Best practices for comprehensive data quality management
Posted: Tue Jan 21, 2025 10:13 am
Comprehensive data quality management starts with a well-defined data management strategy. Here are some best practices that will help you increase your efficiency in 4 steps.
All companies struggle to manage data quality comprehensively . Most companies use only a fraction of the information available to their organization to gain insights that can help improve their overall business performance. And they often fail to realize the associated costs of poor, inaccurate, or inconsistent data.
The significant amount of revenue lost due to working with poor quality information is forcing companies to change their data quality management strategy. The trend is to move from doing data rich people database and optimization from time to time, to approaching it as a comprehensive data quality management performed on an ongoing basis. It is a process of continuous quality improvement , which covers all levels of the organization. Let's look at some of the best practices that can be used.
Data quality as an essential part of MDM
How to increase the efficiency of comprehensive data quality management
There are 4 very effective ways to move towards a comprehensive data quality management approach. They are as follows:
1. Commission or conduct a Data Quality Assessment
The right way to begin addressing end-to-end data quality management issues is by conducting a complete analysis of your current data situation .
You need to know what type of information contains errors or inconsistencies. Know if you have duplicate data or frequently missing fields. These may not be easy to identify and correct, as the data could already be in backups, or it could also be data that is received from external sources such as:
Suppliers.
External applications.
Social media, such as Facebook and Twitter.
2. Build a Data Quality Firewall
Your organization's data is an asset that contains strategic information and should be treated as such. Like any other company asset, the data contained in the organization's information systems has financial value. The value of data increases and correlates with the number of people who are able to make use of it.
All companies struggle to manage data quality comprehensively . Most companies use only a fraction of the information available to their organization to gain insights that can help improve their overall business performance. And they often fail to realize the associated costs of poor, inaccurate, or inconsistent data.
The significant amount of revenue lost due to working with poor quality information is forcing companies to change their data quality management strategy. The trend is to move from doing data rich people database and optimization from time to time, to approaching it as a comprehensive data quality management performed on an ongoing basis. It is a process of continuous quality improvement , which covers all levels of the organization. Let's look at some of the best practices that can be used.
Data quality as an essential part of MDM
How to increase the efficiency of comprehensive data quality management
There are 4 very effective ways to move towards a comprehensive data quality management approach. They are as follows:
1. Commission or conduct a Data Quality Assessment
The right way to begin addressing end-to-end data quality management issues is by conducting a complete analysis of your current data situation .
You need to know what type of information contains errors or inconsistencies. Know if you have duplicate data or frequently missing fields. These may not be easy to identify and correct, as the data could already be in backups, or it could also be data that is received from external sources such as:
Suppliers.
External applications.
Social media, such as Facebook and Twitter.
2. Build a Data Quality Firewall
Your organization's data is an asset that contains strategic information and should be treated as such. Like any other company asset, the data contained in the organization's information systems has financial value. The value of data increases and correlates with the number of people who are able to make use of it.