Quick Start Guide to Data Quality Management

Proper data quality management is the pillar of any successful business. Decisions that are based on high-quality data will lead to prolonged success for your organization. Many organizations tend to overlook properly managing data, but in every business operation, data plays an essential role. Here is everything you need to understand data, and adequately managing it.

What is Data Quality Management?

Data quality management (DQM) is an ambiguous term, as it refers to a set of procedures that aims at maintaining high-quality data and information. Effective DQM usually entails effective data distribution, managerial oversight, and other techniques to ensure quality control. When it comes to any form of data analysis, DQM plays an essential role because the quality of data is what is used to provide insight towards any organization.

The benefits of effective DQM span a wide variety of areas. From supply chain management to customer relationship management, an organization’s performance can be positively impacted if data management is high.

What is Data Quality?

Data quality itself refers to the full assessment of information that a business has relative to what its ambitions are. Usually, the quality of data be defined by its accuracy, completeness, timeliness, and consistency. In order to fulfill the obligations that a business has, the data quality needs to be very high.

In modern times, a company’s decisions rely on the quality of data. Usually if data quality is low, then this can result in failure of technology initiatives, Business decisions and important marketing choices all hinge on where the quality of data currently lies. If there are great deficiencies and gaps in a data’s accuracy, for example, a business’ finances can tank immensely.

Important Roles in DQM

DQM is usually split up into a series of pillars that incorporates everything that needs to be known about how essential this is to an organization.

Proper DQM is a holistic effort that incorporates many factors. The first pillar is the people that are responsible for handling the data.

First, there is the DQM Program Manager who is responsible for oversight on intelligence initiatives. This is a managerial role that is filled by a high-level leader who oversees the management of daily activities regarding program implementation and data scope.

There is also the business analyst who accurately defines the quality needs specifically from an organizational viewpoint. Once these needs are identified, they are then quantified into models for delivery.

Finally, there is the organizational change manager who assists the organization by providing insight into data technology solutions. Their role is essential when it comes to highlighting quality issues and then visualizing the data quality.

Data Profiling

The next pillar in DQM is data profiling, which is an essential part of the data life cycle that cannot be skipped. This is a holistic process which includes:

  • reviewing the data holistically
  • comparing the data to the available metadata
  • running statistics
  • reporting the data quality.

The reason why this process is essential is that it develops further insight into not only the purpose of the data and why it’s being used but how well it aligns with the goals.

Defining Data Quality

The next pillar of DQM is defining the data itself. This comes by establishing “quality rules” that are based on business requirements and goals. These rules are essentially the standards that data must meet in order to be considered viable to use by a business. These rules will also prevent data that is compromised or inaccurate from infecting the entire data set. Only data that is pertinent to the goals of the business will be used.

Many consider setting “quality rules” for data similar to the mechanism of action of antibodies. Antibodies detect and correct viruses in our bodies. In like manner, these quality rules can correct blatant inconsistencies in the data being used.

Data Reporting

Data reporting is an essential process of removing compromised data while capturing exceptions. These exceptions can then be adequately aggregated so that quality patterns can be immediately identified.

Reporting data also provides visibility into the state of data at any current moment of time. Data specialists can be afforded the opportunity to undergo remediation processes as needed.

Data Repair

The final pillar of DQM is data repair. This is a two-step process that determines not only the most efficient way to remediate the data but the most efficient manner in which the change can be implemented.

Another aspect that defines what data repair is is the “root cause” examination that determines how certain data defects originated.