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?? In this article's installment of our knowledge systems series on data management, we'll examine the reasons for bad data quality. Data quality is important to the business. That you know. But do you understand what it takes to provide data quality We’ll review how data quality problems can arise? Briefly defined, data quality refers to the ability of a data set to serve whichever need a company hopes to use it for. That need could be sending marketing materials to customers. It could be studying the market to plan a new product feature. It could be maintaining a database of customer data for help with product support services or any number of other goals. No matter what the exact use case for your data is, data quality is important. Without it, the data can’t fulfill its intended purpose. Errors within a database of addresses would prevent you from using the data to reach customers effectively. A database of phone numbers that don’t always include area codes for each entry falls short of providing the information you need to put the data to use in many situations.
1. The properties of poor data
It's crucial to first define what constitutes "bad" data. A data set may be compromised by a variety of circumstances, thus you should watch out for data that is:
For a variety of causes, low-quality data will exhibit one or more of these characteristics. Let's examine the root causes of these data integrity problems in more detail.
2. 5 reasons for low data quality
After defining data quality and giving a few instances of what it looks like in practice, let's take a closer look at the kinds of issues that can result in poor data quality. Even if you normally follow best practices for maintaining and interpreting your data, there are 5 common ways that data quality problems can snare your organization's data operations:
2.1 Manual data entry
Manual data entry is not particularly advantageous. The risk of human error when manually entering data from A to B is one of the key problems. People are imperfect. It can also be very simple to make mistakes when performing a boring duty like data input. These errors could range from a minor typo to an entire entry being overlooked. A human could accidentally enter information in the incorrect field. It's an honest error; there's no malice involved. At least this is a simple issue to solve. Automation software can perform data entry more quickly, more effectively, and with perfect accuracy than using human team members. Automation is not susceptible to human error and won't become boring. The accuracy of the data that your organization stores will be significantly improved if you configure your automation system with the appropriate rules and integrations.
2.2 Unmanaged data decay
Data is not impervious to change. Data quality will deteriorate over time as it is invalidated. Customers obtain new phone numbers, addresses, and email addresses. Companies adjust their relationships with you and their contacts as well. When fresh innovations or shorter hype cycles occur, once-wanted technology fades into obscurity. The management of data deterioration involves being vigilant. However, rule-based database cleaning offered by automation software can also be helpful in this situation. This enables automation software to automatically reformat faulty entries and delete outdated or antiquated data.
2.3 Inconsistent data entry standards
Beyond mistakes and errors, another cause of poor data quality is not having a standard understanding of how the data should be collected, transformed, stored, or represented. A good example is to imagine you are storing data about American states. Without a clear set standard for how to record this data, one state could have multiple names/entries. I.e., New York, NY, New York State, etc. All these entries mean the same thing. But, they won’t get grouped together because of the non-standard entry practices. So, you need to set and reinforce organization-wide standards to avoid inconsistency issues. Better yet, set your automation tool to recognize incongruent data entries, and automatically update them.
2.4 Siloed information/lack of integration
Siloed data is the next reason for bad data quality, as a result of this. When data is not properly connected within a company, multiple departments may end up holding duplicate data in different formats. This is obviously ineffective. It might also make future integration efforts more challenging. Ineffective integration might also result in valuable data being locked away rather than being exploited to benefit the company. Automation can once again be used, even though overcoming integration challenges can be difficult. Automation can serve as middleware in this scenario to integrate several point solutions into a linked, data-fluid environment.
2.5 Optical Character Recognition Errors
When entering data, machines are also susceptible to error. Optical character recognition, or OCR, technology is frequently used by enterprises to quickly digitize vast volumes of data. OCR technology automatically extracts text from images after scanning them. When, for instance, you wish to insert thousands of addresses that are printed on paper into a digital database so you can analyze them, it can be quite helpful. OCR has the drawback of virtually always being inaccurate.
3. Correcting data quality errors
These are the kinds of data quality errors that are extremely challenging to prevent. In actuality, acknowledging data quality issues as unavoidable is the best way to approach them. You don't have data quality issues because your data management process is flawed. It's because even the best-run data operation cannot prevent the kinds of data problems mentioned above. Thankfully, there are fixes. Precisely provides solutions for data integration and data quality that can help you reduce the number of errors that are introduced during operations like data conversions, then identify and automatically resolve any issues with data quality that do occur.
Data is constantly evolving. It constantly has to be gathered, transformed, and updated. Additionally, each of these stages in the data lifecycle presents a chance for errors or inadequate procedures to reduce the quality of the data. Therefore, the fight against bad data quality is never-ending. However, by being aware of these probable reasons and using the appropriate technological assistance, you have a solid foundation for high-quality data.
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