Data is increasingly becoming the driving force of every organization in the modern world. Organizations want to leverage their data landscape to ensure that data-driven decision-making is a core part of their business operations. For this to happen, organizations need robust data quality management practices to increase trust in their data landscape.

To quote an analogy – if you use low-quality fuel in your vehicle, it can reduce the performance of your car, and in some cases, this can seriously damage the vehicle. The implications of your organization making decisions based on low-quality data can be similarly catastrophic. According to a statistic published by Gartner Research in the year 2021, poor data quality costs organizations an average of $12.9 million.¹

 

In order to mitigate such risks, organizations need to follow data quality management practices. 

This ensures that their business decisions are driven by trustworthy, high-quality data. In this blog, we will understand key data quality dimensions, and how organizations should plan for robust data quality management practices. But, first, let’s start with the definition of data quality.

Data quality is defined as the reliability of data, characterized by the ability of data to serve its intended purpose. Data is considered to be of high quality if it is accurate, complete, unique, valid, fresh, and consistent.

What is Data Quality Management?

Data quality management is a set of practices, tools, and capabilities that ensure the delivery of accurate, complete, and fresh data. A good data quality management system includes a variety of features and capabilities to ensure data quality and trustworthiness of organizational data. These capabilities enable organizations to identify and resolve all data quality issues to ensure the delivery of high-quality, trusted data to end users.

Data profiling: Data profiling is the process of exploring and analyzing data structure, content, data types, relationships between datasets, and data quality issues. For instance, a first-level check of basic quality issues such as missing values, outliers, and data inconsistency can be identified by users with the help of data profiling. Having a good profiling of your data assets is a good first step to gauge the quality of your data. The insights gathered through data profiling help further in the data cleansing process. 

Data cleansing: While data profiling just provides an overview of your data’s health, data cleansing aims to remove data discrepancies. This includes correcting unknown data types, removing duplicate records and substandard data representations. Data cleansing ensures that data follows the defined format and standard. This streamlines data integration initiatives to maintain data integrity. It is crucial to maintain data accuracy and completeness which is essential for data-driven decision making. 

Read more about data cleansing and some best practices here. 

Data standardization: In the modern data world, data sharing across different teams is very important. Data standardization ensures that data follows a common terminology and is consistent across different systems and applications, ensuring that data follows the defined format and standard. This streamlines data integration initiatives to maintain data integrity, improves data consistency and reduces data integration problems.

Data validation: Data validation is one of the critical aspects of data quality management. Through data validation rules, data teams ensure that their datasets meet the required standards and criteria for them to be considered valid. 

For example, an organization dealing with clinical study data (where the participants are all women with age from 30-60 years), would create a data validation rule as follows – Gender: Female, Age: 30-60 years. Data validation rules ensure that any data that does not meet the above two rules (based on the clinical study’s criteria) will be considered invalid.

Data Governance: Data governance plays a crucial role in maintaining high data quality by establishing policies, procedures, and standards that govern data management practices. 

For example, a governance structure might lay out practices to ensure consistency in data definitions, formats, and usage of certain datasets used across the organization, thereby promoting data accuracy and reliability. Effective data governance frameworks also include mechanisms for data stewardship and accountability, ensuring that data quality is monitored, maintained, and improved continuously. 

Automated data quality and observability: In today’s complex data environment, it has become very difficult to detect and troubleshoot data quality issues. For any organization that is serious about its data quality initiatives, data quality automation is very important. Organizations need modern data quality tools that provide data monitoring, troubleshooting, and automated business quality checks to ensure consistent delivery of high-quality data to end users. Good data quality tools enable organizations to proactively monitor and analyze their data pipelines, model performance, and potential biases in real-time and reduce the cost implications of poor data quality. 

Key Data Quality Dimensions

Data quality dimensions provide a good benchmark to assess an organization’s data landscape. Data that is being assessed on multiple dimensions provides a clear picture of organizations’ data quality practices. The overall data quality score that is measured on different dimensions makes it easier for data users to assess whether the data is fit for its intended purpose or not. Overall there are 6 key dimensions of good data quality. Let’s explore each dimension in detail.

Accuracy: Data accuracy refers to the degree to which the said data accurately reflects an event or object that is described. There are two kinds of accuracy, semantic and syntactic.

Semantic accuracy refers to the correctness of the meaning or interpretation of data. Let’s say a database table has a column for product categories. In this table, a product is incorrectly categorized as “Electronics” when it actually belongs to the “Sports” product category. This is an example of semantic inaccuracy.

Syntactic accuracy focuses on the correct data format, structure or pattern. For example, if a customer’s credit card number is supposed to be 16-digits long, the syntactic data quality will check whether each data entry contains 16 digits or not. 

Completeness: Data is considered to be complete when it fulfills certain expectations of comprehensiveness set by an organization. It indicates whether there are enough attributes of data to draw meaningful conclusions from it. In a customer database for an e-commerce platform, the “phone number” field is crucial for customer communication and order processing. Data completeness in this context means that every customer record should include a valid phone number. Null values in the “phone number” field indicate incomplete customer profiles, which could negatively affect order updates, delivery confirmations, and customer support queries.

Consistency: Data consistency ensures that data values obtained from different and independent datasets or different data sources do not contradict each other. With every growing data source, it’s very crucial for organizations to make sure that the data, integrated from different sources, is standardized for data consumption.

Uniqueness: Data uniqueness ensures that data records are distinct and don’t contain any duplicates. For example, in the student database for a university, every student should have a unique ID. This ensures the efficient management of accurate records for enrollment, grades, academic, and medical history.

Validity: Data validity refers to whether data conforms to defined rules, ranges, types, and specific formats or standards. For example, dates should be in specific formats (such as MM/DD/YYYY), the age column of a database should not contain negative values, temperature of a city can’t be 80 degrees Celsius, etc.

Timeliness: Timeliness indicates how fresh the data is. Data timeliness ensures that data is fresh and up-to-date and it is available when needed. For industries like finance, timeliness is very important, as the stock price changes in real-time.

Benefits of Effective Data Quality Management

Data-driven decision-making: With effective data quality management practices, organizations can confidently make business decisions based on trustworthy and high-quality data. Data and business teams can trust organizations’ data landscape, which leads to increased data utilization and promotes data-driven decision-making.

Enhanced productivity: Data quality management ensures that organizations are handling data quality issues in an automated and augmented manner to reduce manual handling of DQ errors. Data quality automation practices reduce the time taken to identify and resolve data quality issues. This means that employees can now spend less time dealing with data quality errors and can now focus on their core data management tasks.

Competitive advantage: Reputation precedes every business. A business with a good reputation gains a higher competitive advantage over others. High-quality data plays an important role in ensuring that a business maintains a good reputation, while low-quality data has been proven to bring about distrust from customers, leading to their dissatisfaction with the business’s products and services.

Financial health improvement: At the start of the blog, we have seen that the implications of poor data quality can be disastrous. Data quality management ensures that organizations don’t have to deal with financial loss due to poor data quality.

Data governance and compliance: Data quality management practices, by ensuring high data quality, enable organizations to meet regulatory and compliance requirements. With effective governance and compliance through data quality management, organizations can avoid substantial penalties and severe damage to their brand value.

Efficient Business Processes: Reliable data ensures smoother and more efficient business processes across various departments. This includes faster reporting, streamlined workflows, and better collaboration between teams.

Conclusion

Ensuring high-quality data through effective data quality management practices is crucial for organizations aiming to make informed decisions and improve operational efficiency. By employing methods like data profiling, cleansing, standardization, validation, and automated quality monitoring, companies can address data inaccuracies and inconsistencies proactively.

Data governance also plays a critical role by establishing clear guidelines and accountability for data management, ensuring data integrity and compliance with regulations. Ultimately, investing in data quality management practices not only enhances data reliability but also supports better business processes, regulatory compliance, and stakeholder trust. Organizations that prioritize data quality management are better positioned to capitalize on their data assets for strategic decision-making and sustained growth.

Organizations that want to implement robust data quality management practices should explore DQLabs, the modern data quality management platform. DQLabs will enable organizations to have a competitive advantage in today’s digital marketplace through Data Quality Management.

 

Source:¹ https://www.gartner.com/smarterwithgartner/how-to-improve-your-data-quality