Measuring Data Quality: A Critical Component of Enterprise Data
Data quality is essential for businesses to make accurate and reliable decisions. Poor data quality can lead to increased costs, unreliable analysis, compliance risks, and loss of brand value.
Data quality is a critical component of enterprise data management that can significantly impact an organization's success. High-quality data can power accurate analysis, leading to trusted business decisions. Conversely, poor quality data can result in high costs, negatively affecting an organization at multiple levels, including higher processing costs, unreliable analysis, poor governance, compliance risks, and loss of brand value.
Higher processing cost: According to the rule of ten, flawed data can result in a tenfold increase in processing costs per unit of work.
Unreliable analysis: When the analysis is flawed and unreliable, it becomes difficult to manage the bottom line effectively.
Poor governance and compliance risk: Compliance requirements are now mandatory, making it increasingly difficult for businesses to survive without adhering to them.
Loss of brand value: Consistently making faulty operations and decisions can rapidly diminish an organization's brand value.
The Art of Evaluating Data Quality
According to Gartner, poor data quality can result in additional costs of $15M annually. The immediate concern for organizations is measuring data quality and finding ways to improve it. However, data quality can be easy to recognize but challenging to determine. For example, the entry of "Mrs. Jane Smith" twice in a database could be due to two people with the same name, the same person's name entered twice by mistake, or the database not being validated after migration or integration.
To measure data quality correctly, you must consider multiple attributes for the correct context and measurement approach. For example, patient data in healthcare must be complete, accurate, and available when required. In contrast, customer data in marketing campaigns need to be unique, accurate, and consistent across all engagement channels. Data quality dimensions capture the attributes specific to your context.
Understanding the Meaning of Data Quality Dimensions
Data quality dimensions are measurement attributes of data that you can individually assess, interpret, and improve. The aggregated scores of multiple dimensions represent data quality in your specific context and indicate the fitness of data for use. On average, 47% of recently created data records have at least one critical, work-impacting error. High-quality data is the exception, with only 3% of DQ scores rated acceptable (with >97% acceptability score), indicating that only 3% of companies' data meets basic quality standards.
The six key data quality dimensions are completeness, accuracy, consistency, timeliness, uniqueness, and validity. Let's explore each of these dimensions in more detail:
Completeness: This dimension measures the minimum information essential for a productive engagement. For example, a company's customer database is missing phone numbers for a few customers, but all other information is present. The data is still considered complete since the missing phone numbers do not hinder the company's ability to contact customers through other means. Completeness measures if the data is sufficient to deliver meaningful inferences and decisions.
Accuracy: This dimension measures the level of data that represents the real-world scenario and confirms it with a verifiable source. For example, a hospital's patient records are cross-checked with the patient's medical history to ensure accurate information. This verification process ensures that the medical decisions based on this data are correct. Measuring data accuracy requires verification with authentic references. High data accuracy can power factually accurate reporting and trusted business outcomes.
Consistency: This dimension represents if the same information stored and used at multiple instances matches. It is expressed as the percent of matched values across various records. For example, a retail store's sales data for a particular product is compared across different locations to ensure it matches. Data consistency ensures that analytics correctly capture and leverage the value of data.
Timeliness: This dimension measures the relevance and usefulness of data concerning the business objective. For example, a bank's customer transaction data is analyzed near-real time to identify potential fraud. This timely analysis ensures that any fraudulent activity is identified and addressed promptly. The timeliness of data is vital for decision-making and helps identify deviations from established trends or patterns.
Uniqueness: This dimension measures the uniqueness of the entity represented in the data. For example, an online retailer's customer database has multiple entries for the same customer with different email addresses and phone numbers. Each entity should have a unique representation in the data. Duplicate entries can lead to incorrect analysis and inaccurate reporting.
Validity: This dimension measures whether the data follows the defined format or structure, adhering to defined business rules. For example, a sales number for a specific customer exceeds the total revenue for your company. This data cannot be valid. Validity ensures that the data conforms to the organization's expectations and standards.
To measure data quality accurately, you need to understand the context-specific of your organization and define acceptable scores to build more trust in data. Data quality dimensions serve as a guide for selecting the most suitable dataset. Analysts can choose the dataset with higher accuracy when presented with two datasets of 79% accuracy and 92% accuracy to ensure their analysis.
Best Practices for Maintaining Data Quality and Integrity
Forbes recently published a report stating that 84% of CEOs are concerned about the integrity of the data they rely on for their decision-making. This statistic highlights the significant value associated with data integrity.
Data integrity and data quality are two different concepts that are often confused. While data quality focuses on ensuring the accuracy and completeness of data, data integrity takes it a step further by enriching the reliable data with relationships and context to improve its effectiveness.
Data quality is the foundation for trusted business decisions, while data integrity adds more value by delivering better business decisions. To maintain data quality, enterprises must establish and adhere to enterprise-wide standards and utilize machine learning-enabled tools for scalable, real-time assessment. Data quality standards should document agreements on the representation, format, and definition of shared data and the objectives and scope of implementing data quality.
Implementing well-defined data quality standards also enables compliance with evolving data regulations. Data quality checks involve determining metrics that address both quality and integrity. Standard data quality checks include identifying duplicates or overlaps for uniqueness, checking for mandatory fields, null values, and missing values to ensure data completeness, applying formatting checks for consistency, using business rules with a range of values or default values and validity, and checking the recency or freshness of data by validating row, column, conformity, and value checks for integrity.
Priorities of Data Consumers Beyond Accuracy
The perspective on data quality differs between data producers/managers and data consumers. The former prioritize accuracy and strive to align data as closely as possible with real-world entities through cleaning, fixing, and management efforts.
However, data consumers seek additional dimensions of quality when searching for data. In particular, they focus on the data supply chain and prioritize accessibility, wanting to know where and how to retrieve data. Timeliness is also important, as data's value lies in its use, and access to data is meaningless if it's unavailable when needed. Timely data availability is crucial for reducing errors, streamlining processes, driving business innovation, and maintaining a competitive edge. Ultimately, data consumers require access to the most recent data to power their projects.
Prioritizing Relevance and Collaboration in Data Quality Strategies
After data accessibility and timeliness are addressed, data consumers prioritize relevance when searching for data. They want to find data that aligns with their specific project requirements, avoiding wasted efforts on irrelevant data. Accuracy becomes essential only after relevance is established, ensuring the selected data will deliver the desired results.
To go beyond accuracy, data producers and consumers must collaborate to develop a comprehensive data quality strategy. Data consumers must identify their priorities, and producers must deliver the most critical data. They should also consider the factors that affect effective data shopping, such as data understanding, intelligence, metadata, and lineage.
Data quality can be successfully improved and continuously maintained by addressing these factors.
Wrapping it up
In conclusion, data quality is a crucial aspect of enterprise data management that can significantly impact an organization's success. Poor-quality data can result in high costs, negatively affecting an organization at multiple levels, including higher processing costs, unreliable analysis, poor governance, compliance risks, and loss of brand value. The key data quality dimensions are completeness, accuracy, consistency, timeliness, uniqueness, and validity. To measure data quality accurately, you need to understand the context-specific of your organization and define acceptable scores to build more trust in data. Maintaining data quality requires well-defined data quality standards that should document agreements on the representation, format, and definition of shared data and the objectives and scope of implementing data quality. By following these best practices, organizations can ensure data quality and integrity, enabling trusted and effective decision-making.