5 Best Data Quality Management Practices

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Data is becoming an essential part of every business, as it should. When it comes to data there is an endless amount of resources and information you can pull to figure out who your customers are, what their needs are, where you can improve, and more. The quality of the data that is gathered, stored, and consumed during business processes, determines the overall success. Throughout this post you will learn why data quality is important, the characteristics that define data quality, what data quality management is and the best data quality management practices.

Why is data quality important?
Data quality is important because, without high-quality data, you will not be able to understand customers. Today, data is easier than ever to acquire, therefore finding out key information about customers and tailoring your marketing to their needs is easier than it has ever been before.

7 characteristics that define data quality
Accuracy and Precision
Legitimacy and Validity
Reliability and Consistency
Timeliness and Relevance
Completeness and Comprehensiveness
Availability and Accessibility
Granularity and Uniqueness

What is data quality management?

The goal of data quality management is to showcase a set of actions in order to prevent future data quality issues. Key Performance Indictors also know as KPIs are needed to achieve business objectives. Your data quality KPIs must directly relate to the KPIs that are being used to measure the business in general. The following disciplines are used to help prevent data quality issues:
Data Governance
Data Profiling
Data Matching
Data Quality Reporting
Master Data Management (MDM)
Customer Data Integration (CDI)
Product Information Management (PIM)
Digital Asset Management (DAM)
Data quality best practices

5 best data quality management practices1. Make sure multiple people are involved in data quality management. Especially top-level management. More often than not data quality issues can only be solved when there is a cross-departmental view. Therefore you want someone besides a data quality analyst involved.
Pro Tip: At least have someone from upper management, IT, and an analyst involved on the data quality management team.

2. For every data quality issue, start at the root cause.  Data quality problems will only go away if the solution addresses the root cause. If you do not determine what the root cause is, then you will never actually fix the problem and only continue to put a bandaid on it.
Pro Tip: Make note of any issues that come up and how you resolved them. That way if they come up in the future they can be solved quickly. 

3. Define data quality KPIs that are linked to the general KPIs of the business.
Pro Tip: Your data quality KPIs and your business KPIs should directly be related to one another. 

4. A business glossary should be used as the foundation for metadata management. Metadata is data about data and allows for common data definitions.

5. When you find solutions to problems, make sure you implement processes and/or technology that can prevent the issues in the future.
Pro Tip: gartner master data management will be a critical part of data quality management practices. 

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