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.
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Consistency ensures a level of same-ness regardless of where and how you’re accessing the information.
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Completeness ensures all the necessary fields and records are captured and that customer profiles are unified.
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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.
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Currency/Timeliness ensures that data is up-to-date and reflects the most recent information available.
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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.
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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.
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Accuracy: Customer total lifetime value must be correct to effectively engage with them based on their loyalty.
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Timeliness: Recent order information must be available in real time to provide relevant customer support.
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Completeness: All orders, including authenticated logins or guest orders should be accessible to the agent.
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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.
The Role of Data Stewardship
Data stewardship is the practice of managing and overseeing the organization’s data assets to ensure they’re accurate, consistent, and secure.
The Data Quality Management Framework
To maintain these standards, NTO utilizes a process framework to manage data throughout its lifecycle.
Following this framework ensures that data isn’t just collected, but actively refined:
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Data profiling: Analyze data and perform a statistical analysis of critical data elements across data sources.
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Data cleaning and standardization: Cleanse and purge unnecessary data and fields and create standards to consistently format data.
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Data enrichment: Enrich your data with additional data points for a more complete picture, building richer profiles.
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Profile unification: Identify and manage duplicate or related records that may span multiple sources. Create contextual profiles through matching and reconciliation rules.
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Data monitoring: Monitor data health through well-established governance processes and controls (data stewardship).
Continuous Monitoring
After the framework is applied, data quality monitoring creates a safety net. This continuous 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:
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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.
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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 the framework required to maintain them, it’s time for them to apply essential data management concepts to improve data reliability, simplify operations, and support strategic decision-making.