Best-Practices

An Ultimate Guide to Data Quality Management & Its Best Practices

Data is an invaluable strategic asset that can make or break a company’s growth. Quality data means competitive advantage, in-depth insights, better analytics, informed decisions, and greater opportunities. If we don’t have quality data, we will miss all those benefits. So, organizations must prioritize data quality management to drive innovation. In addition, if they want to implement Artificial Intelligence like conversational AI into their projects, they must hold quality data, without which their AI investments go futile.


Data that is duplicated, inconsistent, incomplete, inaccurate, and lacking precision are the causes of data quality issues. To cope with or tackle these issues, businesses must adopt intelligent data quality management (iDQM) solutions or tools.

Ask the Experts


What is data quality management?

Data Quality Management (DQM) is achieving the highest data quality level by implementing processes and rules around it, in order for them to be consistent with accuracy, reliability, and up-to-date. It is the collection of various processes and techniques to ensure that high-quality data continues during the entire data lifecycle.

How to implement data quality management processes?

The key aspects of data quality management include:

Data Profiling

Scrutinizing the data so that we can better understand its quality with respect to structure, relationship, and content. Also, it helps in identifying discrepancies and inconsistencies.

Data Cleansing

Rectifying or deleting the mismatched, incomplete or duplicate data thereby establishing the good quality of the dataset.

Data Validation

Rules and checks are implemented to ensure that the organization’s data meets pre-defined data quality standards and the intended use.

Data governance

It involves policies, procedures, and responsibilities that ensure data quality across the organization. 

Data Integration

It ensures that data from various sources are consolidated correctly for uniformity, reliability, accuracy, and consistency.

Data Monitoring

It ensures that data from various sources are consolidated correctly for uniformity, reliability, accuracy, and consistency.

Best Practices of Data Quality Management

Best practices in Data Quality Management are crucial as they provide a structured approach to managing data. This helps organizations in ensuring accurate, consistent, and reliable data. In addition, they help organizations maintain trust with stakeholders, avoid costly errors, and make informed decisions. Implementing these best practices can streamline operations, enhance data integrity, and drive better outcomes, ultimately leading to the best utilization of data assets.

Lean Data Governance Framework

A lean data governance framework is a set of lean principles, policies, and procedures for overseeing the way an organization stores, manages, shares and uses its information. It means reliable, secure and efficient usage of data. This framework emphasizes defining roles, responsibilities, and accountability to ensure data integrity and compliance with regulations. Data governance ensures that master data management practices are consistent, align with business goals, and support decision-making.

Regular Data Quality Audits

Routine master data quality audits and checks involve systematic reviews of all or parts of the data to check its accuracy, completeness and compliance. Through these audits, discrepancies, inconsistencies and potential risk can be identified. Frequent Audits, by carrying out audits regularly, organizations can fix data issues in time and avoid poor quality of data as well as make sure that policy is adhered to every single day. This initiative ensures that data is reliable and supports informed decision-making.

Validation Rules and Checks

Validation rules and checks are used to ensure that data entered into systems meets well-defined standards and criteria. This includes constraints on the formats, ranges of values and even logical relations. By performing strong validation checks to prevent wrong or unfinished data from being processed. It minimizes errors and in turn, lifts the quality of data overall.

Data Standardization

This refers to the process of transforming data into a set format across systems and processes. Standardized data definitions and formats ensure compatibility and high quality. Standardization also makes it easier to integrate and unify data, eliminates inconsistencies as well as improves data accuracy. It allows data from disparate sources to be combined and compared, which is an added benefit for analytics or reporting.

Cleansing and Maintenance Of Data

Data cleansing and maintenance refers to the identification of errors, inconsistencies or any outdated information in a dataset for rectification. In this process, data is maintained consistent and trustworthy every single time. Cleansing tasks such as removing duplicates and fixing errors, enhance the quality or usability of data. It also involves keeping records and managing updates to make the data relevant.

Ongoing Monitoring and Reporting

The continuous monitoring and reporting of the data quality is tracking the quality of the data and generating reports on that. It’s like data quality assessment or data health assessment. This approach identifies issues in real time and generates insights into data quality trends. Besides, regular monitoring data management practices ensure that they remain effective and allow anomalies to be addressed before they turn into bigger problems. Reporting ensures stakeholders stay ahead of progress and helps drive data-driven decision-making

Data Source Verification

Validating origins and reliability is data source verification, the last step before integrating or analyzing any datasets. When data comes from trustworthy and true sources, this ensures that the quality of overall data remains intact. Verification is the process of confirming source integrity, consistency and authenticity. This practice avoids the handling of inaccurate data and helps ensure reliable and trusted outcomes from our data.

User Training and Awareness

User training and awareness sessions guide staff on how to effectively manage data, policies, and tools. It is imperative to conduct training so that users can know their responsibilities towards data integrity and confidentiality. Data awareness initiatives prevent the exploitation of vulnerabilities and minimize errors associated with misuse in data processing activities. Businesses will have good users that polished data governance and quality goals.

Backup and Data Recovery

Backup and Data Recovery practices include making copies of data and ensuring that they are kept properly so you can recover from them when needed to achieve no loss or corruption. Frequent backups allow you to restore fresh data in case of system failure or disaster. A data recovery plan should have steps to ensure the integrity of restoring your system, such that it runs smoothly within no time with zero downtime and without any hassle for continuing on business functionalities.

Access controls and permissions

This includes access to data, who can see, change or manage what is within an organization (access controls and permissions). Data protection against unauthorised access and breaches by implementing RBAC (Role-based Access Control) with permissions. Implementing and enforcing proper access controls weed out unauthorized personnel from managing sensitive data while maintaining data quality and improving data security.

Wrapping Up

Data quality management is an ongoing endeavour. Data comes from all directions and is placed into business applications in a variety of forms both unstructured and semi-structured. And the volume of data is ever-growing. So, organizations must implement data quality measures to organize and improve the quality of data as soon as it is acquired. Utilize the best data quality management tools in the market to ensure your enterprise data is consistent, high quality, accurate, and especially, aligned to your organization’s goals.

Looking for the best tool that resonates with your organization’s data quality requirements? Contact us to schedule a demo.