Data quality management What you need to know

As organizations gather more data, managing the quality of that data becomes more important every day. After all, data is the lifeblood of your organization. Data quality management helps by combining organizational culture, technology and data to deliver results that are accurate and useful.

Data quality is not good or bad, high or low. It is a series or measure of the health of the data being pumped through your organization. For some processes, a marketing list with 5 percent duplicate names and 3 percent bad addresses may be acceptable. However, if you comply with regulatory requirements, the risk of fines requires higher levels of data quality.

Data quality management provides a context-specific process for improving the suitability of data used for analysis and decision making. The goal is to create insights into the health of data using various processes and technologies on ever-larger and more complex data sets.

Why do we need data quality management?

Data quality management is an important process to make sense of your data that can ultimately help your bottom line.

First, good data quality management builds a foundation for all business initiatives. Outdated or unreliable data can lead to errors and mistakes. A data quality management program establishes a framework for all departments in the organization that contains – and enforces – data quality rules.

Second, accurate and up-to-date data provides a clear picture of your company’s day-to-day operations, so you can be sure of upstream and downstream applications that use all this data. Data quality management also reduces unnecessary costs. Poor quality can lead to costly mistakes and oversights, such as losing orders or spending. Data quality management builds an information foundation that allows you to understand your organization and expenses by having a good grip on your data.

Finally, you need data quality management to meet compliance and risk goals. Good data management requires clear procedures and communication as well as good underlying data. For example, a data management committee can define what should be considered “acceptable” for the health of the data. But how do you define it in the database? How do you monitor and enforce the policies? Data quality is an implementation of the database-level policy.

Data quality is an important part of implementing a data management framework. And good data quality management supports data providers in their work.

The dimensions of data quality management

There are several data quality dimensions in use. This list continues to grow as data grows in size and diversity; However, a few of the core dimensions remain constant across data sources.

  • Accuracy measures the extent to which the data values ​​are accurate – and are crucial to the ability to draw accurate conclusions from your data.
  • Completeness means that all data elements have specific values.
  • Consistency focuses on uniform data elements across different data instances with values ​​extracted from a known reference data domain.
  • Age addresses the fact that data needs to be fresh and current with values ​​that are up to date everywhere.
  • Uniqueness shows that each record or item is represented once in a dataset, which helps avoid duplicates.

Key Features in Data Quality Management

A good data quality program uses a system with a number of features to help improve the reliability of your data.

First, data cleaning helps correct duplicate records, non-standard data representations, and unknown data types. Purge enforces the data standardization rules needed to deliver insights from your data sets. This also creates data hierarchies and definitions of reference data to customize data to suit your unique needs.

data Profiling
, the data monitoring and purification action, is used to validate data against standard statistical targets, reveal conditions, and verify data against matching descriptions. Data profiling steps will establish trends that will help you discover, understand and potentially reveal discrepancies in your data.

Validating business rules and creating a business glossary and lineage helps you act on poor quality data before it harms your organization. This involves creating descriptions and requirements for system-to-system translations. Data can also be validated against standard statistical goals or custom rules.

In addition to these key features, having a central view of business activity through a data management console is a key way to make the process easier.

How important is data quality management to big data?

Big data has and will continue to have a disruptive impact on businesses. Consider the huge amount of streaming data from connected devices on the Internet of Things. Or numbers of transfer tracking points that flood business servers and need to be combined for analysis. With all that big data you are having bigger data quality issues. These can be summarized into three main points.


These days there is a violent repetition of the same data sets in different contexts. This has the negative effect of giving the same data different meanings in different settings – and raising questions about data validity and consistency. You need good data quality to understand these structured and unstructured big data sets.


When using the externally created datasets that are common in big data, it can be difficult to integrate validation controls. Correcting the errors will make the data incompatible with their original source, but maintaining consistency can mean making some concessions on quality. This issue of balancing oversight of large data sets asks for data quality management capabilities that can provide a solution.


Rejuvenating data extends the lifespan of historical information that may have been previously stored, but it also increases the need for validation and governance. New insights can be extracted from old data – but first, this data must be properly integrated into newer datasets.

Where and when should data quality happen?

You can best observe data quality management in action through the lens of a modern data problem. In real-life applications, different data problems require different delays.

For example, there is a real-time need for data quality when processing a credit card transaction. This can mark fake purchases and help both customers and businesses. However, if you update loyalty cards and reward points for the same customer, you can perform overnight treatment for this less urgent task. In both cases, you apply the principles of data quality management in the real world. At the same time, you recognize the needs of your customers and approach the task in the most efficient and helpful way possible.

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