Data is very important in every organization, and this is the reason why businesses need to rely on practices that would help manage it effectively. Data quality management (DQM) comprises of the best practices that help businesses to acquire and maintain data of high quality. What is data quality? Data quality is that kind of data that can serve the purpose it was intended to without compromise. Such quality can only be achieved if the company is willing to take advantage of DQM. Effective DQM will help in the acquisition of data, processing, distribution, and protection from theft, among other cyber-crimes. To make sure that your organizational data is well-managed, use the following tips to improve your DQM practices.
1. Ensure Data Availability
One of the main roles of DQM is the acquisition of data because there is no way you are going to manage data if it’s not available. Therefore, use every possible way to garner information both from organizational databases and from third party sources. Some of the sources you could use include ERP applications, legacy file structures, ERP applications, partner systems, data warehouses, or EDI transactions, among others. Having as much data as you can might make DQM more effective that it would be, with fewer data.
2. Work With The Right People
For proper functioning of data quality management, you will need to work with people who understand the processes involved and those who value data. In this case, hire data stewards to oversee the data management programs. The people involved include DQM project manager, data analyst, and a change manager. All these will handle different roles and responsibilities to ensure DQM works as expected, and the organization can enjoy maximum benefits.
3. Focus On Data Profiling
With DQM, you will need to continuously improve the quality of data if you want to get the best out of it. You do this by data profiling, which involves carrying reviews on available data, comparing data from different sources, using statistical models to evaluate it, and providing reports of the outcomes. These processes will help you to identify areas where your data needs improvement or areas that require advanced programs for proper management.
4. Define Quality Rules
You will also need to define the quality of data you want your data to reflect. This is where you incorporate elements that describe data quality. The elements include accuracy, consistency, completeness, orderliness, timeliness, and integrity, among others. Set the rules you want to see for each data field and if possible, award a threshold that must be met for each. This is an essential step in making sure that DQM works perfectly because data will only be considered viable after complying with the rules set.
5. Measure And Repair
With DQM, you will be able to measure your data using different metrics for consistent quality of data. The rules you have set for each data field should help to measure if the data you have is compromised in any way. Identify and capture patterns during recording or in every process of DMQ, and it will help you define specific characteristics of certain data. Sometimes it is good to use software to measure and provide a report on the quality of data, and once a compromise is identified, look for ways to solve it. Start by identifying the root cause of the data issue before formulating a remediation plan. It is okay to review your rules to make them better and perfect quality of data, which translates to an effective DQM.
Importance Of Quality Data Management
• Better decision making through the use of reliable data
• Ability to comply with regulations due to improved transparency
• Improved security for organization information from advanced data management
• Increased profitability because quality data can easily be monetized
• Presence of accountability and enhanced performance by all parties
• Easy measurement of data and provision of room to improve data management