1. 06 | Managing Enterprise Data
Richard Currey | Senior Technical Trainer–New Horizons United
George Squillace | Senior Technical Trainer–New Horizons Great Lakes

2. Module 6 Overview
Master Data Services
Data Quality Services

3. Topic: Master Data Services

4. Topic: Master Data Services
What Is Master Data Services?
Data Models
Entities and Attributes
Hierarchies
Business Rules
Consuming Master Data

5. What Is Master Data Services?
The SQL Server solution developed to help manage data at an enterprise level
Targeted at data that is shared by multiple databases
Allows an organization to implement centralized storage
Relies on ETL to manage the collection and distribution of the data between instances
Uses data models and business rules to define the master data

6. Data Models
A data model is a collection of entities, attributes, and hierarchies
Master Data Services can have several data models
Data models should represent specific areas of the business
Customers Model
Version 1
Version 2
Version 3
Account Type Entity
Customer Entity
Attributes:
Code (string)
Name (string)
Code: 1
Name: Standard
Member
Code: 2
Name: Premier
Code: 1235
Name: Ben Smith
Address: 1 High St, Seattle
Phone:
AccountType: 1
CreditLimit: 1000
Code (free-form text)
Name (free-form text)
Address (free-form text)
Phone (free-form text)
AccountType (domain-based)
CreditLimit (free-form number)
Contact Details Attribute Group

7. Entities and Attributes
Each entity represents a business object that can appear in one or more systems
Entities contain attributes that are the values that need to be known about an entity
Entities have members that are the instances of an entity
Entities typically map to tables in an RDBMS, with attributes mapping to columns
Members typically map to rows in the table

8. Hierarchies Represent relationships between entities or entity members
Derived hierarchies represent the relationship between members
Based on attribute values
Explicit hierarchies represent the relationship between individual members of an entity
e.g. all European customers
Used when there is no other entity to relate the members together
Mandatory hierarchies require all members of the entity to be assigned
Derived Hierarchy
Code: 1
Name: Standard
Account Type Member
Code: 1235
Name: Ben Smith
Account Type: 1
Customer Member
Code: 1267
Name: Amy Strande
Explicit Hierarchy
Code: CustUS
Name: US Customers
Consolidated Member
Code: 1235
Name: Ben Smith
Leaf Member
Code: 1267
Name: Amy Strande
Code: CustEU
Name: European Customers
Code: 2214
Name: Sabina Schütz

9. Creating A Data Model

10. Business Rules What is the biggest myth in IT?
“My data is clean”
Ensure that data is valid and correct
Validated in Microsoft Excel or in the MDS web interface

11. Implementing Business Rules

12. (e.g. Data Warehouse ETL)
Consuming Master Data
Master data architecture is typically a hub and spoke architecture
Data is originally entered into operational systems
Data is then transferred to the MDS database
Data is validated and cleansed in the MDS database
MDS data is then used to update the operational systems
CRM
Marketing System
Order Processing System
Master Data Hub 
Data Steward
Other consumers
(e.g. Data Warehouse ETL)
SSIS

13. Topic: Data Quality Services

14. Topic: Data Quality Services
What Is Data Quality Services?
Building a Data Quality Services Solution
Accessing Reference Data
Validating Data with Data Quality Services
Validating Data with SSIS

15. What Is Data Quality Services?
Data is the lifeblood of any organization
Incorrect data can lead to incorrect decision making
Data Quality Services (DQS) implements the tools to ensure that data is correct
Knowledge bases
Domains
Reference data services KB
DQS Client
Data Cleansing Transformation
SSIS
DQS Server

16. Building a Data Quality Services Solution
Create a knowledge base
Provides access to specific areas of information
Create domains
Provides a repository for the rules relating to specific data
Define rules relating to valid or invalid values
Define matching policies to determine if two values represent the same logical entity

17. Implementing Data Quality Services

18. Accessing Reference Data
Reference data provides values that are used as “correct” during a cleansing project
Reference data can originate from inside or outside the organization
The Widows Azure Marketplace DataMarket is one external source of reference data

19. Validating Data with Data Quality Services
Data cleansing projects allow data stewards to validate and correct data
Projects require a knowledge base and a data source
Cleansing can include validation, matching, or both
Result includes the original data, cleansed values, reason, status, and confidence

20. Creating a Data Cleansing Project

21. Validating Data with SSIS
Create a data cleansing project
Define data source to be cleansed
Configure a data cleansing transform
Specify the knowledge base
Map data columns
Define data destination or subsequent transformations

22. Configuring the Data Cleansing Transform