Research has outlined that poor data quality can cost companies between 15% and 25% of their business revenue. Organizations are often plagued by poor data quality management practices that lead to poor decision-making ability.

Every day, managers drive operational decisions based on data. When this data is poorly defined, incomplete, or wrong, it can lead to immediate consequences – wasted time, dissatisfied customers, and more challenges in strategy execution. In a nutshell, the usefulness of data can be determined based on its quality.


Why Data Quality Monitoring and Management Is Essential?

Data quality monitoring is essential to ensure that the data is reliable and accurate for business use. By monitoring data quality, businesses can identify and fix the problems before they cause any further business disruptions. In essence, data quality management assesses the usability of data for the intended purpose. Here are the top reasons why you need data quality monitoring and management:

  • Improved business functions: When data is monitored regularly and well-managed, it streamlines the core functions across different business departments. This is important because it ensures that the key departments operate better together.
  • Complete and accurate data: Data accuracy and completeness are essential for making the right decisions and adhering to compliance and regulations. The data quality monitoring and management standards pertain to factors like accuracy, timeliness, consistency, and completeness to ensure that data is accurate and complete.
  • Competitive advantage: Low-quality data leads to customer distrust, dissatisfaction, and ultimately bad business reputation. On the contrary, efficient data management helps enhance brand reputation and realize competitive advantage.
  • Reduced cost: When the data errors are identified and corrected in time, it can save costs associated with data storage errors. It also reduces the cost of data evaluation and analysis.
  • Enhanced decision-making process: From supply chain management to customer relationship management, data quality management can directly impact the organization’s performance. With good data quality, organizations can examine trends and patterns correctly to establish future strategies.
  • Better operational efficiency: It improves operational efficiency by providing holistic visibility into the data that is being gathered and processed across every touchpoint. This visibility helps in proactively identifying and addressing data issues.

Also Read: Data Management Mistakes That Derail Business Intelligence Projects


Best Practices to Ensure Good Data Quality

Now that the importance of high-quality data is evident, it is time to solidify the foundation of data management. Here are some of the best practices and techniques to ensure successful data quality monitoring and management.

  • Ensure data governance: Data governance sets the specific roles, processes, metrics, and standards to utilize the data in a secure and efficient manner. In sum, implementing a governance system defines the roles and responsibilities to ensure good data quality.
  • Involve all departments: It is essential to involve all departments within an organization in the data quality management process to ensure a seamless flow of information and avoid confusion.
  • Embrace automation: Manual data entry can lead to human errors and poor data quality. To avoid these errors, using automation tools for data entry is critical. These tools can be used with organization-specific rules and integrations to ensure data quality.
  • Define key performance indicators (KPIs): Like other analytical purposes, employing KPIs helps assess the data quality management performance and success.
  • Define a data glossary: Technical and data quality-related terms should be defined in a way that is easy to access and navigate. This ensures a common understanding of data quality management across the organization.
  • Find root causes for data quality issues: Reviewing current data helps find the root of quality issues. This further helps lay the groundwork for working with high-quality data and identifying the common issues that can be prevented in the future.
  • Integrate data monitoring and management with business intelligence: Implementation of data quality management along with business intelligence helps automate the workflows and ensures better strategic decisions.


Features Integral to Monitoring and Managing Data Quality

Quality management tools help assess consistency, accuracy, completeness, and overall data quality. These tools/platforms automate the remediation and quality control with the below-mentioned features:

  • Profiling: It enables users to identify data quality issues and thoroughly comprehend them.
  • Connectivity: It applies quality rules to ensure proper connectivity and integration. Fundamentally, it ensures an easy connection of data from multiple sources, including internal, external, on-premise, cloud, etc.
  • Data visualization and monitoring: It helps monitor the data quality closely via interactive data visualizations.
  • Management of metadata: Data quality control starts with metadata management. This provides the definitions and documentation to ensure that data is properly consumed and understood across the organization.


Get Started with Best-in-Class Data Quality Management

Choosing a solution that offers all the aforementioned features is critical to the success of data quality management initiatives. With the growing variety, velocity, and volume of data, organizations must manage their enterprise data efficiently while adhering to compliance within the digital ecosystem. Here is why you should choose Trinus for all your data quality management needs:

  • Cost-effective engagement model: You can choose fixed-price, project-based, and managed services according to your business needs.
  • Business-aligned solutions: The data quality services are offered in alignment with your business objectives and IT roadmap to drive optimal outcomes.
  • On-demand scalability and flexibility: It is easier to ramp up/down the services even when leveraging a large pool of resources.


Get in touch with us to consult your data quality management needs today.