Data quality is like the weather: many people and organizations talk about it but few are willing to address its challenges. Data governance can play an active role in the improvement of an organization’s data quality and data accuracy. Data governance initiatives are essential for ensuring reliable data management and addressing potential risks. A strong data governance program can support an organization’s efforts to develop a permanent approach in the improvement of an organization’s data quality.
Understanding Data Quality and Data Governance
Understanding the intricacies of data quality and data governance is paramount for any organization aiming to leverage its data assets effectively. These two concepts, while distinct, are deeply interconnected and essential for ensuring that data serves its intended purpose reliably and efficiently.
What is Data Quality?
Data quality refers to the degree to which data is accurate, complete, consistent, and reliable for its intended use. It is a critical aspect of data management, as poor data quality can lead to incorrect decisions, inefficiencies, and financial losses. Data quality is often measured across six dimensions: accuracy, completeness, consistency, timeliness, validity, and uniqueness. Ensuring high data quality is essential for organizations to make informed decisions, improve operational efficiency, and maintain customer trust.
Accuracy ensures that data correctly represents the real-world entities it is supposed to model. Completeness means that all necessary data is present. Consistency ensures that data is the same across different datasets. Timeliness refers to data being up-to-date. Validity ensures that data conforms to the required formats and standards. Uniqueness means that each data record is distinct and not duplicated.
Poor data quality can have severe repercussions, including flawed business strategies, operational inefficiencies, and loss of customer trust. Therefore, organizations must prioritize data quality to harness the full potential of their data assets.
The Importance of Data Governance
Data governance is the process of managing data availability, accessibility, integrity, consumption, and security by using internal data standards and rules. It is a broad concept that includes various components, such as compliance, privacy, and security. Effective data governance is crucial for ensuring that data is managed, safeguarded, and used appropriately. It provides a comprehensive framework for improving data quality, reducing data-related risks, and increasing trust in an organization’s data assets.
A robust data governance strategy involves setting clear policies and procedures for data management, defining roles and responsibilities, and implementing the necessary tools and technologies. This approach not only helps in improving data quality but also ensures regulatory compliance, enhances data security, and fosters a culture of data-driven decision-making.
Data Governance Framework
A data governance framework is a set of policies, processes, and technological tools that outline the mission statement, goals, and how success will be measured. It defines the roles and responsibilities of data stewards, data owners, and other stakeholders involved in data management. A well-designed data governance framework ensures that data is accurate, complete, and consistent, and that it is used in a way that supports business objectives.
Data stewards play a crucial role in this framework by overseeing data quality and ensuring adherence to data governance policies. They work closely with data owners and other stakeholders to promptly maintain data integrity and address any data quality issues. By establishing clear data quality rules and conducting regular audits, organizations can ensure their data remains a reliable asset for decision-making and corporate innovation.
Challenges to Organizational Data Quality Issues
Poor data quality abounds; every organization can recite many instances where a data quality problem caused extra work, lost revenue, higher costs, production problems, etc.… However, many companies struggle to address and resolve their data quality issues from an enterprise data management perspective. “If you talk about the data, you’re talking about the integrity of the business,” says Philip Say, an SAP director of solution marketing for ERP and finance applications. “Companies are grappling with understanding what they have really created — what’s really running their business.”
Awareness of bad data is on the rise, and the practices needed to address data integrity issues across the enterprise are now available through the development of strong enterprise data governance programs. Using the best practices of data governance can enable an organization to face their data challenges and ensure data quality, improving the quality and usability of their data permanently, since many data governance processes are focused on improving data and metadata quality in source systems and in analytical solutions.
A data steward plays a crucial role in maintaining data accuracy and quality. They manage the oversight of data, ensuring its integrity through regular reviews and corrections, which is pivotal for effective data governance and overall data management strategies.
Historically, companies have allowed IT to shoulder the burden of correcting data quality problems, despite the fact that many data quality issues arise in the business and not from information technology issues. Many companies “solve” the data quality problem by having large numbers of staff extract data from various sources, parse it and re-key it into spreadsheets so management could have some analytical data. This process does not resolve any data quality problems, and may create additional errors made in the parsing and re-keying of the source data.
Regulations, Data Quality, and Data Governance
As compliance regulations, such as the Sarbanes-Oxley Act, HIPPA, GDPR, etc., have emerged, the ultimate responsibility for accurate data has shifted from IT to business leaders. The development of an enterprise data governance program provides the organization with the opportunity to establish and implement processes for fixing the data quality problems of the past and avoiding new data quality errors.
When organizations examine data management and data governance issues, they discover two major problem areas: responsibility and management. Establishing a robust data governance policy is crucial for defining responsibilities and standards for data accuracy, accessibility, and security. Data governance professionals and the business data stewards form the foundation of the data governance program. Together, they are responsible for the data instances and the metadata for that data – the two parts of data quality management. Therefore, it is important that any data governance program include the design and implementation of a data quality improvement practice, one that involves business data stewards from each subject area.
Ensuring good data quality is an ongoing process that requires continuous quality assurance practices and effective data governance frameworks to maintain high data quality, which in turn influences the overall effectiveness of a data governance program.
Data Quality Program and Data Governance Requires Organizational Change
The change in responsibility for data quality is also a change to the organizational culture, driven from the executive layer. Executives and business managers must understand the scope of the data quality problem and support the development processes that combine data governance with data quality activities. Once executives are committed to this approach, the entire organization must be made aware of the effort, the focus on improved data quality and its alignment with data governance, and each staff member’s responsibility for data quality improvement. Bad or missing data elements create chaos and poor results, and possibly economic disaster. Having a strong data stewardship program with business data stewards who have been trained in the concepts and practices of data governance and data quality improvement will help to instill a “data quality first” culture in all business units.
If an organization wishes to improve its data quality and maintain that improvement, it must start with the foundation of a robust data governance and data stewardship program. Aligning all data quality improvement and management efforts with data governance will ensure continued success and accurate, actionable data and information for any organization.
Dimensions and Strategies for Ensuring High Data Quality
Ensuring high data quality requires a comprehensive approach that addresses the six dimensions of data quality. Here are some strategies for improving data quality:
- Accuracy: Implement data validation rules to ensure that data is accurate and consistent. Use data profiling tools to identify and correct errors.
- Completeness: Establish data quality standards and workflows to ensure that all required data fields are complete. Use data integration tools to combine data from multiple sources.
- Consistency: Develop data governance policies and procedures to ensure that data is consistent across all systems and platforms. Use data standardization tools to ensure that data is formatted consistently.
- Timeliness: Implement data refresh schedules to ensure that data is up-to-date and relevant. Use data monitoring tools to detect and correct data quality issues in real-time.
- Validity: Establish data quality metrics and KPIs to measure data quality. Use data validation tools to ensure that data meets defined standards.
- Uniqueness: Implement data deduplication tools to eliminate redundant data. Use data matching tools to identify and merge duplicate records.
By implementing these strategies, organizations can improve data quality, reduce data-related risks, and increase trust in their data assets. This comprehensive approach ensures that data is recorded and updated promptly, maximizing its relevance and utility across business processes.
Strengthening the Foundations: Aligning Data Governance and Quality
To ensure sustainable success, organizations must prioritize a data governance framework that incorporates data quality dimensions into daily operations. This involves defining clear data quality rules, such as metrics for accuracy, completeness, and consistency, and conducting regular data quality audits to identify and address discrepancies. Leveraging data quality tools like automated checks and data profiling further enhances the ability to manage and monitor high-quality data effectively.
A well-implemented data governance strategy includes aligning efforts across data governance policies, master data management, and the active participation of data owners to promptly address data errors. Integrating these measures not only improves data quality but also bolsters regulatory compliance, strengthens data security, and fosters trust in an organization’s data assets. Ultimately, this alignment leads to operational efficiency and ensures data remains a reliable asset for decision-making and innovation.
