What is Data Quality Management? What Are Its Best Practices?

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Data quality management makes data accurate, complete, consistent, and reliable. It is a process that helps businesses unlock crucial hidden insights and drive strategic decision-making.

In the field of analytics, data and quality are intrinsically linked. While data quality directly impacts its usability, reliability, and effectiveness, high-quality data ensures accuracy, completeness, consistency, and relevance, thus providing a solid foundation for meaningful analysis and decision-making.

The significance of business data quality management in today's data-driven landscape is immense. Effective data quality management helps organizations reduce the risk of errors and avert mistakes that can prove costly. Besides, data quality management simplifies the process of complying with regulatory guidelines. When businesses care for data quality, operational efficiency, customer satisfaction, and business innovation follow suit.

This blog will discuss what is data quality management and its best practices to minimize data errors and concerns. 

A study conducted by MIT Sloan notes that bad data can cost as much as 15-25% of total revenue.

Understanding Data Quality Management 

The significance of data quality to businesses can best be understood by the way the marketing department of an ecommerce company leverages data quality for improved outcomes. Consider an e-commerce company analyzing customer data to enhance marketing campaigns. The collated data will have missing values, data entry errors, inconsistent formatting, and outliers. All these together undermine the reliability of data. 

Missing values make it hard to analyze age distribution and tailor strategies. Data entry errors distort conclusions, leading to misguided campaigns. Inconsistent formatting hampers accurate data aggregation and comparisons, potentially causing duplications and flawed calculations. Outliers, such as an age of 150, skew statistics and misrepresent the customer age distribution. 

Data quality management uses various techniques to cleanse data and make it consistent. Techniques such as imputation are employed to fill in the gaps. Inconsistent formatting is managed by enforcing standardized data formats, guidelines, and validation checks. Outliers are detected through statistical analysis and can be corrected or treated separately. Wrong entries are identified through data validation and verification processes. All these techniques can be deployed together to weed out inconsistencies and turn raw data into superior-quality data. 

4 Common Data Quality Issues that Impacts Businesses

Best Practices for Data Quality Management 

Businesses initiating the data quality management process should follow the below-mentioned practices: 

1. Data Profiling and Assessment 

Data profiling and assessment are vital components of data quality management. 

Data profiling offers valuable insights into the characteristics and reliability of data. It helps organizations analyze data from various sources, unveil patterns, and find anomalies, and inconsistencies. 

Data assessment, on the other hand, helps to evaluate data quality against predefined metrics and standards. It maps data to  business rules, integrity constraints, and quality thresholds to gauge its alignment with desired standards. 

How to perform data profiling and assessment? 

  • Identify the data sources: Determine the source of your data and then understand the structure and format for each data source.
  • Gather Metadata: Collect metadata including field names, data types, lengths, formats, etc., to understand the context of data.
  • Examine Data Distribution: Analyze the distribution of value within each field/column to check for missing values, outliers, or unexpected patterns. 
  • Assess Data Completeness: Evaluate the completeness of data by determining the percentage of missing data and assess its impact on analysis.
  • Validate Data Accuracy: Verify the accuracy of data through cross-referencing data with authoritative sources as benchmarks.
  • Evaluate Data Consistency: Check for inconsistencies in the data such as formatting issues, incorrect unit of measurement, or inaccurate coding schemes. 
  • Document Findings: Document your observations, insights, and issues identified during data profiling and assessment. 

2. Data Cleansing and Standardization

Data cleansing and standardization are integral to data quality management, ensuring accurate and consistent data. 

Data cleansing involves identifying and rectifying errors, inconsistencies, and inaccuracies within a dataset. For instance, in a customer database, data cleansing may involve merging duplicate records or validating addresses for accurate contact information.

Data standardization focuses on establishing uniform formats, values, and structures across the data. This includes standardizing naming conventions, units of measurement, date formats, and categorization schemes.

Data cleansing and standardization together support regulatory compliance, such as adhering to data protection regulations or financial reporting standards. They reduce data-related risks and support data governance efforts by establishing consistent data quality practices.

Process for Data Cleansing and Standardization

  • Review Data Profiling and Assessment Findings: Refer the findings and understand the specific data quality issues that were identified.
  • Prioritize Data Quality Issues: Focus on resolving issues that have the greatest impact on data quality and require more attention.
  • Develop a Data Cleansing Plan: Create detailed steps and actions specifically for addressing the data quality issues depending on the nature of specific issues.
  • Handle Missing Data: Implement strategies to address missing data and this involves inputting missing values, deleting records with excessive missing data.
  • Remove Duplicates: Apply techniques to identify and eliminate duplicate records using key fields and advanced algorithms.
  • Validate and Verify Cleansed Data: Ensure that the identified data issues have been addressed and validated against predefined rules.
  • Document Changes and Processes: Maintain a record of changes made during data cleansing and standardization process for further processes.

3. Data Validation and Verification

Data validation and verification are pivotal components of data quality management, ensuring accurate and reliable data. Data validation confirms that data meets predefined rules, constraints, and criteria. It involves checking data types, validating ranges, ensuring uniqueness, and verifying referential integrity. 

For example, in a customer database, data validation ensures phone numbers are correctly formatted, email addresses are valid, and numerical values fall within acceptable ranges.

On the other hand, data verification focuses on confirming data accuracy and completeness through comparison and reconciliation. It entails cross-referencing data from multiple sources or performing manual checks for consistency. For instance, in a sales system, data verification reconciles inventory counts with physical inventory to identify discrepancies.

Data validation and verification support regulatory compliance, data governance, and risk mitigation efforts. They help maintain data quality standards, ensure compliance with industry regulations, and identify potential data-related issues or anomalies.

How to Perform Data Validation and Verification?

  • Define Validation Rules: Establish a set of rules or criteria which includes checks on data type, range, formats, referential integrity, etc., specific to your data and intended use. 
  • Implement Data Validation Checks: Apply the defined validation rules to the cleansed data and use techniques such as automate scripts, custom algorithms, to validate the data.
  • Validate Data Relationships: Verify the relationship between different data elements or entities and check if the data adheres to business rules, constraints, and other dependencies.
  • Conduct Statistical Analysis: Deploy statistical techniques such as data distribution analysis, regression analysis, or hypothesis testing to uncover data discrepancies.
  • Perform Sample Spot Checks: Randomly sample records and cross-check them against original sources of documentation to ensure consistency. 
  • Document Validation Results: Maintain a record of validation rules applied, the findings, and any issues or discrepancies discovered.

4. Data Governance and Stewardship

Data governance and stewardship are essential components of effective data management, ensuring the availability, integrity, and security of data within an organization. Data governance encompasses the framework and processes that govern how data is managed, controlled, and utilized. It involves defining policies, procedures, and guidelines for data management, as well as assigning roles and responsibilities.

Data stewardship, on the other hand, focuses on implementing and executing data governance practices. Data stewards are responsible for managing and safeguarding data assets, ensuring compliance with data governance policies, and resolving data-related issues as they arise.

Both data governance and stewardship establish data standards, ensuring consistent data quality across systems and departments. This promotes data transparency, enabling better decision-making and analysis based on reliable and trustworthy data. 

Process involved in Data Governance and Stewardship

  • Establish Data Governance Framework: Identify key stakeholders, define data governance framework, and establish governance policies.
  • Designate Data Stewards: Assign data stewards to act as custodians of data, ensuring its quality, integrity, and compliance with data governance policies.
  • Define Data Standards: Establish standards and guidelines to ensure data consistency and uniformity across the organization.
  • Implement Data Governance Processes: Implement processes and workflows that includes defining data entry procedures, data charge management, data access controls, & data retention policies.
  • Document Metadata: Create a metadata repository that documents data definitions, data lineage, data sources, data transformations, etc., for data governance and stewardship activities.
  • Establish Data Privacy & Security Measures: Implement data security measures to protect sensitive data and ensure compliance with data protection rules and industry standards.
  • Continuously Improve Data Governance: Regularly collect feedback from data stewards, data users, and other stakeholders to refine data governance policies and practices.
Data Quality Management ROI

5. Data Integration and Quality Monitoring 

Data integration is the process of consolidating data from various sources or systems into a unified and consistent view. It ensures the accessibility, reliability, and usability of data across the organization. By integrating data from different departments, organizations can eliminate data silos, enhance consistency, and gain comprehensive insights for informed decision-making.

Quality monitoring involves ongoing surveillance and assessment of data to maintain its accuracy, completeness, and adherence to quality standards. Through checks, controls, and validation processes, data quality issues can be identified and resolved promptly.

Data integration and quality monitoring work hand in hand to optimize data utilization, enhance operational efficiency, and foster data-driven decision-making. By ensuring reliable and high-quality data, organizations can achieve better outcomes, maximize business opportunities, and maintain a competitive edge in the dynamic business landscape.

How to perform Data Integration and Quality Monitoring? 

  • Identify Integration Requirements: Identify data sources to be integrated, frequency of integration, target systems or databases where the integrated data will be stored.
  • Design Data Integration Architecture: Consider various data formats, data transformative requirements, data mappings, and integration technologies before creating the architecture.
  • Extract, Transform, and Load (ETL): Develop and implement ETL processes to extract data from source systems, transform it to consistent format, and load it to target system or repositories.
  • Data Mapping and Transformation: Establish relationships between data elements and perform data transformation through the defined mapping to ensure consistency and compatibility across integrated datasets.
  • Implement Data Integration Tools: Utilize appropriate tools or platforms to streamline the integration process.
  • Establish Data Quality Monitoring Framework: Define data quality metrics, thresholds, and monitor processes to identify and address data quality issues that may arise during or after integration.
  • Establish Data Quality Remediation Processes: Review data quality reports, conduct data quality assessments, and implement corrective actions to improve the process further. 


Data quality management is an ongoing process that requires commitment, collaboration, and continuous improvement. It is a continuous journey towards maintaining data integrity and quality. By embracing these best practices and fostering a data-driven culture, organizations can harness the true value of their data and stay ahead in a competitive business landscape.

The first task for businesses, therefore, is to prioritize data quality management.  For this, they need to establish clear data governance policies, implement robust data validation processes, invest in data cleansing tools, and regularly audit data sources

At Phygital Insights we specialize in providing top-notch data quality management services to keep your data accurate, reliable, and consistent. Our clients partner with us to unleash the true value of their data. Contact us to know more.

Article by

John is a seasoned data analytics professional with a profound passion for data science. He has a wealth of knowledge in the data science domain and rich practical experience in dealing with complex datasets. He is interested in writing thought-provoking articles, participating in insightful talks, and collaborating within the data science community. John commonly writes on emerging data analytics trends, methodologies, technologies, and strategies.



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