In today's data-driven world, companies across industries recognize data as a powerful asset for decision-making. But what constitutes reliable data? How can you assess whether the data your business relies on is mature and dependable enough to instill confidence in your team and drive strategic decisions?
Understanding the 10 characteristics of data quality makes it easier to evaluate and enhance the quality of your data, which will serve as a solid foundation for your organization's growth and success.
What are the 10 characteristics of data quality?
The criteria for good data quality comes down to 10 data characteristics, which are:
- Accuracy
- Accessibility
- Completeness
- Consistency
- Validity (Integrity)
- Uniqueness
- Currency
- Reliability
- Relevancy
- Timeliness
These elements of data quality, in one way or another, build the foundation for an organization to make the best decisions for its business and better communicate and engage with customers.
1. Accuracy
The first piece of criteria for good data quality to consider is accuracy, which verifies an organization's information is correct. The measured value should match the actual, true value, and the data must be error-free. Error-free data should be up-to-date, not redundant, and have no typos. Prioritizing data accuracy means organizations can make more informed decisions, foster stakeholder trust, and achieve better business outcomes.
When developing data management strategies, remember the actual value of data lies in its accuracy and ability to represent the real world accurately. The overarching goal should be to increase data accuracy, especially as datasets grow continually.
2. Accessibility
Accessible data is obtainable at the time it is needed and by those who need it. Having data readily obtainable with traceable changes allows organizations to enhance transparency, accountability, and efficiency. This data quality characteristic is essential for timely decision-making and effective business operations.
Data accessibility is not just available when needed but is also usable. The ability to quickly access data allows businesses to respond promptly to market changes, internal demands, and customer needs. If data is inaccessible, its value diminishes, as delays in obtaining the necessary information can lead to missed opportunities, inefficiencies, and poor decision-making.
3. Completeness
Completeness refers to whether all the necessary data is available. For instance, while a customer's first and last name might be crucial for communication, their middle initial might be optional. Comprehensive data lets businesses engage effectively with customers, make informed decisions, and operate efficiently.
Completeness is one of the critical data quality characteristics because incomplete information can render data unusable. Imagine sending out mail without the recipient's last name. The mail might not reach the intended address, wasting resources, time, and money, ultimately leading to missed opportunities to connect with customers. Incomplete data can impact business operations, decision-making, and customer engagement.
4. Consistency
Consistency is another one of the necessary characteristics of data quality. It means the same information is stored and used across multiple instances to match each other. Consistent data is the percentage of matched values across various records, making accurate and meaningful data analytics essential.
Data consistency prevents organizations from using contradictory information that undermines their operations and decision-making processes. Consistent data supports accurate analytics and effective business operations and fosters trust in the information used across the enterprise.
5. Validity (Integrity)
Validity is one of the data quality dimensions verifying values align with specific domain requirements and formats. It signifies that data attributes are appropriate and accurate for their intended use, which any organization needs for maintaining data integrity and usability.
This fundamental characteristic of data quality makes sure data is fit for its intended purpose. By maintaining high data validity, organizations can enhance the accuracy and reliability of their data for better decision-making and operational efficiency.
6. Uniqueness
Uniqueness verifies that each data point is recorded only once in a dataset, eliminating duplication and overlaps. This data quality characteristic maintains the integrity and reliability of data, supporting accurate analysis and effective decision-making.
Prioritizing uniqueness improves an organization's data accuracy and reliability, leading to more trustworthy analytics. Implementing thorough data cleansing and deduplication processes allows for keeping unique records, further building upon data governance and compliance efforts. As a result, fostering data uniqueness is key to effective data management and organizational success.
7. Currency
Currency directly impacts the relevance and accuracy of the information used for decision-making. Currency refers to how recently the data was collected or updated to reflect the most up-to-date and contemporary reality.
Data currency checks that data remains relevant and accurate over time. Focusing on the recency of data collection and updates allows organizations to have high data quality for more accurate insights and better decision-making. Maintaining current data is considered part of the criteria for good data quality, as it drives organizational success in a rapidly changing environment.
8. Reliability
Reliability encompasses the degree to which data is factual across various sources and systems. Reliable data means the information used for analysis and decision-making falls within the acceptable values for the specific task or question.
Reliable data increases trust, improves operational efficiency, and mitigates risks associated with inaccurate data. Implementing validation rules and maintaining audit trails builds data reliability. Ultimately, reliable data provides a solid foundation of accurate and trustworthy information.
9. Relevancy
Relevance is one of the elements of data quality determining whether collected data serves a meaningful purpose. It pertains to whether the data collected is necessary and helpful in achieving specific business goals and objectives. Without relevancy, data collection efforts can become wasteful and unproductive.
Relevant data collection efforts are purposeful and align with business goals. Data relevancy allows organizations to use resources more efficiently, extract valuable insights, and conduct focused analyses. Implementing practices to define goals, evaluate the necessity of data, and align data with applications creates relevant data.
10. Timeliness
Timeliness refers to how current and up-to-date the information is. In an era where information constantly evolves, timeliness can be considered one of the mandatory data quality dimensions.
Organizations can gain a competitive edge by designing timely data collection, storage, and updates and making informed decisions based on the most recent information available. Data timeliness contributes to organizational success by enacting data quality management systems supporting rapid data processing and frequent updates.
Maintaining data quality
High-quality data is essential for a business's success, and incorporating these characteristics of data quality in your data management plan will save you time, money, and resources in the long term. Developing the criteria for good data quality in your organization helps you avoid relying on invalid or incomplete information.
Experian’s data quality management tools can help you with your data quality platform. Contact us today to get started with cleansing and maintaining your data.
