The article sheds light on some key elements of SQL Server’s Data Quality Services.
Data Quality Services, introduced in SQL Server 2012, are an exciting feature of SQL which helps users in improvement and maintenance of the data quality in your enterprise. Data Quality Services solved many problems that users faced in previous versions, like inconsistency, inaccuracy, duplicity and invalidity, etc. which further lead to wrong analytics results, data mining and reporting related problems. So, DQS provided data quality solutions of these issues to IT professionals to ensure that Data quality isn’t compromised.
This article explains the key elements of Data Quality Services and how useful they are while working with SQL Servers.
1. Challenges and Causes
Prior to its release there were many challenges that users faced such as Duplicity of data, Non Conformity, Inaccuracy and Invalidity in data values and inconsistent data.
These issues caused several problems as they led to incorrect results and analytics which nearly kill the purpose of having a database. There are also chances of data manipulation if they are consolidated from different sources which have distinct representative formats. So there are chances that the data can get corrupted during storage or transmission. To overcome these challenges, Data Quality Services were introduced.
2. Data Quality Services (DQS)
DQS helps IT professionals in maintaining the integrity and quality of data by applying interactive or automated/batch mode approaches. DQS has some of the most exciting features which include Semantics, Knowledge Discovery, Open and Extensible and handy use. In DQS, users can create a reusable DQKB which is Data Quality Knowledge Base that improves the integrity of data. There you can create and capture semantics to map data into data domains. It has made SQL Server very user friendly and focuses on enhancing productivity.
DQS basically provides its users a knowledge base about the data which can be used to improve the quality of database and its efficiency.
DQS has also extended its support to Windows Azure Marketplace to use Reference Data Services. DQS helps in monitoring the state of data activities and its quality which gives users an upper hand on its progress and control. The analytical work has been made easier through DQS as it performs analytics to provide insights about the Data Quality and helps in sorting issues with quality at various stages and processes.
4. Cleansing and Matching
Using SSIS Component, DQS client tool can perform cleansing in an interactive manner. Users can update, amend, enrich or remove data that is wrong or incomplete seamlessly. It includes standardization and Correction too. The cleansing component classifies the data in Correct, Incorrect, New, Auto Suggested, etc. categories.
Other than Cleansing, DQS suite also includes Matching which helps in removing duplicates from the data sources. It identifies and links and merges related entries across the sets of data. It can be performed either inter source (matching source against look-up table) or intra source (matching the source against its own) depending on the requirement. It is usually done in four steps which are Matching Policy Training, Matching, Auto Approving and Survivor-ship.
While data quality is important, companies also need to ensure the safety of the data stored in their SQL databases. Thus it is imperative that they invest in a damaged mdf recovery tool to negotiate any issues of data corruption.
Victor Simon is a data recovery expert in DataNumen, Inc., which is the world leader in data recovery technologies, including Access recovery and sql recovery software products. For more information visit https://www.datanumen.com/