Ensure Data Quality
Learning Objectives
After completing this unit, you’ll be able to:
- Describe the multiple dimensions of data quality.
- Define fit-for-purpose data.
- Explain the importance of data compliance and security.
Data Quality Concepts Overview
Data quality is the cornerstone of reliable data management. There are six key dimensions of data quality.
- Consistency ensures a level of same-ness regardless of where and how you’re accessing the information.
- Completeness ensures all the necessary fields and records are captured and that customer profiles are unified.
- Correctness refers to the accuracy of the data. While it might not be possible to know if something is correct, rules or algorithms can detect when they might be incorrect.
- Currency/Timeliness ensures that data is up-to-date and reflects the most recent information available.
- Context refers to the relevance of data to specific use cases. Data should be presented in a way that makes sense for the user’s role and responsibilities.
- Compliance involves adhering to data retention, security, and usage policies.
Using these dimensions and their definitions, the CDO of NTO rolls out a data quality awareness training to ensure the team understands how to gather data quality requirements and enforce them.
Fit-for-Purpose Data
Data quality refers to the degree to which data meets the expectations of its users, based on its intended use. In other words, data must be “fit for business purpose” to be considered reliable for business use. Making sure the data meets your quality standards is essential because the effectiveness of business processes, decision-making, and customer interactions depend on the reliability of the data used.
In the context of NTO’s customer service operations, here are some examples of fit-for-purpose data.
- Accuracy: Customer total lifetime value must be correct to effectively engage with them based on their loyalty.
- Timeliness: Recent order information must be available in real time to provide relevant customer support.
- Completeness: All orders, including authenticated logins or guest orders should be accessible to the agent.
- Relevance: Only the necessary data should be presented to service agents to avoid information overload.
Without fit-for-purpose data, business decisions can be made based on flawed or incomplete information, leading to inefficiencies, poor customer experiences, and potential compliance risks.
Ensuring Data Quality and Compliance
Maintaining data quality and ensuring compliance are ongoing processes that require continuous monitoring and active stewardship. These are key components of NTO’s overall data management strategy.
Data stewardship is the practice of managing and overseeing the organization’s data assets to ensure they’re accurate, consistent, and secure.
Data profiling entails analyzing the content, structure, and relationships within datasets. It helps identify inconsistencies, errors, and potential areas for improvement. Conducting data profiling regularly allows NTO to detect issues early and maintain high data quality over time.
Data quality monitoring involves regularly assessing data to ensure it meets predefined quality standards. This can involve automated checks for accuracy, completeness, and consistency, and manual reviews for more nuanced aspects of data quality. For example:
- Automated checks: Regularly scheduled analysis and deviation detection can identify anomalies, such as fields that are no longer populated, indicating a possible integration failure or business process change.
- Manual reviews: Data stewards or analysts can periodically review critical datasets or metadata to ensure they align with business expectations.
Now that NTO understands their data management needs and what needs to change based on their business goals, it’s time for them to apply essential data management concepts to improve data reliability, simplify operations, and support strategic decision-making.