Here we will see Data Quality Services (DQS) : Data Cleansing & Data Matching
Table of Contents
Introduction to Data Quality Services (DQS)
- SQL Server Data Quality Services (DQS) is a knowledge-driven data quality product. Building a knowledge base using DQS gives you the ability to use it for a number of crucial data quality tasks, such as data correction, enrichment, standardization, and de-duplication.
- Data Quality Server and Data Quality Client are the two components of DQS, and SQL Server includes both of them by default. Data Quality Server is a SQL Server instance feature comprised of three SQL Server catalogues which provide data-quality functionality and storage.
- Data Quality Client is a SQL Server shared feature that allows business users, information workers, and IT professionals to conduct computer-assisted data quality analyses and manage data quality in an interactive manner.
- Data Quality Services (DQS) offers a data-quality solution that allows a data steward or IT professional to maintain the integrity of their data and ensure that it is fit for business use.
- DQS is a knowledge-driven solution that allows you to manage the integrity and quality of your data sources in both computer-assisted and interactive ways.
- DQS allows you to discover, create, and manage knowledge about your data. This knowledge can then be applied to data cleansing, matching, and profiling.
Problems with Inaccurate Data
- Inaccurate data can be caused by user input errors, transmission or storage corruption, mismatched data dictionary definitions, and other data quality and process issues.
- Aggregating data from different sources that use different data standards, as well as applying an arbitrary rule or overwriting historical data, can result in inconsistent data.
- Incorrect data negatively affects a company’s ability to perform business functions and provide services to customers, resulting in a loss of credibility and revenue, customer dissatisfaction, and compliance issues.
- Incorrect data frequently causes automated systems to fail, and bad data wastes the time and energy of people performing manual processes. Incorrect data can disrupt data analysis, reporting, data mining, and data warehousing.
DQS – The Best Solution for Busines Requirements
- A data quality solution can improve data reliability, accessibility, and re-usability.
- It can improve your data’s completeness, accuracy, conformity, and consistency, resolving issues caused by bad data in business intelligence or data warehouse workloads as well as operational OLTP (Online Transaction Processing) systems.
- DQS facilitates a non-programmer business user, information worker, or IT professional to create, maintain, and execute their organization’s data quality operations with minimal setup or preparation time.
- DQS indicates potentially wrong data and gives you a prediction of how likely is that the data is inaccurate. You can analyze the suitability of the data by using the semantic knowledge of the data that DQS gives you.
- DQS provides you to resolve issues involving incompleteness, lack of conformity, inconsistency, inaccuracy, invalidity, and data duplication.
Data Quality Features
DQS provides the following features to resolve data quality issues.
Data cleansing : It is the process of modifying, removing, or enriching incorrect or incomplete data using both computer-assisted and interactive processes.
Matching : It is the method of identifying semantic duplicates in a rules-based process that allows you to determine what constitutes a match and removes duplicate data.
Profiling : profiling is the study of a data source to give insight into the quality of the data at each level of the knowledge discovery, domain management, matching, and data purification processes
Knowledge Base : Data Quality Services is a knowledge-driven solution which analyses data using the knowledge you develop. This makes it possible for you to develop data quality procedures that continuously advance our understanding of your data, thereby enhancing its quality.
