What Are Data Quality Dimensions?

Data-Quality-Dimensions-25

“A recent survey by TDWI found that 66% of organizations are looking for ways to improve data quality and trust. Data Validation using Data Quality testing tools such as Datagaps DataOps suite is essential to ensure trust in your data and analytics.”

The Data Quality dimensions provide a way to categorize data validation rules and measure data quality. There are seven data quality dimensions that are commonly used to measure data quality.

Get a Free Trial of DataOps DQM - Data Quality Monitor for 14 Days

How To Measure Data Quality Using Data Quality Dimensions?

Datagaps DataOps suite automatically computes Data Quality Scores for each rule based on the number of good vs bad data. This score is rolled up to Data Quality Dimensions at Table, Data Model, and System level.

A sample dashboard showing the Data Quality trend is shown below:

data-quality-dimensions

Figure: Shows the Data Quality Dimensions and their scores at a system-level DataOps Data Quality comes with the following rule types to make it easy to define Data Validation rules:

How To Compute Data Quality Score?

Data Quality Score allows us to quickly understand the current state of our data and more easily compare quality over time. Data Quality Scores will be a percent calculated by the following:

= [1 – (# of bad records / # total records)] x 100
Data-Quality-scores1

Figure: Shows the trend of Data Quality scores at the system level

Conclusion

Automated Data Quality testing can be done using Data Validation rules. Data Quality Score provides a means to measure and track the Data Quality of your enterprise data at rest and in motion. Data Quality dimensions help categorize the data validation rules into meaningful buckets. DataOps Data Quality is a simple Data Validation testing tool that can be used to automate the Data Quality testing process.

Datagaps-logo-1536x406-1

Established in the year 2010 with the mission of building trust in enterprise data & reports. Datagaps provides software for ETL Data Automation, Data Synchronization, Data Quality, Data Transformation, Test Data Generation, & BI Test Automation. An innovative company focused on providing the highest customer satisfaction. We are passionate about data-driven test automation. Our flagship solutions, ETL ValidatorDataFlow, and BI Validator are designed to help customers automate the testing of ETL, BI, Database, Data Lake, Flat File, & XML Data Sources. Our tools support Snowflake, Tableau, Amazon Redshift, Oracle Analytics, Salesforce, Microsoft Power BI, Azure Synapse, SAP BusinessObjects, IBM Cognos, etc., data warehousing projects, and BI platforms.  www.datagaps.com 

Related Posts:

Data Quality

Automate testing of Business Intelligence applications by making use of the metadata available from the BI tools such as Tableau, OBIEE, and Business Objects.

Synthetic Data

Automate testing of Business Intelligence applications by making use of the metadata available from the BI tools such as Tableau, OBIEE, and Business Objects.

ETL Testing

Automate testing of Business Intelligence applications by making use of the metadata available from the BI tools such as Tableau, OBIEE, and Business Objects.

BI Validation

Automate testing of Business Intelligence applications by making use of the metadata available from the BI tools such as Tableau, OBIEE, and Business Objects.
Products
product_menu_icon01

DataOps Suite

End-to-End Data Testing Automation

product_menu_icon02

ETL Validator

Automate your Data Reconciliation & ETL/ELT testing

product_menu_icon03

BI Validator

Automate functional regression & performance testing of BI reports

product_menu_icon04

DQ Monitor

Monitor quality of data being Ingested or at rest using DQ rules & AI

product_menu_icon05

Test Data Manager

Maintain data privacy by generating realistic synthetic data using AI

About
Free Trial