Data is important, but the quality of the data collected is more important. With the data collected, you can make business decisions, which is why it’s important to pay attention to your data quality. You should measure the quality of your data based on consistency, accuracy, completeness, timeliness, uniqueness, and so on. Here we will discuss the best practices and stages of data quality management.
Best practices of data quality management
Data quality management is a process that involves rational step-by-step execution. These steps normalize the data management practices which are used to incorporate data quality techniques into the business.
The best practices include:
Prioritize data quality
Low data quality can cause a lot of issues. The first step is to ensure that your employees understand. Then, you create an enterprise-wide data strategy. Thirdly, design user roles with clear privileges and liabilities. Fourthly, establish a data quality management process and finally, have a dashboard to monitor the status quo. Incorporating all of this into your business process will help prioritize data quality management.
Data entry automation
Manual data entry is one of the root causes of poor data quality. Human errors are sometimes inevitable, but automating data entry processes can help reduce it. Implementing data entry automation will help increase data quality.
Preclude duplicates
As you should know, prevention is better than resolution. Precluding duplicates is a better option for improving data quality. Implementing duplicate detection rules and regular cleaning will help identify similar entries that already exist in the database. Then, you can ban creating another one or merge the entries.
Data quality management process
Data quality management revolves around certifying that the data is relevant, reliant, and accurate. It is a process aimed at accomplishing and preserving high data quality. Its main stages involve:
1. Gather data and establish data quality rules
After collecting and analyzing data, tables need to be created in database design. Then you scrutinize what data will be held in each table and what fields will be integrated into each table. In a situation whereby there are massive amounts of data in existence, you have to determine what is relevant to keep and what will be withheld in each table of the database.
2. Assess the quality of data
The business/technical rules that have been created and defined should be checked. The development of quality rules is essential for the success of the data quality management process. You should enforce these rules to make sure they will find and stop compromised data from corrupting the whole set.
3. Resolve data quality issues
At this stage, data quality rules should be reviewed again. The review process will help determine if the rules need to be modified or updated, and it will help resolve data quality issues. Once the data quality issue is resolved, vital business processes and functions should proceed more efficiently and accurately.
4. Monitor and control data
Data quality management is a continuous process that involves regular review of data quality rules. Monitoring and controlling data is very important in this time of constant change within the business environment.
Data quality can be ensured by engaging in effective data management tools. You have to consider a quality management solution that closely aligns with your unique business objectives. Data quality management involves many aspects and most often requires professional assistance. At ISU Corp we are always ready to help, contact us today!
