Another week bites the dust. Quarantine week 14 is complete. On Monday hair and nail salons were allowed to open up. People are eating in restaurants, and life is slowly getting back to normal. Temperatures have been heating up, and my adventures have only been taking place bright and early. Here\’s a peek at some of my favorite things from this past week.

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.