Data Quality Improvement Process

The importance of high-quality data cannot be overstated in today’s world. Inaccurate, incomplete, or inconsistent data can lead to erroneous conclusions, flawed decision-making, and even significant financial losses. To avoid these potential pitfalls, organizations must have a robust data quality improvement process in place. This article provides an overview of such a process, including the steps involved, the tools and techniques used, and the benefits that can be expected. By implementing the practices outlined in this article, organizations can ensure that their data is accurate, reliable, and consistent, leading to better business outcomes and a competitive advantage in the marketplace.

What is a Data Quality Improvement Process?

A data quality improvement process is a set of activities and strategies aimed at improving the quality of data in an organization. The process involves identifying data quality issues, assessing the impact of these issues on the organization, and taking steps to resolve them.

The key steps in a data quality improvement process typically include:

1. Data profiling: This involves analyzing data to understand its structure, content, and quality.
2. Data cleansing: This involves identifying and correcting errors, inconsistencies, and redundancies in the data.
3. Data standardization: This involves establishing a set of rules and guidelines for data entry, formatting, and validation to ensure consistency across the organization.
4. Data enrichment: This involves enhancing the value of data by adding additional information or context.
5. Data governance: This involves establishing policies, procedures, and controls for managing data throughout its lifecycle.
6. Data monitoring: This involves continuously monitoring data quality and taking corrective action when issues are identified.

By implementing a data quality improvement process, organizations can ensure that their data is accurate, consistent, and reliable, which can help to improve decision-making, reduce costs, and increase efficiency.

An approach

1. Define data quality requirements: Clearly define the data quality requirements for each data element, including accuracy, completeness, consistency, and timeliness. Establish data quality standards that define what constitutes good quality data, and how data quality will be measured and monitored.
2. Data profiling: Conduct data profiling to analyze the structure, content, and quality of your data. This will help identify data quality issues, such as missing data, duplicates, inconsistencies, and incorrect values.
3. Data cleansing: Data cleansing is the process of identifying and correcting errors, inconsistencies, and redundancies in your data. This can be done through manual data cleansing, automated data cleansing, or a combination of both.
4. Data validation: Data validation involves checking data for accuracy, completeness, and consistency against predefined rules and standards. This can be done through manual validation or automated validation.
5. Data governance: Data governance involves establishing policies, procedures, and controls for managing data throughout its lifecycle. This includes data quality standards, data access policies, data security, and data retention policies.
6. Data stewardship: Data stewardship involves assigning ownership and responsibility for data quality to specific individuals or teams within the organization. Data stewards are responsible for monitoring data quality, identifying data quality issues, and taking corrective action.
7. Data quality monitoring: Continuous data quality monitoring involves tracking data quality metrics, identifying data quality issues, and taking corrective action when necessary. This can be done through automated monitoring or manual monitoring.

By adopting these approaches to manage data quality, organizations can ensure that their data is accurate, consistent, and reliable, which can help improve decision-making, reduce costs, and increase efficiency.

General opinion

The general opinion of implementing a data quality process in an enterprise is positive. Organizations recognize the importance of high-quality data for making informed business decisions, improving operational efficiency, and meeting regulatory compliance requirements. Here are some reasons why implementing a data quality process in an enterprise is seen as beneficial:

1. Improved decision-making: High-quality data is essential for making informed business decisions. By implementing a data quality process, organizations can ensure that the data used for decision-making is accurate, complete, and consistent.
2. Increased efficiency: High-quality data can improve operational efficiency by reducing the time and effort required to find and correct data errors. By implementing a data quality process, organizations can streamline their data management processes and reduce the risk of errors and rework.
3. Cost savings: Poor data quality can result in significant costs for organizations, including lost revenue, increased operational costs, and regulatory fines. By implementing a data quality process, organizations can reduce the cost of poor data quality and achieve cost savings through improved efficiency and accuracy.
4. Regulatory compliance: Many regulatory requirements, such as GDPR, HIPAA, and SOX, require organizations to maintain high-quality data. By implementing a data quality process, organizations can ensure that they meet regulatory compliance requirements and avoid penalties and fines.
5. Competitive advantage: High-quality data can provide a competitive advantage by enabling organizations to make better-informed decisions, deliver better products and services, and respond more quickly to market changes. By implementing a data quality process, organizations can improve their competitive position in the marketplace.

In summary, implementing a data quality process in an enterprise is seen as beneficial for improving decision-making, increasing efficiency, achieving cost savings, meeting regulatory compliance requirements, and gaining a competitive advantage. As a result, more and more organizations are investing in data quality initiatives to improve the accuracy, completeness, and consistency of their data.

Pros and cons

Here’s a crosstab of some of the pros and cons of implementing a data quality process in an enterprise

Pros of Data Quality Process	Cons of Data Quality Process
Improved decision-making based on reliable data	Costly and time-consuming
Increased operational efficiency and reduced risk of errors and rework	Lack of data governance policies and procedures
Cost savings through improved efficiency and accuracy	Difficulty in identifying all data quality issues
Regulatory compliance and reduced risk of penalties and fines	Resistance from stakeholders
Competitive advantage through better-informed decisions and improved products and services	Potential for increased complexity and challenges in integrating multiple systems and data sources

It’s important to note that the pros and cons of implementing a data quality process will vary depending on the organization’s specific needs, goals, and challenges. However, by understanding these pros and cons, organizations can make informed decisions about whether to invest in a data quality process and how to mitigate potential challenges. By balancing the benefits and costs of a data quality process, organizations can achieve their data quality objectives while minimizing any negative impacts.

How can we develop and maintain a data quality improvement practice?

Developing and maintaining a data quality improvement practice involves several key steps. Here are some best practices to follow:

1. Establish data quality goals and metrics: Identify the specific data quality goals and metrics that are important to your organization, such as accuracy, completeness, consistency, and timeliness. Establishing clear goals and metrics will help to ensure that everyone in the organization understands the importance of data quality and is aligned around the same objectives.
2. Develop data quality policies and procedures: Develop policies and procedures that outline how data quality will be managed within the organization, including data quality standards, data governance, data stewardship, and data quality monitoring.
3. Assign data quality roles and responsibilities: Identify the roles and responsibilities for managing data quality within the organization, including data stewards, data analysts, and data quality champions. Assign clear ownership and accountability for data quality to ensure that everyone understands their role in maintaining data quality.
4. Implement data quality tools and technologies: Implement data quality tools and technologies that can help to automate and streamline data quality processes, such as data profiling, data cleansing, data validation, and data quality monitoring.
5. Train and educate staff: Train and educate staff on data quality best practices, policies, and procedures. Provide ongoing training and support to ensure that everyone is up to date with the latest data quality practices and technologies.
6. Measure and monitor data quality: Establish a process for measuring and monitoring data quality metrics, such as data accuracy, completeness, consistency, and timeliness. Regularly review data quality metrics and take corrective action when necessary.
7. Continuously improve data quality: Establish a process for continuously improving data quality, such as conducting root cause analysis on data quality issues, implementing corrective actions, and tracking the effectiveness of data quality improvement initiatives.

By following these best practices, organizations can develop and maintain a data quality improvement practice that ensures that data is accurate, consistent, and reliable. This can help to improve decision-making, reduce costs, and increase efficiency.

The features of a data quality improvement process across its pros and cons.

Features	Pros	Cons
Clear data quality requirements	– Ensures everyone understands the importance of data quality and is aligned around the same objectives. – Provides a clear baseline for measuring data quality.	– Can be time-consuming and resource-intensive to develop and maintain.
Data profiling	– Helps to identify data quality issues, such as missing data, duplicates, inconsistencies, and incorrect values. – Provides insights into the structure, content, and quality of the data.	– Can be complex and require specialized skills and tools. – May not identify all data quality issues.
Data cleansing	– Improves the accuracy, consistency, and completeness of the data. – Reduces the risk of making decisions based on incorrect data.	– Can be time-consuming and resource-intensive to implement. – May result in data loss or data transformation.
Data validation	– Ensures data accuracy, completeness, and consistency against predefined rules and standards. – Reduces the risk of making decisions based on incorrect data.	– Can be complex and require specialized skills and tools. – May result in false positives or false negatives.
Data governance	– Establishes policies, procedures, and controls for managing data quality. – Provides a framework for managing data quality throughout its lifecycle.	– Can be complex and require significant effort to implement. – May require a cultural change within the organization.
Data stewardship	– Assigns ownership and responsibility for data quality to specific individuals or teams within the organization. – Provides accountability for data quality.	– May require significant effort to implement. – May not be effective if data stewards do not have the necessary skills or authority.
Data quality monitoring	– Provides ongoing monitoring of data quality metrics. – Enables timely identification of data quality issues. – Provides insights into data quality performance over time.	– Can be complex and require specialized skills and tools. – May result in false positives or false negatives.
Continuous improvement	– Provides a process for continuously improving data quality. – Helps to identify areas for improvement. – Ensures that data quality is maintained over time.	– Can be time-consuming and resource-intensive to implement. – May require cultural change within the organization.

By understanding the pros and cons of each feature of a data quality improvement process, organizations can develop a data quality improvement strategy that is tailored to their specific needs and goals. By addressing the cons of each feature, organizations can maximize the benefits of the data quality improvement process and minimize the risks.

How can I manage the data quality improvement lifecycle?

Managing the data quality improvement lifecycle involves several key steps. Here’s a high-level overview of the process:

1. Plan: The first step in the data quality improvement lifecycle is to plan the data quality improvement initiative. This involves identifying the data quality issues, setting goals and objectives, establishing the scope of the initiative, and defining the metrics to measure success.
2. Analyze: The next step is to analyze the data to identify data quality issues. This involves using data profiling tools to understand the structure, content, and quality of the data.
3. Improve: Once data quality issues have been identified, the next step is to improve the data quality. This can involve data cleansing, data validation, and data enrichment.
4. Monitor: After data quality has been improved, it’s important to continuously monitor data quality metrics to ensure that data quality is maintained. This involves setting up a data quality monitoring process that tracks data quality metrics over time.
5. Report: Finally, data quality metrics should be reported to stakeholders within the organization. This involves communicating data quality metrics and issues to stakeholders, such as executives, data stewards, and data analysts.

It’s important to note that managing the data quality improvement lifecycle is an iterative process, and each step in the process may need to be revisited as new data quality issues are identified. By continuously improving data quality over time, organizations can ensure that their data is accurate, consistent, and reliable, which can help to improve decision-making, reduce costs, and increase efficiency.

Roles and responsibilities that are required to manage a data quality improvement process.

Managing a data quality improvement process requires a team effort with clearly defined roles and responsibilities. Here are some of the key roles and responsibilities that are required to manage a data quality improvement process:

1. Data Quality Manager: The data quality manager is responsible for overseeing the data quality improvement process, including planning, executing, and monitoring data quality initiatives. This role involves setting data quality standards and policies, managing data quality projects, and providing leadership and guidance to the data quality team.
2. Data Steward: The data steward is responsible for ensuring the accuracy, completeness, and consistency of the data within their area of responsibility. This role involves defining data quality requirements, identifying data quality issues, and working with the data quality team to resolve data quality problems.
3. Data Analyst: The data analyst is responsible for analyzing data and identifying data quality issues. This role involves using data profiling tools, data visualization tools, and data analytics techniques to identify data quality issues and provide insights into data quality performance.
4. Data Quality Engineer: The data quality engineer is responsible for developing and implementing data quality processes and solutions. This role involves designing and implementing data quality checks, data cleansing processes, and data validation rules.
5. IT Support: The IT support team is responsible for providing technical support and maintaining the data quality tools and systems. This role involves installing, configuring, and maintaining data quality tools, and providing technical support to the data quality team.
6. Business User: The business user is responsible for using data to support decision-making and business processes. This role involves working with the data quality team to ensure that the data used for decision-making is accurate, complete, and consistent.

Each of these roles plays a critical role in managing a data quality improvement process. By defining the roles and responsibilities of each team member, organizations can ensure that everyone is aligned around the same objectives and has a clear understanding of their responsibilities. This can help to ensure that data quality is maintained throughout the data lifecycle and that the organization is able to make informed decisions based on accurate and reliable data.

Typical roadmap with timelines to implement a data quality improvement process.

The timeline for implementing a data quality improvement process can vary depending on the size of the organization, the complexity of the data, and the scope of the data quality improvement initiative. Here is a typical roadmap with timelines for implementing a data quality improvement process:

Phase 1: Planning (4-6 weeks)

• Define the scope of the data quality improvement initiative
• Identify the data quality issues and prioritize them
• Set goals and objectives for the initiative
• Develop a data quality improvement plan
• Identify the data quality team and their roles and responsibilities

Phase 2: Analysis (8-12 weeks)

• Perform data profiling to understand the structure, content, and quality of the data
• Identify the data quality issues and their root causes
• Analyze the impact of the data quality issues on business processes
• Develop a data quality improvement roadmap

Phase 3: Improvement (12-24 weeks)

• Implement data quality rules and validation checks
• Cleanse the data to remove duplicates, inconsistencies, and incorrect values
• Enrich the data with additional information
• Validate the data to ensure accuracy, completeness, and consistency

Phase 4: Monitoring (ongoing)

• Establish a data quality monitoring process
• Track data quality metrics over time
• Identify new data quality issues and address them in a timely manner
• Communicate data quality performance to stakeholders

Phase 5: Continuous Improvement (ongoing)

• Review data quality performance on a regular basis
• Identify areas for improvement
• Update data quality rules and processes
• Implement new technologies and tools to support data quality initiatives

It’s important to note that this timeline is only an example and can vary depending on the organization’s specific needs and goals. Additionally, managing data quality is an ongoing process that requires continuous improvement and monitoring to ensure that data quality is maintained over time.

What software tools are available to improve the data quality process?

There are various software tools available to improve the data quality process. Here are some examples:

1. Data profiling tools: These tools analyze the structure, content, and quality of data to identify data quality issues. Some examples of data profiling tools include Talend, Informatica, and Datawatch.
2. Data cleansing tools: These tools identify and correct errors, inconsistencies, and redundancies in data. Some examples of data cleansing tools include Trifacta, OpenRefine, and Talend.
3. Data validation tools: These tools check data for accuracy, completeness, and consistency against predefined rules and standards. Some examples of data validation tools include Apache Nifi, Talend, and Informatica.
4. Master data management tools: These tools manage and maintain the master data, such as customer data, product data, and supplier data, to ensure consistency and accuracy across the organization. Some examples of master data management tools include Talend, Informatica, and SAP Master Data Governance.
5. Data governance tools: These tools help establish policies, procedures, and controls for managing data throughout its lifecycle. Some examples of data governance tools include Collibra, Informatica, and Talend.
6. Business intelligence tools: These tools help organizations to analyze and visualize their data to support decision-making. Some examples of business intelligence tools include Tableau, Microsoft Power BI, and QlikView.
7. Data quality monitoring tools: These tools continuously monitor data quality metrics and alert users when data quality issues are identified. Some examples of data quality monitoring tools include Talend, Informatica, and IBM InfoSphere Information Governance Catalog.

By leveraging these software tools, organizations can automate and streamline their data quality processes, which can help to improve data quality, reduce costs, and increase efficiency.

How can we use APIs to manage the data quality of databases?

APIs (Application Programming Interfaces) can be used to manage the data quality of databases in several ways. Here are some examples:

1. Data profiling: APIs can be used to retrieve metadata and data samples from databases and analyze the structure, content, and quality of the data. This can help identify data quality issues, such as missing data, duplicates, inconsistencies, and incorrect values.
2. Data cleansing: APIs can be used to automate data cleansing processes, such as identifying and correcting errors, inconsistencies, and redundancies in the data. This can be done through custom scripts or by using third-party data quality tools that provide APIs.
3. Data validation: APIs can be used to validate data against predefined rules and standards. For example, APIs can be used to check data for accuracy, completeness, and consistency against a set of rules or validation criteria.
4. Data enrichment: APIs can be used to enrich data with additional information or context. For example, APIs can be used to retrieve data from external sources, such as social media or weather data, and add it to the database.
5. Data governance: APIs can be used to automate data governance processes, such as establishing data quality standards, data access policies, and data retention policies. APIs can be used to enforce these policies and ensure that data quality is maintained throughout the data lifecycle.
6. Data monitoring: APIs can be used to continuously monitor data quality metrics and alert users when data quality issues are identified. This can be done through custom scripts or by using third-party data quality monitoring tools that provide APIs.

By leveraging APIs to manage data quality of databases, organizations can automate and streamline their data quality processes, which can help to improve data quality, reduce costs, and increase efficiency.

Data quality tools across pros and cons

Here is a crosstab of the most commonly used data quality tools across their pros and cons:

Data Quality Tools	Pros	Cons
Data profiling tools	– Help to identify data quality issues, such as missing data, duplicates, inconsistencies, and incorrect values. – Provide insights into the structure, content, and quality of the data.	– Can be complex and require specialized skills and tools. – May not identify all data quality issues.
Data cleansing tools	– Improve the accuracy, consistency, and completeness of the data. – Reduce the risk of making decisions based on incorrect data.	– Can be time-consuming and resource-intensive to implement. – May result in data loss or data transformation.
Data validation tools	– Ensure data accuracy, completeness, and consistency against predefined rules and standards. – Reduce the risk of making decisions based on incorrect data.	– Can be complex and require specialized skills and tools. – May result in false positives or false negatives.
Master data management tools	– Manage and maintain the master data, such as customer data, product data, and supplier data, to ensure consistency and accuracy across the organization. – Provide a single source of truth for master data.	– Can be complex and require significant effort to implement. – May result in a cultural change within the organization.
Data governance tools	– Establish policies, procedures, and controls for managing data throughout its lifecycle. – Provide a framework for managing data quality.	– Can be complex and require significant effort to implement. – May require cultural change within the organization.
Business intelligence tools	– Help organizations to analyze and visualize their data to support decision-making. – Provide insights into data quality performance over time.	– May not provide a complete picture of data quality. – May not be effective for identifying data quality issues.
Data quality monitoring tools	– Provide ongoing monitoring of data quality metrics. – Enable timely identification of data quality issues. – Provide insights into data quality performance over time.	– Can be complex and require specialized skills and tools. – May result in false positives or false negatives.

By understanding the pros and cons of each data quality tool, organizations can select the tools that best fit their specific needs and goals. By addressing the cons of each tool, organizations can maximize the benefits of the data quality tools and minimize the risks.

How can data science skills help improve data quality in an enterprise?

Data science skills can play a crucial role in improving data quality in an enterprise. Here are some ways in which data science skills can be applied to improve data quality:

1. Data profiling and analysis: Data scientists can use their skills to perform data profiling and analysis to identify data quality issues. They can use statistical techniques, machine learning algorithms, and data visualization tools to identify patterns, inconsistencies, and errors in the data.
2. Data cleansing and transformation: Data scientists can develop and implement data cleansing and transformation processes to address data quality issues. They can use data wrangling techniques, such as data normalization and data imputation, to ensure that the data is accurate, complete, and consistent.
3. Data validation: Data scientists can use their skills to develop and implement data validation rules and processes to ensure that the data is accurate and consistent. They can use statistical techniques, data modeling, and data profiling tools to identify anomalies and validate data against predefined rules and standards.
4. Data governance: Data scientists can help to establish data governance policies and procedures to ensure that the data quality is maintained throughout the data lifecycle. They can use their skills to develop data quality metrics, establish data quality standards, and create data quality reports to track data quality performance over time.
5. Continuous improvement: Data scientists can play a critical role in continuously improving data quality by using their skills to identify areas for improvement and implementing new data quality initiatives. They can use data analytics and machine learning techniques to identify patterns and trends in the data and develop new data quality rules and processes to address emerging data quality issues.

In summary, data science skills can be leveraged to improve data quality in an enterprise by using data profiling and analysis, data cleansing and transformation, data validation, data governance, and continuous improvement techniques. By applying these skills to the data quality improvement process, organizations can improve the accuracy, completeness, and consistency of their data and make better-informed decisions based on reliable and trustworthy data.

Examples of Regulatory compliance and reduced risk of penalties and fines

Regulatory compliance and reduced risk of penalties and fines are important benefits of implementing a data quality process in an enterprise. Here are some examples of how a data quality process can help ensure regulatory compliance and reduce the risk of penalties and fines:

1. GDPR compliance: The General Data Protection Regulation (GDPR) requires organizations to ensure the accuracy, completeness, and relevance of personal data. By implementing a data quality process, organizations can ensure that personal data is accurate, complete, and up-to-date, and reduce the risk of GDPR violations and penalties.
2. HIPAA compliance: The Health Insurance Portability and Accountability Act (HIPAA) requires healthcare organizations to ensure the accuracy and completeness of patient data. By implementing a data quality process, healthcare organizations can ensure that patient data is accurate, complete, and consistent, and reduce the risk of HIPAA violations and penalties.
3. SOX compliance: The Sarbanes-Oxley Act (SOX) requires organizations to maintain accurate and complete financial data. By implementing a data quality process, organizations can ensure that financial data is accurate, complete, and consistent, and reduce the risk of SOX violations and penalties.
4. Banking regulations: Banking regulations, such as Basel III and Dodd-Frank, require banks to maintain accurate and complete data for risk management and regulatory reporting. By implementing a data quality process, banks can ensure that their data is accurate, complete, and consistent, and reduce the risk of regulatory violations and penalties.
5. Data privacy regulations: Data privacy regulations, such as CCPA and LGPD, require organizations to ensure the accuracy and completeness of personal data. By implementing a data quality process, organizations can ensure that personal data is accurate, complete, and consistent, and reduce the risk of privacy violations and penalties.

In summary, implementing a data quality process can help ensure regulatory compliance and reduce the risk of penalties and fines by ensuring the accuracy, completeness, and consistency of data. By meeting regulatory requirements, organizations can avoid the costs and reputational damage associated with non-compliance and build trust with their customers and stakeholders.

Similar and different features across data quality tools

Here’s a crosstab of similar and different features across commonly used data quality tools:

Data Quality Tool Features	Informatica Data Quality	Talend Data Quality	IBM InfoSphere Information Analyzer	Trifacta	OpenRefine
Data profiling	Yes	Yes	Yes	Yes	Yes
Data cleansing	Yes	Yes	Yes	No	Yes
Data validation	Yes	Yes	Yes	Yes	No
Master data management	Yes	Yes	Yes	No	No
Data governance	Yes	Yes	Yes	No	No
Business intelligence	No	No	No	Yes	No
Data quality monitoring	Yes	Yes	No	No	No

Similar features across data quality tools include data profiling, data cleansing, data validation, and master data management. These features are commonly found in most data quality tools and are essential for improving the accuracy, consistency, and completeness of data.

Different features across data quality tools include business intelligence, data governance, and data quality monitoring. These features are more specialized and are designed to address specific data quality needs or challenges.

By understanding the similarities and differences between data quality tools, organizations can select the tools that best fit their specific needs and goals. By leveraging the strengths of different data quality tools, organizations can maximize the benefits of their data quality initiatives and improve their decision-making capabilities.

Outline the detailed list of data quality tools in their respective main category.

Here’s an outline list of some of the commonly used data quality tools categorized by their respective main category:

1. Data profiling tools:
   • Talend Data Profiling
   • Informatica Data Quality
   • IBM InfoSphere Information Analyzer
   • Trifacta
2. Data cleansing tools:
   • Informatica Data Quality
   • IBM InfoSphere Information Server
   • Talend Data Preparation
   • OpenRefine
3. Data validation tools:
   • Talend Data Quality
   • Trifacta
   • Informatica Data Quality
   • IBM InfoSphere QualityStage
4. Master data management tools:
   • Informatica MDM
   • Talend MDM
   • IBM InfoSphere MDM
   • SAP Master Data Governance
5. Data governance tools:
   • Collibra
   • Informatica Axon
   • IBM InfoSphere Governance Catalog
   • Talend Data Catalog
6. Business intelligence tools:
   • Tableau
   • Power BI
   • QlikView
   • SAP BusinessObjects
7. Data quality monitoring tools:
   • Informatica Data Quality
   • Talend Data Quality
   • IBM InfoSphere DataStage
   • HVR

Each of these tools provides specific functionalities and features that are designed to help organizations improve the quality of their data. By selecting the appropriate tools for their needs, organizations can streamline and automate their data quality processes, reduce costs, and increase efficiency.

Conclusion

In conclusion, the data quality improvement process outlined in this document is an essential component of any organization’s data management strategy. By following the steps outlined and utilizing the tools and techniques recommended, organizations can improve the quality of their data, leading to more accurate insights, better decision-making, and ultimately, improved business outcomes. By investing in data quality improvement, organizations can gain a competitive advantage in the marketplace, build customer trust, and reduce the risk of financial losses. We hope this article provides a helpful guide for organizations looking to improve their data quality and achieve greater success in today’s data-driven world.

Resources

1. Your Guide to Data Quality Management: https://www.scnsoft.com/blog/guide-to-data-quality-management
2. How to improve data quality: Define, design, and deliver – Data Ladder
3. Data quality management: What, why, how, and best practices – Data Ladder