What Is Data Quality Management?
Data quality management refers to the processes, actions, systems and technologies that maintain an organization’s data standard. Quality management processes facilitate the collection, maintenance and use of accurate, reliable data. Prioritizing data quality control is essential to cultivating organizational data fit for business applications like analysis, strategy design, performance and customer engagement.
Understanding Data Quality Management
There are various elements, practices and systems that contribute to successful data quality assurance.
Data Quality Management Frameworks
Key frameworks to consider when establishing internal quality management infrastructure include:
- Data validation
- Data standardization and integration
- Data cleansing
- Data profiling
- Metadata management
- Data monitoring and reporting
- Data governance
When you implement these methodologies throughout your data’s life — from extraction to deletion — you can carefully control its quality.
Benefits and Uses of Data Quality Management
Applying data quality techniques to your organization’s datasets and databases can have various advantages. You can use reliable, accurate data to enhance:
- Data regulation compliance
- Business decisions and strategies
- Business intelligence and analysis
- Risk management
- Organizational performance and efficiency
- Customer relationship management
- Cost management and financial decisions
- Data governance
- Internal processes and policies
- Your organization’s reputation and competitive advantage
Tools and Solutions for Data Quality Management
There are many tools available on the market to empower your organization’s data quality management. To select the right tool for optimal quality performance, you must understand what you want to achieve with your management efforts.
Data Quality Management Metrics
The key metrics and characteristics of efficient data quality management are:
- Accuracy
- Validity
- Trustworthiness
- Consistency
- Accessibility
- Integrity
- Wholeness
- Timeliness
- Applicability
- Uniqueness
Your institutional systems and policies should enable you to collect and maintain data that meets these metrics.
Anomalo’s Approach to Data Quality
With these metrics in mind, select a management tool that can enable you to control and gain insight into data quality levels in your organization. Choose a platform that can empower you to fulfill management techniques like validation, cleansing, standardization and governance. At Anomalo, we have a comprehensive outlook on data management and understand how different layers and frameworks cooperate to generate and sustain quality data.
Features we offer for enhanced and extensive data quality management include:
- AI for rapid error and anomaly detection
- Root cause analysis
- Data quality checks
- Deep data monitoring and alerts
- Visualization for analysis
- Automated data lineage tracking
- Diverse system integrations
- Data reporting
Anomalo Is an Advanced Data Quality Assurance System
A well-rounded data quality management strategy requires dedication to various frameworks, metrics and outcomes. Automated software streamlines data management and executes repeated activities like checks and issue detection for consistent quality. Find out more about how Anomalo can elevate your organization’s data quality. Request a demo today!
