business. However, poor data quality leads to hundreds of consequences, which a business organization can adversely suffer. Though, you can overcome the data quality mistakes.
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