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 | Data Quality Basics and Introducing Data Quality Services (DQS) This is the first training video in the series that introduces the concept of data quality and its importance to any business. It also introduces you to Data Quality Services (DQS), new feature is SQL Server 2012, which provides a knowledge-driven solution to easily improve data quality through profiling, cleansing, and matching of data. Wednesday, Feb 1 |
 | How Do I: Install and Configure DQS? SQL Server Data Quality Services (DQS) is a new feature in SQL Server 2012 that provides you with a knowledge-driven data cleansing solution. In this video, you will learn about the prerequisites to install DQS, how to install DQS, and how to perform post-installation tasks to configure DQS. This video walks you through a basic installation where we use the SQL Server setup installation wizard to perform installation, and all the DQS components are installed on the same computer. Monday, Oct 10 |
 | How Do I: Manage a knowledge base in SQL Server Denali DQS? The DQS client application enables you to perform data quality operations using a standalone tool. To cleanse data, you have to have knowledge about the data. To prepare knowledge for a data quality project, you build and maintain a Data Quality knowledge base (KB) that DQS can use to identify incorrect or invalid data. In this video you will learn how to add knowledge through a computer-assisted process - the knowledge discovery activity - and how to add domain rules.This video is a prerequisite for "How Do I: Cleanse my data with SQL Server Denali DQS", "How Do I: Create a Matching Policy with SQL Server Denali DQS?" and for "What is a Composite Domain and How Do I use it to cleanse my data with SQL Server Denali DQS?". Monday, Oct 10 |
 | How Do I: Cleanse my data with SQL Server Denali DQS? Data cleansing is the process of analyzing the quality of data in a data source, and manually applying rules and making changes to the data. Data cleansing includes a computer-assisted process that analyzes how data conforms to the knowledge in a knowledge base, and an interactive process that enables the data steward to ensure that the data cleansing includes exactly what they want done. In this demo we will learn how to use a published data quality knowledge base to cleanse our data. We will open a cleansing project, select a source file, and go through the computer-assisted cleansing process and the interactive process. In the end we will export the cleansing results. Monday, Oct 10 |
 | What is a Composite Domain and How Do I use it to cleanse my data with SQL Server Denali DQS? Before viewing this video, we recommend watching "How Do I: Manage a knowledge base in SQL Server Denali DQS?. Data contains different types of data types: some are simple and stand alone, and some are composite and are more meaningful when grouped together. A composite domain enables you to group together individual domains that represent common data. For example, addresses can contain house number and street name, city, state, and Zip Code, and names of people can contain title, first name, last name, and gender. In this demo, we will learn how to create a composite domain, how to create business rules for composite domains, and how to use a composite domain to cleanse data. Monday, Oct 10 |
 | How Do I: Cleanse my data with SQL Server Denali Data Quality Services (DQS) and Data Market Reference Data Service (RDS) Provider? Before viewing this video, we recommend watching "How Do I: Cleanse my data with SQL Server Denali DQS?" and also watching "What is a Composite Domain and How Do I use it to cleanse my data with SQL Server Denali DQS?". There are various reference data providers today with different types of reference data, such as address data, company data, Geocoding, etc. These providers can help you cleanse and enrich your data easily using DQS and the DataMarket Reference Data service provider. Instead of building and maintaining your own knowledge, you can use external knowledge to cleanse the data. You can attach a DQS domain or a DQS composite domain to a DataMarket reference data service provider and use it to cleanse and enrich your data. In this video we will learn how to configure, and then use, the Reference Data Services feature in SQL Server Data Quality Services (DQS) to cleanse your data. Monday, Oct 10 |
 | How Do I: Cleanse my data with SQL Server Denali SSIS DQS Cleaning Component? Before viewing this video, we recommend watching "How Do I: Cleanse my data with SQL Server Denali DQS?". Many real-world scenarios require running cleansing as a batch process. The DQS Cleansing component in SQL Server Integration Services addresses these scenarios. In this video, we will learn how to define and use the DQS Cleansing component in a SSIS data flow. Please note: familiarity with SSIS is a prerequisite for understanding this video. Monday, Oct 10 |
 | How Do I: Create a Matching Policy with SQL Server Denali DQS? Before viewing this video, we recommend watching "How Do I: Manage a knowledge base in SQL Server Denali DQS?. Data matching analyzes your data and groups matching records. The challenge for a matching system is to overcome errors and variations that naturally exist in the data and across different data sources. DQS can help you create rules that will identify matching records in a controlled and interactive fashion. In this demo we will learn how to create a matching policy in a knowledge base that we created earlier. Monday, Oct 10 |
 | How Do I: Match my Data with SQL Server Denali DQS? Before viewing this video, we recommend watching "How Do I: Create a Matching Policy with SQL Server Denali DQS?". Data matching analyzes your data and clusters matching records. The challenge for a matching system is the degree to which it can understand and overcome the errors and variations that naturally exist in the data and across different data sources. DQS helps you identify and remove duplicate records from your data in a guided and easy way. In this demo we will learn how to use the matching policies defined in the knowledge base that we created in the previous video to match your data in a data quality project. Monday, Oct 10 |
