Data quality is a crucial aspect of any data-driven organization. It refers to the ability of a given set of data to serve its intended purpose, which means that high-quality data is essential for making informed decisions, achieving business goals, and ensuring overall efficiency. In today’s digital era, data is often referred to as the ‘new oil,’ highlighting its importance and value. This article will provide an in-depth understanding of data quality, its main characteristics, and their real-life examples and benefits.
What is Data Quality?
Data quality refers to how well the data describes the objects, events, or ideas it represents. Essentially, how well it meets the expectations of users who will consume it in whatever job function they’re in. High-quality data is accurate, complete, reliable, relevant, timely, unique, and valid. Each of these characteristics plays a critical role in ensuring the overall quality of data.
Accuracy
Accuracy refers to the closeness of data to the true values, checked against external data sources and visual data governance. Accurate data provides a reliable foundation for decision-making, as it ensures that the information presented is correct and reflects real-world situations.
Examples:
- Cross-checking supplier information such as credit ratings against Dun & Bradstreet’s database to uphold supply chain data accuracy.
- In the asset maintenance space, Engineering and Maintenance teams review equipment criticality data to ensure accuracy.
Completeness
Completeness refers to the extent to which all required data is available. Ensuring that data is comprehensive and contains all necessary information is crucial for its usability and effectiveness.
Examples:
- Ensuring that employee data contains full name, Social Security number, and bank account number to facilitate prompt disbursement of salaries.
- In asset management, making sure that model number and serial number are filled in for each of your equipment to enable tracking and maintenance.
Consistency
Consistency refers to how uniform the data appears within data sets, across different data sets, or with other data sources. Ensuring data consistency helps avoid confusion, as users can trust that the data presented to them is accurate and reliable.
Examples:
- Handling ‘non-applicable’ entries where people have different ways of entering them, e.g., “N/A”, “Not Applicable”, “NA”, to avoid confusion.
- Ensuring that the record count is the same between target and source systems to maintain consistency between different data systems.
Timeliness
Timeliness refers to how up-to-date the data is according to your business needs. Current data is more relevant to the task at hand and less likely to lead to inaccurate conclusions.
Examples:
- For month-end closing, ensure that all Finance-related data is made available and updated for accurate financial reporting.
- In the supply chain space, make sure that certificates and licenses provided by suppliers are current to avoid third-party risks.
Validity
The validity of data refers to the degree to which it conforms to the defined business rules of the domain (reference table, range, etc.). Ensuring that data is valid allows organizations to automate processes and streamline operations, increasing efficiency and reducing the need for manual intervention.
Examples:
- In finance and project data, ensuring that only one company code is assigned to one cost center, as per a generally accepted cost center accounting rule.
- For asset management, making sure that every maintenance plan created has a task list to serve its original purpose.
Uniqueness
Uniqueness refers to the presence of a single version of the truth for data, accessible across the enterprise landscape. Ensuring data uniqueness eliminates duplicate records and redundancies, which can save time and resources when searching for and managing data.
Examples:
- Identifying duplicate customer info to avoid contacting the same customer multiple times and merging records to form a unique record.
- Maintaining a golden record for spares within your inventory management system to prevent uncontrolled accumulation of spares and increased inventory and holding costs.
Integrity
Integrity refers to the consistency in the relationship between entities and attributes, including parent-child relationships and orphan records. Ensuring data integrity helps maintain accurate and reliable data relationships.
Examples:
- Within a functional location hierarchy, making sure that spares are always linked to their parent functional location or equipment, and the attributes reflect the same information.
- Ensuring that no orphan items are left behind when deleting a functional location from the system.
Conformity
Conformity refers to the extent to which attributes are defined based on standards and compliance requirements. Ensuring data conformity helps organizations adhere to industry standards and maintain compliance with regulations.
Examples:
- Organizing and categorizing asset data according to ISO14224 standard to ensure standardization and better identification of assets.
- Implementing data encryption to secure sensitive customer data and comply with GDPR.
Data Quality Management Solutions
SimpleMDG, an SAP BTP master data governance solution, to automate data quality procedures and rules. SimpleMDG offers a variety of capabilities including:
- Supporting various types of business rules and definitions.
- Harmonizing, de-duplicating, and consolidating data from multiple systems to obtain a ‘Golden Record’ view.
- Automating large-scale data cleansing and workflow-based remediation.
- Standardizing master data and data quality parameters based on industry/corporate standards.
- Ensuring compliance with validation checks that account for compliance rules and external sources.
- Providing dashboards and analytics tools to monitor data health and cleansing status.
- Offering pre-defined integration adapters with SAP and other systems.
By implementing SimpleMDG, organizations can streamline their data quality management, gain greater confidence in their data, and ensure high-quality data is always accessible.
Conclusion
Data quality is the foundation of effective decision-making and improved business outcomes. By focusing on the seven dimensions of data quality, organizations can ensure that their data is accurate, complete, reliable, relevant, timely, unique, and valid for their intended use. Understanding and applying these dimensions in real-life scenarios can help organizations maintain high-quality data and drive their business growth.
