Data Quality Services in SQL Server 2012 (An Introduction) Stéphane Fréchette Friday April 26, 2013 Matching Cleansing DQS 

Who am I? My name is Stéphane Fréchette I’m a Database & Business Intelligence Professional and CEO | Founder of I have a passion for architecting, designing and building solutions that matter. Self proclaimed Open Data Hacker/Advocate I founded Gatineau Ouverte a citizen led initiative which aims to promote open access to civic data of the city of Gatineau. Twitter: @sfrechette Email: [email protected] Blog: stephanefrechette.com 

Session Outline • Microsoft Business Intelligence (The Stack) • Dirty Data… • SQL Server Data Quality Services (DQS) • Data Steward • Knowledge Base and Domains • Data Quality Projects • Data Cleansing Transform – SSIS • DQS (Install & Architecture) • Enterprise Information Management (EMI) • Resources 

Analysis Services Reporting Services Integration Services Master Data Services SharePoint Collaboration Excel Workbooks PowerPivot Applications SharePoint Dashboards & Scorecards Data Quality Services OData Feeds Line of Business Applications Hadoop Big Data Microsoft Business Intelligence 

Dirty Data… Do you have dirty data? (all projects have it! Its inevitable) 

Dirty Data… Causes? Bad data entry Poor Data Governance Duplicate entities in different LOB systems 

Sample Data Representation • Prospect in CRM System: Mark Smith | 613.111-1234 | Ottawa | ON | K1P 1K1 • Prospect buys goods now entered in POS System: Markus Smith | 1234 Stilton Ave | Kanata |ON | K1P 1K1 • Record also entered into Accounting System: Markus Smith | 1234 Stilton Avenue | Kanata | ON | K1P 1K1 ETL process imports these records into the Data Warehouse / Data Mart FirstName LastName Phone Address City Province PostalCode Mark Smith 613.111-1234 Ottawa ON K1P 1K1 Markus Smith 1234 Stilton Ave Kanata ON K1P 1K1 Markus Smith 1234 Stilton Avenue Kanata ON K1P 1K1 

Sample Data Representation • Duplicate records and inaccurate, incomplete data • What we want is a golden record (one version of the truth) FirstName LastName Phone Address City Province PostalCode Mark Smith 613.111-1234 Ottawa ON K1P 1K1 Markus Smith 1234 Stilton Ave Kanata ON K1P 1K1 Markus Smith 1234 Stilton Avenue Kanata ON K1P 1K1 FirstName LastName Phone Address City Province PostalCode Markus Smith 613-111-1234 1234 Stilton Ave Kanata ON K1P 1K1 

SQL Server Data Quality Services (DQS) • New in SQL Server 2012 • Enables cleansing, matching, standardizing and enriching data • Delivers trusted information for business intelligence, data warehouse, transaction processing workloads • Knowledge-Driven Solution (create/edit) • A knowledge management process that builds the knowledge base • A data quality project that proposes changes to source data based on the knowledge in the knowledge base (cleansing and matching) • A key component to an Enterprise Information Management (EIM) solution 

Answering the Need with DQS • DQS enables to resolve issues involving incompleteness, lack of conformity, inconsistency, inaccuracy, invalidity, and data duplication • Provides the following features to resolve data quality issues:  Data Cleansing  Matching  Reference Data Services  Profiling  Monitoring  Knowledge Base 

Data Steward • Key role - Is usually a Business User and not from the Information Technology side • Nutshell: Responsible for maintaining data elements in a metadata registry… • Data Steward -> DQS Client • Create and edit Knowledge Bases • Run and process data though continually, iteratively, improving the Knowledge Bases • Knowledge Bases can be consumed and used by other Data Stewards and IT (SSIS / ETL Developers) DQS Data Steward MDS Data Steward SSIS Developer Matching Cleansing 

Knowledge Bases and Domains The knowledge base is a repo of knowledge about your data that enables you to understand your data and maintain its integrity. • Processes: • Computer-assisted • Interactive • Components: • Knowledge Discovery • Domain Management • Reference Data Services • Matching Policy 

Demo Knowledge Base Management (Creating a Knowledge Base) 

Data Quality Projects Improve quality of source data by performing data cleansing and data matching activities using defined knowledge bases • Cleansing Activity (2 step process) • Computer-assisted : data is categorized (suggested, new, invalid, corrected, and correct) • Interactive: data steward to approve, reject, or modify the proposed results from the computer-assisted cleansing process • Matching Activity • Using existing knowledge base matching policy • Prevent and remove data duplication • Data Profiling and Notifications • Profiling provides data quality stats and info: completeness and accuracy • Notification on actions that can be taken to enhance operations 

Demo Data Quality Project (Cleansing and Matching) 

DQS Cleansing Transform in SSIS • When you want to automate the cleansing and matching process and not use the DQS Client • Use SSIS for batch data cleansing • Matching can be done with Master Data Services (MDS) • SSIS can be leveraged to bring DQS and MDS together *DQS does not expose matching functionality for SSIS, but you can use Fuzzy Grouping Transform to identify duplicate data *Cleansing Transform is single threaded – use multiple transform for parallelism 

Demo Data Cleansing Transform (Automating the Cleansing and Matching using SSIS) 

Installing DQS • Requires Business Intelligence or Enterprise/Developer version of SQL Server 2012 • During SQL Server setup; • Instance Features -> Data Quality Services • Shared Features -> Data Quality Client • Execute the Data Quality Server Installer; • C:\Program Files\Microsoft SQL Server\MSSQL11.MSSQLSERVER\MSSQL\Binn\DQSInstaller.exe • Data Quality Service – Data Quality Server Installer (Apps - Microsoft SQL Server 2012) 

DQS Architecture DQS Server DQS Catalog (3 databases) • DQS_MAIN (Knowledge Bases) • DQS_PROJECTS (Projects) • DQS_STAGING_DATA (Sandbox, scratch pad area) Security – Database Roles • dqs_administrator • dqs_kb_editor • dqs_kb_operator 

Windows Azure Marketplace Reference Data Services -> validating, cleansing and enriching your data 

Performance considerations - FYI • Major performance improvements from RTM to CU1 release of SQL Server 2012 (strongly recommend patching and upgrading) http://bit.ly/11eEhHC • Must read -> DQS Performance Best Practice Guide http://bit.ly/16Gwenl • Understand data volumes and hardware requirements… plan wisely! 

Enterprise Information Management (EIM) The EIM Stack as a whole is the ‘Master Data Management’ solution from Microsoft and consist of the following: • SQL Server Data Quality Services (DQS) - Capture and record knowledge, rules, and actions • SQL Server Master Data Services (MDS) - Master Data Management repository, Dimension data • SQL Server Integration Services (SSIS) – Moves data, integration Enterprise Information Management (EMI) ‘Master Data Management’ 

Resources • Data Quality Services Team Blog (MSDN) http://bit.ly/WCI2nO • SQL Server Data Quality Services (TechNet) http://bit.ly/ZaUO8k • DQS Performance Best Practices Guide http://bit.ly/16Gwenl • Enterprise Information Management (EIM) Bringing Together SSIS, DQS, and MDS (Video – Channel 9) http://bit.ly/NJXvKr • Matt Masson – Getting Started with DQS and MDS http://bit.ly/149Ga9n • Paras Doshi’s – Blog (DQS) http://bit.ly/YoLthh 

What Questions Do You Have? 

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