Data Quality Management: Key Aspects and Features

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The era of smart devices is becoming the era of smart systems. By providing personal services for event tracking, route planning, and optimizing shopping carts, smart devices can then transmit this information to smart systems. Sharing this data allows the entire information apparatus to adapt to individual preferences.

All information technologies are based on digital data. Global “digitalization” leads to gigantic volumes of data. It is impossible to describe the development of IT without understanding the nature of data and the technologies working to process it.

Data quality management helps to ensure accurate and useful results by combining organizational culture, technology, and data.

Data quality management provides a context-sensitive data usability improvement process that is used for analysis and decision making. The goal is to reveal insights into the “health” of this data using different processes and technologies via increasingly complex datasets.

Why is Data Quality Management Important?

Clive Humby, a British mathematician and entrepreneur in the field of data science, once said that “Data is the oil of the 21st century.” 

Today, data is the raw material for the production of a new technological product – knowledge. The process of combining technologies and solutions to improve the usefulness (quality) of data is called “Data Quality Management”. 

Good data quality management forms the basis for all business initiatives. Outdated or unreliable data can lead to errors and incorrect steps. The data quality management program establishes a framework for every department in an organization to enforce data quality rules.

Accurate and up-to-date data gives a clear picture of your company’s day-to-day operations, so you can be sure of the highs and lows that your data is operating on. Data quality management also reduces unnecessary costs. Poor quality can lead to costly mistakes and oversights, such as losing a large number of orders or spending. Management creates a database that allows you to understand your organization and data-supported spending.

You need to manage data quality to meet requirements while minimizing risks. Good data management requires clear procedures and communication, as well as adequate underlying data. For example, a data management committee may determine what is considered “healthy” data. But how do you define this within a database? How do you control and enforce policies? Data quality is the implementation of policy at the database level.

Data quality is an important part of implementing a data governance framework. And good data quality management supports data managers in carrying out their tasks.

Aspects of Data Quality Management

There are several aspects of data quality. This list will continue to grow as the volume and variety of data increases. However, some of the core standards for measurement remain constant:

  1. Reliability measures the degree to which data values ​​are correct and is of paramount importance for being able to draw accurate conclusions.
  2. Completeness means that all data items have tangible values.
  3. Sequencing focuses on single data elements across different instances of data, with values ​​taken from a known area of ​​reference data.
  4. Age matters. The data must be fresh and current, with values ​​that are updated across the board.
  5. Uniqueness demonstrates that each record or element appears once in a data set, which helps to avoid duplication.

Key Features of Data Quality Management

A good data quality program utilizes a system with many features that help improve the reliability of your data.

  • Data cleansing helps fix duplicate records, non-standard data representations, and unknown data types. Cleansing enforces the data standardization rules required to provide information from your datasets. Cleansing also establishes data hierarchies and references data definitions to customize them and suit your unique needs.
  • Data profiling – the act of monitoring and cleaning data – is used to test data against standard statistical measures, identify relationships, and test data against comparable descriptions. Data profiling steps identify trends that will help you detect, understand, and potentially identify inconsistencies in your data.
  • Validating business rules and building a business vocabulary helps you react to poor data quality before it harms your organization. This entails creating descriptions and requirements for translations of business terms between systems. The data can also be checked against standard statistical measures or custom rules.

In addition to these key features, having a centralized view of enterprise activity through a data management console is a sure way to simplify the process.

Data Quality Management for Big Data

Big data has and will continue to have a disruptive impact on businesses. Consider the massive amounts of streaming data from connected devices to the Internet of Things. Or the numerous shipping checkpoints that litter business servers and generally need to be tidied up for analysis. With all this big data comes challenges in data quality management. These challenges can be described in three points: repurposing, validation, and updating.


There is an unbridled repetition of the same datasets in different contexts. This repetition negatively affects the fact that the same data has different values ​​in different settings. This calls into question the reliability and consistency of the data. Good data quality is needed to parse these large structured and unstructured datasets.


It can be difficult to implement validation controls if externally sourced datasets are used. Fixing errors will cause the data to be inconsistent with the source code, but maintaining consistency may mean some trade-offs in terms of quality. This problem requires data quality management functions that can provide a solution to the problem.


Refreshing data extends the life of historical information that may have been previously kept in storage but also increases the need for verification and management. New data can be extracted from old data, but this data must be properly integrated into the new datasets.

In Conclusion

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