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The Importance of Data Quality Management

The study of advanced business analytics focuses heavily on data. Students enrolled in the University of North Carolina at Pembroke's MBA with a Concentration in Business Analytics online examine the many ways data informs business analytics practices. This includes the study of programming and debugging software, data analysis, the application of information, and data-driven insight to create actionable processes.                 

Students integrate this in-depth knowledge of data analytics with an understanding of many other essential aspects of business management. After all, nearly every function of modern business depends on some form of data analysis, from customer relations to marketing strategy. But data-driven decision-making is only as good as the data it's based on. Enter data quality management (DQM), a fundamental part of business analytics.

How Does Good Data Affect an Organization's Success?

An organization lives or dies on optimizing operations and maximizing return on investment (ROI). Outcomes rely heavily on data analysis and data-driven processes. Good data can be used to pinpoint and address problems, as well as their root causes, effects and potential solutions. Current, accurately analyzed data can be essential to predicting market trends and areas for resource allocation. Good data and analysis can help an organization capitalize on opportunities, secure a competitive advantage and, where necessary, take corrective measures.

What About Bad Data?

"Dirty" data can be downright destructive. If a sizable portion of customer data is incorrect, incomplete or redundant, the analysis and use of it can lead to inefficient resource utilization and poor decision-making. Poor integration of information across departments can produce inconsistencies in the customer experience affecting brand loyalty and organizational reputations.

Data must be integrated across departments and systems in the most efficient and consistent manner. As the name suggests, DQM is responsible for ensuring the highest quality control. Thus, DQM plays an essential role in maximizing each data-driven process and system.

How Does DQM Work?

DQM reflects a complex set of processes, but it should not be a "one-and-done" endeavor. DQM combines organizational structure, business intelligence (BI) systems, and a continuous, iterative approach sometimes called the data quality cycle.

Data quality is to some degree subjective, measured by usefulness to organizational operations. A DQM system's effectiveness is judged by both defined data quality metrics and quantitative changes in supporting goals, such as increasing the ROI of a marketing campaign. DQM should therefore be developed and actuated in the context of organizational goals, vision and size.

Many DQM systems follow a similar procedural design, although they use different terms. At the root of DQM are its people, often including:

  • A program manager to provide general oversight and program implementation.
  • A data steward responsible for more focused data management, defining rules, etc.
  • A data manager or analyst responsible for the nuts and bolts — qualifying data needs and quantifying them into the processes and systems they are measured by.

The DQM process is often organized into:

  • Establishing data quality definitions and rules — accuracy, consistency across platforms and systems, completeness, timeliness, uniqueness of data or lack of duplicates — as well as desired outcomes and metrics.
  • Analyzing or profiling data.
  • Monitoring, recording and reporting exceptions/bad data.
  • Cleansing data, or correcting and repairing errors.

DQM also involves standardizing data processes and integration across organizational systems and between departments, although this may be considered part of master data handling — the overarching management of an organization's complete data set.

This continuous, iterative process of DQM is integral to high-functioning, data-driven business, and business analytics is at the root of managing data quality, from multi-source inputs to end-user outputs for both customers and employees. With a comprehensive understanding of the scope and purpose of the data life cycle and its application, graduates of UNCP's MBA with a Concentration in Business Analytics online play a vital role in this essential quality management process.

Learn more about UNC Pembroke's online MBA with a Concentration in Business Analytics program.


Sources:

BMC Blogs: What Is Data Quality Management?

Bi-Survey: Data Quality and Master Data Management: How to Improve Your Data Quality

Gartner: How to Create a Business Case for Data Quality Improvement

SAS: Data Quality Management: What You Need to Know

Datapine: The Ultimate Guide to Modern Data Quality Management (DQM) for an Effective Data Quality Control Driven by the Right Metrics

Datapine: Top 10 Analytics and Business Intelligence Trends for 2019


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