Microsoft SQL Server Master Data Services DesignMind

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By Mark Gschwind, VP Business Intelligence > A recent survey by Information Week found that data quality is the greatest barrier to BI adoption in enterprises. MDS addresses this challenge with modeling, validation, alerting and security capabilities.

In this presentation, you will learn how to use MDS to model your data to ensure correctness, update it with changes from your ERP, and alert users to data conditions that require attention. Next you will learn the capabilities of DQS and see how it addresses data standardization, completeness and uniqueness challenges.

You will then see how to use them together to enable Enterprise Information Management. BI professionals will come away with knowledge on how to use tools that address the greatest risk to success for BI projects

Mark Gschwind is an expert on Data Warehousing, OLAP, and ERP migration. He is VP, Business Intelligence at DesignMind in San Francisco. Prior to that he was with AMB Property Corporation. He has authored three successful enterprise data warehouses and over 80 OLAP cubes for 46 clients in a wide range of industries. Certified in SQL Server and Oracle Essbase. MBA in Finance from Duke University, BBA from the University of Notre Dame.

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Microsoft SQL Server Master Data Services DesignMind

  1. 1. Master Data and Data Quality Management in SQL Server 2012Mark GschwindVP, Business IntelligenceDesignMind
  2. 2. Mark Gschwind VP of Business Intelligence at DesignMind PASS member for over 10 years BI Consultant since 1995 BI implementations for over 50 clients  Data Warehousing/Cubing/Reporting/Data Mining/EIM MCP, certified in Oracle Essbase Working with clients on EIM since 2008 mgschwind@designmind.com find me on
  3. 3. DesignMind Microsoft Gold Certified Partner San Francisco based, 25 people, 3 MVPs Capabilities include .NET Development, SharePoint, SQL Server, and Business Intelligence Data Warehouses, Reporting, Analytics, Dashboards, Mobile, EIM Focus on delivering value from BI using Agile Methodology www.designmind.com
  4. 4. Agenda Enterprise Information Management (EIM)  What is it and why do we need it? Microsoft EIM, 3 technologies working together  DQS • Capabilities • Demo  SSIS  MDS • Capabilities • Demo  EIM=DQS+MDS+SSIS Wrap up Questions
  5. 5. Why Do We Need EIM?
  6. 6. Impediments to EIM Success
  7. 7. DATA QUALITY MASTER DATASERVICES SERVICES INTEGRATION SERVICES
  8. 8. What is Data Quality?
  9. 9. DQS: What is Data Quality? Data Quality represents the degree to which the data is suitable for business usages Data Quality is built through People + Processes + Technology Bad Data  Bad Business “Poor data quality can cost companies 15% to 25% (or more) of their operating budget” - Larry English (International Data Quality Expert)
  10. 10. Common Data Quality IssuesData Issue Sample Data ProblemQualityStandard Are data elements consistently Gender code = M, F, U in one system and defined and understood? Gender code = 0, 1, 2 in another systemComplete Is all necessary data present? 20% of customers’ last name is blank, 50% of zip-codes are 99999Accurate Does the data accurately A Supplier is listed as ‘Active’ but went out of represent reality or a verifiable business six years ago source?Valid Do data values fall within Salary values should be between acceptable ranges? 60,000-120,000Unique Data appears several times Both John Ryan and Jack Ryan appear in the system – are they the same person?
  11. 11. Common DQ Issues Illustrated Name Gender Street House # Zip code City State D.O.BBefore John Doe Male 60th street 45 New York New York 08/12/64 Jane Doe Male Jonathan ln 36 10023 Poughkeepsy NY 21-dec-1954 Name Gender Street House # Zip City State D.O.B code John Doe Male E 60th St 45W 10022 New York NY 08/12/64After Jane Doe Female Jonathan 36 10023 Poughkeepsie NY 12/21/54 Lane Completeness Accuracy Conformity Consistency Uniqueness Name Address Postal Code City StateBefore John Smith 545 S Valley View Drive # 136 34563 Anytown New York Margaret & John smith 545 Valley View ave unit 136 34563-2341 Anytown New York Maggie Smith 545 S Valley View Dr Anytown New York John Smith 545 Valley Drive St. 34253 NY NY Name Address Zip Code City State ClusterAfter John Smith 545 S Valley View Drive # 136 34563 Anytown New York 1 Margaret & John smith 545 Valley View ave unit 136 34563-2341 Anytown New York 1 Maggie Smith 545 S Valley View Dr Anytown New York 1 John Smith 545 Valley Drive St. 34253 NY NY 2
  12. 12. DQ Use Cases• One-Time cleanups o Merge/Migrate multiple divisional CRMs into one• Continuous Process with Steward Intervention o Vendor master with continuous trickle of data o Customer master with incomplete data• Continuous Process with Minimal Intervention o Database marketing mailing list
  13. 13. DQS Process Knowledge ManagementBuild Knowledge BaseUse
  14. 14. Demo
  15. 15. Integrate DQS using SSIS(continuous low-intervention use case)
  16. 16. MDS: What is Master Data?  Continuous quality management  Ease of use for business users (not just IT)  Effective sharing (producing and consuming)  Centralized maintenance, by different departments  Changes that keep pace with the business Master Data contains different attributes for different departments (marketing, finance, operations, business groups…) The challenge: To make a trusted single source of business data used across multiple systems, applications, and processes
  17. 17. MDS Use CasesRegulatory Data Warehouse / Operational Data Data Marts Mgmt ManagementEnable security Enable business users to Central data recordsmanagement and auditing manage the dimensions mgmt and consumptionof data used for and hierarchies of DW / sourced by otherregulatory reporting Data Marts operational systems The IT department has built a A company has adopted 6 “best There are 3 G/L systems of breed” systems from data warehouse and reporting whose G/L accounts need to platform, but business users different vendors. They need be consolidated and rolled up complain about the to be able to propagate the to create financial statements correctness of the dimensions correct customer information to for regulatory reporting to and lack of agility in making each system in a consistent several countries updates. way. MDS enables an approval MDS provides a platform for MDS empowers the process for changes with business users to manage central schema, integration role-based security and dimensions themselves points and validation for transactional auditing of all while IT can govern the SI/ISV/Internal IT to develop a changes custom solution changes
  18. 18. Where is Master Data (in a DW)?
  19. 19. MDS Capabilities Modeling Validation Authoring business rules Entities, Attributes, to ensure data Hierarchies correctness Master Data Data MatchingRole-based Security and Stewardship (DQS Integrated)Transaction Annotation Excel Add-In Web UI Versioning Enabling Integration & Sharing Loading batched Registering to Consuming data Workflow / data through changes through through Views Notifications Staging Tables APIs External Excel DWH (CRM, ..)
  20. 20. MDS Architecture WEB-UI Excel Add-In Silverlight WCF BizTalk / Others Workflow / MDS Service Notifications CRM/ERP IIS Service DWH SSIS BI OLAP SSIS Excel Subscription Entity Based Cleansing and PW Views Staging Tables Matching Pivot MDS Database SSIS (DQS) External System External System
  21. 21. Demo
  22. 22. Business Rules Business Rules are expressions and actions that can govern the conduct of business processes* Enable data governance by: -- Enforcing data standards -- Alerting users to data quality issues -- Creating simple workflows Have limitations, but can be extended*EIM = DQS+MDS+SSIS+People+Process
  23. 23. Security Functional area permissions Model/Entity level permissions provide column- level security Hierarchy permissions allow row-level security Use AD groups, not individual users Only use Hierarchy permissions if row-level security is required
  24. 24. DATA QUALITY MASTER DATASERVICES SERVICES INTEGRATION SERVICES
  25. 25. Key Takeaways SQL Server has tools to address EIM, the biggest impediment to BI success EIM is People + Processes + Technology
  26. 26. Questions?Mark Gschwindmgschwind@designmind.comSee my slides on SlideShare

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