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Decision-making, customer relationships, the credibility of marketing efforts, and other areas all suffer when a company’s data is of poor quality. You may improve your organization’s capacity for data analysis and decision-making by fixing bugs and inconsistencies in your data using our guide on the five most typical problems with data quality.
Problems with the quality of the data might potentially cause problems. A significant portion of real-time e-commerce systems can go down at any moment! Why does that happen? Well, the website might be expecting alphabetic letters in the comment area. Because there is an unreadable “TAB” character, it produces a chain reaction of system failures. Data quality professionals get a kick out of these problems, and business leaders and senior managers should be terrified they will impact their businesses. The average person might find it boring to consider that “TAB” characters could be significant enough to crash a website.
We have concluded that even a tiny data quality problem might temporarily cripple a company. A natural follow-up question is, “what causes these data quality difficulties in the first place?” Some common issues with data quality are as follows.
Why do I have problems with my data?
Over time, problems might arise in a dataset in many forms. However, a certain amount of low-quality data is unfortunately unavoidable. Most problems with data quality may be traced back to when operators first entered it. This might be because of flaws in your data-gathering method or the accuracy of the individual inputting the data. Other problems may emerge over time, impacting your current database, as formatting standards change or consumer information changes. If problems develop, your company may readily identify and solve them with data input, management strategy, and the appropriate tools.
Most frequent problems with data quality
Several frequent problems might impair the quality of your data, from errors made during data gathering to outdated information. Keeping an eye out for data quality concerns and building mechanisms to resolve them is crucial since they are practically unavoidable but may be fixed. The following are the most typical problems that occur while gathering information and keeping a database for a company.
1. Missing or unfinished information
Forget to answer a question or speed through the form altogether? Those are all too common occurrences during data entry. Your company cannot get a complete picture of your consumer information or make reliable inferences from the data if it is lacking.
Fortunately, companies may quickly fix this problem by employing software that can establish mandatory fields. The program will not allow the form to be submitted until all fields have been filled in. Forms and inquiries may benefit from adding rules to address this problem. Depending on the inquiry, such restrictions might include not allowing certain characters or requiring using numeric-only fields or fields designated for money or dates. These techniques are a prime illustration of preemptive measures that users may take to enhance data quality before it is ever entered into the database.
2. Data duplication
One of the most common problems with data quality that affects organizations is the presence of duplicate records. When firms utilize various data-collecting tools and techniques, duplicate information is inescapable. A system that routinely checks for duplicate data in a database is essential when dealing with a large volume of data collected via in-person contacts, phone calls, and online forms.
When previously collected consumer data undergoes revisions, duplicates are typically the unintended result. For instance, a customer’s email address may be required to find their account. When the system no longer accepts a user’s current email address, they may choose to create a new account rather than update the existing one.
Your company needs to purchase a program to remove duplicates and merge similar records. Since your company receives such a large volume of data, fixing this problem would be tedious and unrealistically time-consuming.
3. Format inconsistency
Errors in formatting enormous quantities of data caused by dates, addresses, and numbers may be frustrating. Manually entering a date (for something like a birth date) allows for various combinations, including two-digit months and days, one-digit months and days, two-digit years, four-digit years, and a mixture of each, with or without separators. What about when people write “O” for “0” or “I” for “1”? The date may be misspelled or written in a nonstandard format if people spell it out in full, as in “May 1, 2016.”
Even addresses are impacted since some entries may have the postal code in a different location than what is correct. When data is not formatted consistently, it becomes more difficult to generate reports, conduct analyses, and make meaningful comparisons. Due to the potential for many formatting errors, it is essential to evaluate and sanitize data routinely. Address validation tools are only one example of data cleaning tools companies may use to eliminate data inconsistencies and improve research quality.
One of the most prevalent sources of data quality difficulties is people making mistakes while filling out forms. Human mistake is inevitable in any data input process; therefore, the blame may not lie with anyone. Nevertheless, this is a severe problem that has to be addressed. Although technological advancements have helped mitigate the effects of human error, people are still crucial to the process. Mistakes in typing or inputting data into the incorrect field, such as a person’s name being entered into the “address” column, are common. Also, sometimes people willfully input false information, skipping over a needed field and submitting the form. Despite the inherent probability of such mistakes, users may take some steps to mitigate their impact.
5. A variety of languages and measuring systems
The way information is handled and used has been profoundly impacted by globalization. A stricter admissions policy is needed. As a result, every system must clearly define measurement units and a method to alert possible inaccuracies, especially for firms with consumers and data input professionals in many countries. Mistakes in inventory orders are more common when attention to detail is lacking. An error in units may have serious consequences, such as using too little or too much substance. Businesses must establish uniform data quality standards that consider various metrics like weights, lengths, distances, and currencies.
Improving the quality of your data and how to fix it
If your company regularly collects new client information or keeps a database for a long time, you can be confident that data quality concerns will occur. The good news is that several tools are available to facilitate data collecting and administration. Email Oversight’s Data Quality is a valuable tool for preventing data entering mistakes and cleaning up existing data sets.