Data quality control is a set of activities aimed at detecting and correcting errors in existing data sets, ensuring that information is always accurate and reliable. In this article, DIGI-TEXX will clarify the concept of Data quality control, key components, common tools, and practical examples.
What Is Data quality control?
Data quality control is a reactive process, focusing on identifying, measuring and correcting errors in collected data. The ultimate goal of this activity is to ensure that the data set meets the highest standards of data quality before being used for analysis, reporting or machine learning systems, an important step in comprehensive data quality management.
Data Quality Control vs Data Quality Assurance
We need to clearly distinguish between data quality control and data quality assurance, as these are two different approaches.
- Data quality control: This activity focuses on detecting and correcting errors in existing data. This activity takes place immediately after the data has been recorded.
For example, running a script to find and merge duplicate customer information data records.
- Data quality assurance: This step focuses on preventing errors from occurring in the first place. This is a proactive activity, taking place before and during the data collection process by establishing procedures, standards and policies.
For example, designing data entry forms with validation rules so that users do not enter incorrect date formats. This is also something a professional data entry service provider can help you set up more efficiently.
Key Components of Effective Data Quality Control
It is understandable that a good and complete data quality control process will have to be built on the evaluation of many aspects or dimensions of data quality. Below are the key components to note when starting to evaluate data quality
Accurate Data Capture and Formatting
In the data analysis process, data accuracy is the most important factor, ensuring that the data reflects reality. A good data quality control process will have to check for errors such as mistyped (customer name), incorrect numbers (order value), or outdated information (old address). This step plays a key role in data accuracy improvement, helping businesses make more reliable decisions.
In addition, consistent formatting is also very important. Date data such as “11-07-2025” and “July 11, 2025” need to be standardized to a single format (eg: YYYY-MM-DD) so that filters and calculations work as accurately as possible.
Validity and Integrity Checks
Validity is ensuring that data adheres to established business rules. For example, an employee’s age must be between 18 and 65, or a product code must be on a pre-defined list of valid codes. Data quality checks flag values that fall outside these rules.
Additionally, data integrity maintains the correct relationships between data tables. For example, an order must be linked to an existing customer code in the system. This check prevents orphan records from falsifying reports.
Consistency and Standardization
Data consistency is ensuring that a piece of information is the same across all systems. For example, a customer’s company name must be spelled consistently in the CRM system and the accounting system, rather than “Company A” in one place and “Company A” in another. The data quality control process identifies these inconsistencies. The solution is standardization – that is, converting data variations into a single common form, making the aggregation and analysis more reliable.
Completeness and Relevance
Completeness will measure whether any important data fields are missing. A customer profile that is missing an email or phone number will reduce the effectiveness of marketing campaigns. The data quality control process will scan and identify records with missing data so that additional or removed options can be made.
In addition, relevance will ensure that the data serves the right analysis purpose. Outdated data may no longer be relevant to forecasting current trends and should be taken into account during the control process.
Best Data Quality Control Tools and Technologies
Manual data quality control is ineffective with big data – especially when companies are dealing with more data than usual. Therefore, dedicated data quality measurement and control tools are essential to automate the data analysis process, helping to save maximum time.
- Data Profiling Tools: This is the first step in the data analysis process. This tool will help explore the structure, statistics on null values, data distribution and anomalies to understand the current state of data quality.
- Data Cleansing & Standardization Tools: These tools will automate error correction, remove duplicate dataand standardize data formats according to defined rules. It can be understood that this is the core step of the data cleaning process. If you’re looking for a more scalable approach, you can explore our data cleansing services to streamline your data cleaning process and maintain high data quality across systems.
- Data Management Platforms: Major vendors such as Informatica, SAS, and Ataccama provide comprehensive solution sets that integrate data quality control with data governance and master data quality management.
- Open source libraries (Python/R): With high flexibility, libraries such as Pandas (Python) or dplyr (R) help organizations build custom data quality control processes that are suitable for complex business logic as well as custom data formats of the business.
Data Quality Control in Practice: Examples
Once you understand the process and how it works, let’s see how data quality control is applied in practice through the following examples:
- E-commerce: It is understandable that an e-commerce company will have to run a process every night to check the product catalog. At this point, the system will automatically detect products that lack images, have zero prices, or have too short product descriptions, helping to prevent poor quality listings from being displayed to customers.
- Finance: A bank will apply real-time data quality control rules when customers open an account. The authentication system will format phone numbers, check addresses, and check information against blacklists to minimize the risk of fraud by trolls.
- Marketing: Before each campaign, the marketing team will have to use data cleaning tools to authenticate and remove invalid or duplicate emails from the sending list, therefore increasing efficiency and protecting the reputation of the sending mailbox domain.
Conclusion
It can be seen that data quality control is a continuous operating process, not a one-time or sudden activity. Properly implementing these processes will help maintain a healthy data asset for your business, ensuring that business analysis and decisions are built on a foundation of accurate information. At DIGI-TEXX, we understand that data quality is the foundation of every valuable insight, helping your business implement effective data quality control. Contact us for advice on the most suitable solution for your organization.
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