Impact of Having Poor Data Quality & Importance of Data Quality Management System (DQM)

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By: Manish Shewaramani

Impact of Having Poor Data Quality & Importance of Data Quality Management System (DQM)

For every enterprise, data plays an important role and is a driving force for business growth. With time as the business grows, the data keeps on growing. It becomes important for proper handling and managing of all this data.

Having bad data will cause a severe negative impact on business operation; especially in the case of having bad customer data it will affect lead generation and cause harm to the brand.

The organization has to manage different types of data related to the systems, products, processes, operations, etc. This article briefs the importance of having good customer data and why an organization should focus on a data quality management system (DQM).

What is Data Quality Management (DQM)?

Well, before understanding data quality management you should be aware of data quality. Data quality is a term that refers to how the data matches the user requirement and how it represents or describes the data in the real world.

Data is the driving force for all types of businesses in today’s modern world. Data quality management is just a set of practices an organization should follow to maintain its data quality standards. Organizations use the data quality management system for the importing, handling, implementing, analysis, and distribution of accurate and quality data.

Why is Data Quality Management Important?

The Digital era has forced businesses around the world to move digital to survive the heavy competition and to match up with the demands of modern customers. In the virtual world, having good data becomes inevitable owing to that organizations have now started focusing on the data management system for succeeding businesses’ core objectives.

Data quality plays a major role in marketing, online sales, supply chain management, and also in financial reporting. Apart from, quality data encompass potential consumers with the brands.

Impact of Having Bad Customer Data

It doesn’t matter whether you are running an enterprise-level or a small-scale business, having a small data quality problem will cause you a severe headache on business operation & its growth.

Here are the negative impacts businesses or organizations can face due to the poor data quality standards.

1. Pulls Your Brand Loyalty Down

Almost every business would be aware of developing customer loyalty over the brand and also how much time & effort it will take to build customer loyalty. In general, customer loyalty represents the amount of trust a customer places on a brand. This trust usually gets developed when the brand manages to provide consistent and reliable services.

Even a small amount of data error will cause a severe negative impact on customer trust. Once a brand starts to receive a poor impression from its potential customers, it will affect brand loyalty.

2. Poor Data Quality Will Cost You A Lot

It may sound unfamiliar to many, but it is true. Having an improper or poor quality of data will cost a lot for businesses. In addition, handling customers’ queries becomes a nightmare for customer service staff.

Poor data quality will lead to an increase in product return along with a steep decrease in customer satisfaction levels. Besides, poor data may lead to privacy breaches which may increase your legal cost.

An increase in product return and customer bounce rates will cost significant revenue loss for any business. For small-scale and medium-level businesses it will be a heavy blow and it will be nearly impossible to witness profit margin in the near future.

3. Poor Data Affects Customer Bond With Brand

Well, you might witness how the poor data quality affects customer loyalty but customer bond or customer goodwill with the brand is an entirely new chapter.

In general, customer bonds or goodwill with the brand will create awareness about the launch of the new products. If that connection gets broken it will cause a severe blow on lead generation and brand reputation.

Even people who are not directly linked with your brand products might affect your brand reputation and lead due to the poor data quality through the ‘word of mouth’ rule. Small and mid-level companies would face a significant struggle to bounce back and survive the blow of poor data quality.

4. Poor Data Quality Affects Staff’s Morale

Apart from affecting brand reputation and customer loyalty, poor data quality will cause frustration among staff. Having a poor data quality management system will cause employees to spend a lot of time rectifying and cleaning up bad data and it will be a tedious job to do.

Data silos pile up due to the poor data quality management system would cause more trouble for the staff to navigate and directly impact the brand’s productivity along with the staff’s overall performance.

A recent survey points out; employees who are experiencing low morale at their workplace are more likely to perform well.

5. Poor Data Leads To Incomplete & Duplicate Data

Complete data can also be termed as missing data or missing values, this can cause serious trouble while being analyzed by staff, and also for consumers it will create confusion on understanding the product. Just like that, duplicated data will cause a similar impact on your brand performance.

Besides, incomplete data and duplicate data will disintegrate your potential buyers from your brand and will lead you to a situation where it is impossible to predict what your customers need. Eventually, this will end up with revenue loss and a decrease in the customer base.

Challenges On Maintaining Data Quality Standards

Though you can point out numerous challenges when it comes to maintaining your brand data quality standards, human errors are the most significant reasons behind the cause of poor data quality.

Study report suggests more than 60% of the poor data or siloes piles are due to human errors. Bringing the right set of tools to your brand data quality management system will automate things and eliminate the risk of a high human error ratio.

Also, with the right data quality management system, staffs no need to worry about the data pile and concentrate on productivity by adapting to new data models.

Features of Data Quality Management

Data quality management (DQM) uses a system that has different features essential for improving data quality. These different key features of the data quality management include:

Data cleaning

The data is imported from various sites and in different formats. The DQM allows in cleaning all types of data and removing duplicate and substandard data. It also establishes hierarchies for making the data customizable for fitting the different requirements.

Data profiling

DQM must be able to validate the data against the available standard statistical measures and create data relationships. It helps in developing a trend for understanding, discovering, and exposing the inconsistencies in the data. This helps in making the required adjustment and corrections.

Data accuracy and reliability

DQM ensures that the data is accurate and reflects the real-time situation. Accuracy is an important characteristic of quality data. The accurate data is reliable and helps the organization in building the brand impression.

The Necessity Of Data Quality Management for your business

The data is important for the organization for various purposes. Quality, accuracy, and real-time data are very important for any organization to make its mark in the business world. The quality of data can adversely affect monetary loss and status.

The different reasons for an organization to implement the data quality management process for the business include:

Enhanced business functioning

DQM allows for efficient management of all sorts of organizational data that is essential for the various basic operations within the enterprise. Maintaining high-quality data improves decision-making at different levels of operations, processes, and management.

Efficient use of resources

The quality of data impacts the proper functioning. low-quality data leads to inefficient use of finances. Implementing the DQM process in the organization can save from the wastage of resources and allow in getting better results and moving towards achieving the goals.

Competitive advantage

In this growing world of competition, the organization needs to build and maintain its reputation. Maintaining and providing high-quality data ensures maintaining a higher reputation. Low quality of data can lead to distrust and dissatisfaction in their customers. To gain customer confidence, it is important to have high-quality data.

Good business leads

The different processes for business development like sales and marketing campaigns require quality data. Using low-quality and erroneous data can lead to targeting the wrong set of customers. This will adversely affect the sales and thus hamper the business growth.

Data completeness

Implementing the DQM allows in completing the data by collecting and compiling the data and filling in the missing details. This completeness of data helps in improving the quality of data.

Data auditability

The data quality management process helps in proper documentation of the details. It also allows tracking the changes introduced in the data to use the updated data at all times.


Manually handling and organizing the data can be tedious, tiresome, and also time-consuming. Implementing the DQM process enables faster management of the data while maintaining the data standards.

Data Quality Management Best Practices

Well, the above section will give clear thoughts on the impact of having bad customer data along with the importance for a brand to maintain its data quality standards through a data management system. However, to reap the full benefits of a data quality management process, there are certain practices to be followed.

Here are the lists of best practices that an organization needs to follow to improve their data quality and these include:

Prioritizing data quality

It is necessary to understand that the data has higher priority and all the employees should know its importance. For this, the organization should design a proper strategy for handling the data. It is also important to create different users for accessing the data based on their role.

This will give the rights and accountability access to the user for handling the data. It is important to have a monitoring dashboard for keeping the check of the different parameters like consistency, accuracy, auditability, completeness, orderliness, timeliness, and uniqueness of the data.

Automated data entry

It is very important to maintain the quality of data. The organization has to handle a lot of data and handling all the data manually can lead to the incorporation of errors that can affect the quality of the data. Implementing the DQM process allows the automation of data entry. Automating data entry leads to a reduction in human error and maintaining higher quality.

Prevent data duplication

The data is imported from various sources and thus available in various formats and attributes. There are chances of having duplicate data that can lead to increased confusion.

Implementing DQM helps in identification of the similar data in the database. This can cause an error while merging the entries. DQM eases the detection and deletion of duplicate data.

Handling master and metadata

Handling of the master data is very important but the metadata should also be considered while incorporating the data. For keeping the data updated based on real-time situations, it is important to use the metadata that will allow replacing the obsolete data and adding the updated ones.

Establish metrics

It is essential for tracking the quality of the data and assessing the ability for improving it over some time. The metrics are essential for measuring the quality of the data.

The metric includes the details like the number of redundant entries, incompleteness, data that cannot be analyzed due to format incompatibilities, etc. The data metrics will allow efficient access to the quality of data and further improve it.

Bottom Line

Data plays an important role in all the stages of business growth and development. The data should be handled carefully and maintained in a hassle-free manner. Implementing the data quality management process in the organization enables improving the quality of the data that will have a positive impact on the status and reputation of the organization.

Apart from that, it will help the brand on building a strong customer base and drive more target audiences as brand customers. DQM also leads to gaining a competitive advantage in the industry.

Say goodbye to data chaos and embrace the power of PIM/MDM to supercharge your business growth!