1. Data Science

What is data quality management?

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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 enough 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.


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 Modern Data Quality Platform to help you keep your organization competitive in today’s digital marketplace through Data Quality Management.


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