A Complete Guide to Modern Data Quality Management (DQM)

Modern Data quality management

Data quality management (DMQ) refers to a set of practices that aim at maintaining a high quality of information. DQM involves the acquisition of data, the implementation of advanced data processes, and the effective distribution of data. It also involves an executive oversight of the data you have. Effective DQM is vital to any consistent data analysis because the quality of data is key to developing actionable and accurate insights from your data.

DQM processes basically prepare your organization to face the challenges of digital age data, wherever and whenever they appear. Data quality management helps businesses by combining organizational culture, technology, and data to deliver results that are accurate and useful. Data quality management offers context-specific processes for refining the suitability of data that is used for analysis and decision making. The idea is to create insights into the fitness of that data via various processes and technologies on increasingly bigger and more complex data sets.

Why You Need Data Quality Management

The truth is that data quality management is an essential process in creating value from your data. This ultimately helps your bottom line because good data quality management builds the foundation for all business initiatives. In essence, data quality management program establishes a framework for all departments in the organization to provide and enforce rules for data quality.

 Then again, accurate and up-to-date data provides a clear picture of your company’s day-to-day operations. This allows you to be confident in upstream and downstream applications that use all that data. Data quality management also cuts down on unnecessary costs since poor data quality can lead to costly mistakes and oversights such as spending or losing track of orders. You need data quality management because it builds an information foundation that allows you to understand your organization and expenses by having a firm grasp on your data.

 One other reason you need data quality management is to meet compliance and risk objectives. This is because good data governance requires clear procedures and communication, as well as good underlying data. Data quality is an important part of implementing a data governance framework. And good data quality management supports data stewards in doing their jobs.

Given the repurposing of the same data sets in different contexts, you need good data quality to grasp both structured and unstructured big data sets. This repurposing has the negative effect of giving the same data different meanings in different settings and raising questions about data validity and consistency.

Another reason to use data quality management is that when using externally created data sets which are commonplace in big data, it can be difficult to embed controls for validation. So while correcting the errors will make the data inconsistent with its original source, maintaining consistency can lead to making some concessions on quality. Indeed, balancing oversight with big data sets needs data quality management features that can provide a solution.

 You also need data quality management because of data rejuvenation. This extends the lifetime of historical information that previously may have been left in storage on the one hand while also increasing the need for validation and governance on the other. New insights can then be extracted from old data after that; it must be correctly integrated into newer data sets.

How Do You Measure Data Quality?

 You need data quality metrics to measure data quality. This is because these metrics are key in assessing your efforts in increasing the quality of your data. It is important that the data quality metric is of premium quality and is clearly defined. Data quality metrics cover various aspects of quality, which are summed up by the acronym “ACCIT.”This stands for Accuracy, Consistency, Completeness, Integrity, and Timeliness.

It’s a fact that data analysis is complex. However, there are a few basic measurements that all key DQM stakeholders must be aware of. Data quality metrics help to provide a rock-solid base for future analyses. They also allow you to track the effectiveness of your quality improvement efforts. Here’s some detailed info on data quality metrics.


Refers to business transactions or status changes as they happen in real-time. Accuracy refers to business transactions or status changes as they happen in real-time. It should be measured via source documentation (i.e., from the business interactions), in the absence of which independent confirmation techniques could be used. Accuracy indicates whether data is void of significant errors.

One way to measure accuracy is the ratio of data to errors, which tracks the number of known errors relative to the data set. It is important that this ratio increases over time because it proves that the quality of your data can improve. However, there is no specific ratio of data to errors because it depends on the size and nature of your data set, although the higher, the better, of course.


What consistency in this context refers to is the lack of conflict between two data values that are pulled from separate data sets. However, consistency does not automatically indicate correctness.

For instance, rules that will verify that the sum of employees in each department of a company does not exceed the total number of employees in that organization are examples of consistency.


This indicates if there is enough information to draw conclusions. Completeness can then be measured by defining whether or not each data entry is “full” data entry. This means that all available data entry fields must be complete, and sets of data records should contain pertinent information.

One simple quality metric you can use is the number of empty values within a data set. Say, for example, in the inventory context, each line of an item refers to a product. Each of them must have a product identifier, and until that product identifier is filled, the line item is not valid. The goal of monitoring the metric over time is to reduce it.


Integrity refers to the structural testing of data to ensure that the data complies with procedures. In some circles, it is known as data validation, and it ensures that there are no unintended data errors and the data corresponds to its appropriate designation

In the end, it all comes down to the data transformation error rate. You want to use metrics that track the process of taking data stored in one format and converting it to a different one is not successfully performed.


This matches the prospect for availability and accessibility of information. In other words, timeliness measures the time between when data is expected and the moment when it is readily available for use.

A metric to this is the data time-to-value. It is essential to measure and optimize this time because of its repercussions on the success of a business. Optimally, the best moment to derive valuable information of data is always now, so the earliest you have access to that information, the better.

However, you choose to improve the quality of your data; you will always need to measure the effectiveness of your efforts. This is because data quality metrics make a good assessment of your processes, and shouldn’t be left out of the picture. The truth is, the more you assess, the better you can improve.

What Are Data Quality Metrics Examples

Here are 5 data quality metrics examples you can use:

  1. Ratio of Data To Errors: This monitors the number of known data errors when compared to the entire data set.
  2. Number of Empty Values: This counts the number of times you have an empty field within a data set.
  3. Data Time-To-Value: This metric evaluates the length of time it takes to gain insights from a data set. Although there are other factors influencing it, yet the quality is one of the main reasons this time can increase.
  4. Data Transformation Error Rate: With this metric, you can tracks how often data transformation operation fails.
  5. Data Storage Costs: When your storage costs go up while the amount of data you use remains the same, gets worse or decreases, it might mean that a significant part of the data stored is of quality too low be used.

Benefits of Data Quality Management

  1. Lesser mailing costs: Since accurate customer data reduces the amount of undeliverable mail, you are able to save money in postage costs. You are also able to save some money when you do not have to continually resend packages that would have arrived the first time safely if your data was accurate in the first place.
  2. Better customer relations: It is a given that accurate data improves relations with your customers. This is because data enables you to truly know your target audience, so you do not need to send an email that they do not want to read. It also helps you to anticipate and meet their needs better. Sending the right mail and anticipating needs create a great deal of goodwill with your customer base and improve your business. Indeed, this is one of the major reasons why data quality is important.
  3. More consistent data: Larger companies and organizations which offer several points of entry for their customers and clientele must continually face the problem of inconsistent data. This keeps the company and organizational departments from reaching key clients, confuses the mailing system, and it creates a host of other problems. 

With data quality management, different departments are on the same page when analyzing and meeting the needs of clients are involved.

Hi my name is rohit sharma and i love blogging.I have been writing articles on range of topics.

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