Data Quality Management: Definition, Importance and Metrics - Chuyên Trang Chia Sẻ Kiến Thức Thời Trang Mới Nhất

Data is the driving force of every organization in the modern world. As organizations continue to collect more and more data, the need to manage the quality of the data becomes more prominent each day. Data Quality Management can be defined as a set of practices undertaken by a data manager or a data organization to maintain high-quality information. This set of practices are undertaken throughout the process of handling data; from acquiring it, to implementation, distribution, and analysis.

This article outlines what DQM entails, its importance, and the metrics used to assess data quality measures.

Why you need Data Quality Management for your business

The proliferation of data in the digital age has presented a real challenge – data crisis. The data crisis entails low-quality data in its volumes that makes it hard for the businesses to make sense out of it, and in some instances, unusable. DQM has thereby come forth and has become an important process used to make sense out of data. It aims at helping organizations point out errors in their data that need to be resolved. It also aims at assessing if the data in their systems is accurate to serve the intended purpose.

Let us outline four reasons why you need Data Quality Management;

Better functioning business

All the basic operations of a business are managed quickly and efficiently when the data has been managed properly. High-quality data enhances decision-making at all levels of operations and management.

Efficient use of resources

Low-quality data in an organization means resources including finances are used inefficiently. When businesses maintain data quality through DQM practices saves them from wastage of resources leading to bigger and better results.

Competitive advantage

Reputation precedes every business. A business with a good reputation gains a higher competitive advantage over others. High-quality data ensures that a business maintains a high reputation. Low-quality data has been proven to bring about distrust from customers, leading to their dissatisfaction with a business’s products and services.

Good business leads

Creating a marketing campaign from erroneous data where the targeted customers do not exist, makes no sense. When the leads are from poor quality data, then there is no point in targeting them with campaigns. Accurate customer data brings about better conversion from a better reach.

What are the key features of Data Quality Management?

A good DQM makes use of a system that has various features that will help in improving the trustworthiness of organizational data. Let us outline the various features of a good DQM;

Data cleansing corrects unknown data types, duplicate records, as well as substandard data representations. Data cleansing ensures that data standardization rules that are needed to enable analysis and insights from your data sets are followed. The data cleansing process also establishes hierarchies and makes data customizable to fit an organization’s unique data requirements.

Data profiling is the process of monitoring and cleansing data. Data profiling is used to;

Validate available data against the standard statistical measures,
Create data relationships
Verify the available data against matching descriptions

The data profiling process establishes trends that help in discovering, understanding and exposing inconsistencies in the data, for any corrections and adjustments.

What are the metrics that measure Data Quality?

Data quality metrics are very important in assessing the efforts made to increase the quality of your data. Data quality metrics must be top-notch and must be clearly defined. In the data quality metrics, be sure to look out for; accuracy, consistency, completeness, integrity, and timeliness. Let us discuss different categories of data quality metrics and what they hold in;

Accuracy

Data accuracy refers to the degree to which the said data accurately reflects an event or object that is described.

Completeness

Data is considered to be complete when it fulfills certain expectations of comprehensiveness in an organization. Data completeness indicates if there is enough of it that can draw meaningful conclusions.

Consistency

Data consistency simply specifies that two data values retrieved from multiple and separate data sets should in no way conflict with each other. However, data consistency does not necessarily imply that the data is correct.

Integrity

Also referred to as data validation, data integrity refers to structurally testing data to ensure compliance with an organization’s data procedures. Such data shows that it has no unintended errors and that it corresponds to its appropriate data types.

Timeliness

When your data isn’t ready when users need it, it fails to fulfill the data quality dimension of timeliness.

Some examples of data metrics that help an organization to measure data quality efforts include;

The ratio of data to errors

This data metric allows tracking of the number of known errors within a data set corresponding to the actual size of the data set.

Number of empty values

This metric counts the number of times there is an empty field within a data set. Empty values usually indicate missing information or information recorded in the wrong field.

Data time-to-value

This metric evaluates how long it takes to gain meaningful insights from a data set.

Data transformation error rate

This metric will track how often a data transformation operation will fail.

Data storage costs

If an organization stores data without using it, this could be an indication that the data is of low quality. Conversely, if the organization’s data storage costs decline while the data operations stay the same or continue to grow, the quality of the data is most likely to improve.

Summary

While it may look like it is a real pain to maintain high-quality data, some organizations also feel like Data Quality Management is a huge hassle. This means if your organization is the one that takes the lead in making its data sound, it will automatically gain a competitive advantage in its industry.

This article details the information needed to maintain high-quality data. Be sure to look out for DQLabs.ai – a leading data quality management platform to help you in keeping your organization competitive in today’s digital marketplace through Data Quality Management.