Data Quality Services (DQS) is a crucial concept in SQL Server that allows users to identify and correct data quality issues. In this blog post, we will provide an overview of DQS and walk through the process of building a knowledge base and using it to improve data quality.

Building a New Knowledge Base

To begin, we need to create a new knowledge base in DQS. This knowledge base will serve as a reference for identifying and correcting data quality issues. We can use various data sources, such as Excel files or SQL Server tables, to build our knowledge base.

Once we have selected our data source, we can define domains within the knowledge base. Domains represent the different attributes or columns in our data. For example, if we have a dataset with columns for “Colors” and “Shade,” we can create separate domains for each column.

After defining the domains, we can proceed with the data discovery process. DQS will analyze the source data and provide insights into the quality of the data. It will identify unique and non-unique values, as well as the frequency of each value.

Creating a New Data Quality Project

Once we have built our knowledge base, we can start a new Data Quality Project in DQS. This project allows us to apply the knowledge base to a dataset and clean the data based on the defined domains.

In the Data Quality Project, we can select the knowledge base we created earlier and map the columns from our dataset to the corresponding domains. This mapping ensures that the correct rules and corrections are applied to each attribute.

Next, we can initiate the cleaning process by clicking on the “Start” button. DQS will analyze the data, suggest corrections for any incorrect values, and provide a summary of the cleaning process.

Reviewing and Approving Corrections

After the cleaning process, we can review the suggested corrections and manually approve or reject them. DQS provides a user-friendly interface where we can easily navigate through the corrected and validated data.

Once we have approved the corrections, the data will be moved to the “Corrected” tab, indicating that the quality issues have been addressed. We can also explore the summary of all activities performed during the cleaning process.

Exporting the Cleaned Data

Finally, we have the option to export the cleaned data to various formats, such as a SQL Server table, CSV file, or Excel. DQS also allows us to choose whether to include all the associated cleansing information or just the data itself.

By following these steps, we can effectively use Data Quality Services in SQL Server to improve the quality of our data. The process is straightforward and can be easily learned by anyone.

If you are currently using DQS in your production environment, we would love to hear about your experiences and how it has benefited your business. Please leave a comment below with details about your environment and business needs.

Stay tuned for future blog posts where we will explore more advanced concepts related to Data Quality Services in SQL Server.