This document provides an overview of master data management and how it relates to data governance. It defines key concepts like master data, reference data, and different master data management architectural models. It discusses how master data management aligns with and supports data governance objectives. Specifically, it notes that MDM should not be implemented without formal data quality and governance programs already in place. It also explains how various data governance functions like ownership, policies and standards apply to master data.

1. Master Data Management’s Place in the Data
Governance Landscape
SQL Server Master Data Services in Action
February 21, 2019

2. Introductions
CCG Data Governance
Master Data Management Defined
Why Master Data Management?
MDM and Data Governance
Master Data and Reference Data
Master Data Architectures/Models
Demo

3. Presenters
David Langlois
Data Governance Lead
Tony Williams
Data Management Strategist
Cherian Mathew
BI Architect

4. CCG
Analytics Solutions & Services
DATA
MANAGEMENT
Data & analytics consultants with a passion for helping clients
overcome business challenges & increase performance by
leveraging modern analytic solutions.
BUSINESS
ANALYTICS
DATA
STRATEGY

5. CCG Analytics
We bring great People together to do extraordinary Things
DATA ANALYTICS STRATEGY
Working with CCG is like working with extended team members. Consultants become an
integral part of the work bringing expertise for cutting edge design and development.
- CIO, HCPS

6. CCG Clients

7. Key Drivers and Benefits of Data Governance
Information Fit for Purpose

8. 1 2 3
Key Drivers for Data Governance:
Inactive
There are some aspects of
DG employed within the
organization, but there
are no enterprise
standards in place(e.g. the
IS team has developed a
data dictionary).
Reactive
The enterprise is responding
to a specific issue or problem
(e.g. data breach or audit).
The enterprise is facing a
major change or there is a
potential regulatory threat to
the organization (e.g. GDPR,
acquisitions, or preparing for
a pubic offering)
Proactive
The enterprise recognizes
the value of data and has
decided to treat data as a
corporate asset (e.g.
recruitment of a CDO,
budgeted DG program,
etc.).

9. 1 2 3
Benefits of DG
Increase Revenue
Improve opportunity to
rapidly exploit
information for business
insights and competitive
advantage
Improve profitability with
better analytics for
improved decision making
Reduce Cost through
Operational Efficiencies
Smoother business
processes
Standardized and high
quality information
Reduced IT costs
Minimize Risk
Reduce regulatory
compliance risk and improve
confidence in operational
and management decisions
Provide better insights into
fraud with improved
analytics; Improve reporting
to regulators and authorities
through defined data
processes and data
management

10. Respondents to CIO Watercooler’s Data Governance Survey 2017 -
How long has your organization been implementing data governance?

11. “Hiding within those mounds of data is
knowledge that could change the life of a
patient, or change the world.”
– Atul Butte, Stanford
“Information is the oil of the 21st century,
and analytics is the combustion engine.”
– Peter Sondergaard
“$3.1 trillion, IBM’s estimate of the yearly
cost of poor quality data, in the US alone,
in 2016” – Harvard Business Review
Data Informed, “Without accurate data
on customers, an organization can’t
achieve revenue goals. Poor data quality…
could lead to missed sales opportunities.”
Forrester reports that “Nearly one third of
analysts spend more than 40 percent of
their time vetting and validating their
analytics data before it can be used for
strategic decision-making”
Ponemon Institute and GlobalScape report
the annual cost of non-compliance to
businesses now runs an average of $14.8
million, a 45% increase since 2011
GDPR: There will be two levels of fines
based on the GDPR. The first is up to €10
million or 2% of the company’s global
annual turnover of the previous financial
year, whichever is higher. The second is up
to €20 million or 4% of the company’s
global annual turnover of the previous
financial year, whichever is higher.
According to the Forbes Insights and
KPMG “2016 Global CEO Outlook,” 84% of
CEOs are concerned about the quality of
the data they’re basing their decisions on.

12. Master Data Management
Alignment with Data Governance

13. What Is Master Data?
Definitions
Master Data…
Data that provides the context for business activity data in the form of common and abstract concepts that relate to
the activity. It includes the details (definitions and identifiers) of internal and external objects involved in business
transactions, such as customers, products, employees, vendors, and controlled domains (code values).
Master Data Management…
Processes that control management of master data values to enable consistent, shared, contextual use across
systems, of the most accurate, timely, and relevant version of truth about essential business entities. (DAMA-
DMBOK Guide, 1st edition, pg. 171.)
Master Data Management Architecture Pattern…
Architecture patterns, when applied to MDM, address the scope and architectural aspects of MDM. Consideration
of the various Enterprise Master Data business and technical strategies, master data implementation approaches,
and MDM methods of use are also included in the MDM architecture pattern. (IBM, Information service patterns,
part 4)

14. Connecting Master Data Management And Data Governance
Master Data Management…
Should not be undertaken without a formal and functioning Data Quality Program – it is difficult to develop a DQ
program at the same time as an MDM program
A Data Quality Program will require an information ownership model to be established (stewardship) – implementing
MDM will use this model
And we should all already know this: Data Governance and Data Quality have a symbiotic, mutually interdependent
relationship. You shouldn't want one without the other.
Therefore, to ensure take up, adoption, and sustainability of an MDM program, we need Data Governance!
Like Data Governance and Data Quality, MDM is a business program, not a technology solution implementation!
Therefore, it must be sponsored by the business. If implementing MDM is under the CIO, it is not receiving the
enterprise attention is must have to become a sustaining program
Master Data Management Program Sponsorship…

15. Master Data Management
Some Key Implementation Factors
MDM needs to align to data governance and data management strategy and objectives – which are in turn aligned to
business strategy and objectives. The business case used to justify a Data Governance (or Data Quality) program can be
reused and adapted for a MDM program.
When determining data and information (management) functions as part of Data Governance Functional Design, we
map them to business objectives. An MDM program needs to align to the same business objectives and to the designed
data functions specified in Data Governance.
Requires an information/data ownership model, and that data stewardship be in place or implemented simultaneously
Like Data Governance, an MDM program should have an appropriate scope: Geographic, lines of business, enterprise-
wide. In fact it will often align to DG’s boundaries and federation.
Policies and standards will need to shift, be added, changed, etc. when implementing MDM. Data Governance provides
the policy and standards framework and the processes and procedures to accomplish this
Processes and procedures around our data will change because we are implementing MDM. Data Governance to the
rescue again – processes and procedures should already be defined for all data management functions
Some Key Factors in Implementing MDM… Other Factors?

16. Data Governance and Data Management
MDM is a Core Data Management Function
Data Governance
Data Quality Program
Master Data Management
Data and
Information
Life Cycles
Data Management
ENSURING DATA IS WELL-GOVERNED ENSURING THE RIGHT DATA IS WELL-MANAGED
Making sure data is managed properly Managing data to achieve goals
Data Governance Council
• Planning Functions
• Design Functions
• Managing Functions
• Operate Functions
• Planning Functions
• Design Functions
• Managing Functions
• Operate Functions
• Planning Functions
• Design Functions
• Managing Functions
• Operate Functions
DataOwnershipandStewardship

17. Improve data quality – although a MDM program only improves data quality to an extent – we need quality to be governed over the
long term and the Data Quality program to frame and guide our efforts as we clean (and keep clean) data for use in MDM
Streamline data architecture
Reduce data integration complexity – regardless of the architecture approach chosen for MDM there is a huge opportunity to investigate
current state data integration, movement, and interoperability
Fully enable ownership and stewardship – MDM can further maturity around data ownership and stewardship. Data steward processes
and procedures to maintain reference data in its various locations should become much easier as this data BECOMES master data.
Tools integrate with metadata management needs – from a tool and technology perspective, MDM provides another source and target
of enterprise metadata.
Natural overlap in capturing business rules to enable process changes
Facilitate process and culture change – especially towards a culture of data
Why are we talking about MDM?
Some compelling outcomes we may be seeking… Other Potential Outcomes?

18. Master Data Management
Master Data Management’s Mission
Apply a combination of data governance practices and technologies intended to confirm integrity and accuracy of
data to help organizations frame the “single version of the truth” needed to answer business questions and drive
insights.
What business questions and insights?
KPIs related to revenue growth, for example:
– Revenue per customer
– Inventory levels
– Customer profitability
– New customer sales due to new products/services launched

19. Master Data vs. Reference Data
What is Reference Data?
Reference Data Definition
Is data that is used to structure and constrain other data. Typically, it is very stable with a known set of values that
are rarely changed
It classifies and categorizes other data, and it can be hierarchical
Examples of Reference Data
Geographical Locations – localities, states, regions, countries, zip/postal codes, census blocks
Financial Markets – valid stock tickers, for example
Industry Classifications – NAIC, SIC, etc.
Science – known elements in the periodic table
Calendars – lists of days of the week, months, ISO weeks, reporting weeks, accounting months
Product Hierarchy – a company’s product listing used as reference data (for purchase and customer orders,
inventory, etc.)**
Reference data is any data we agree can be used as reference data across our enterprise and systems

20. Master Data vs. Reference Data
The Differences, the Similarities
Reference Data Master Data
Provides structure to other data ✓ ✓
Constrains other data ✓ ✓
Provides context to other data ✓ ✓
Analytical “Slicers and Dicers” ✓ ✓
Limited to pre-defined values ✓ 
Can be defined/controlled by business rules  ✓
Represent business entities (the “nouns” of a business)  ✓
Data quality formality and requirements  
Data governance control and authority  
Data ownership and stewardship  
Managed via standard technology/tools/procedures (MDM!)  

21. External
Reference Data Management
Internal and External Reference Data
Internal
Internal reference data applies to business
concepts that are specific to the
organization.
Managing internal reference data requires
a federated approach, since it is created
and managed by many different business
data stewards.
Requires alignment to the data ownership
model
External reference data is maintained by
authorities outside the enterprise (e.g.,
ISO, government agencies, etc.).
External reference data must be
discovered, selected, understood, and
captured appropriately.
May align to the ownership model
Common
Mapping reference data often requires
human judgment, so the need for
intervention by business data stewards in
the reference data management process
should not be overlooked.
Requires standard processes and
procedures to maintain
Apply MDM governance, processes, procedures, technology

22. Master Data Management Architectural Models
Application
Application DB
Master Data Hub or
Repository
Registry
Master
Data
Repository
Master
Data
Hybrid
Master
Data

23. Registry (or Federated) MDM Architecture
Application #1
Application DB contains:
Business key + some attributes
Application #2
Application DB contains:
Business key + some attributes
Master Data Hub
System of Record: MDM Hub
System of Entry: Application
Timing: Batch
Hub stores
• Business keys
• Limited attributes
+ -
Minimal application
changes
Queries hit against all
systems containing
elements involved
Minimal impact to end
users of applications
Master data is focused
on referential use
Quicker to deploy
Lower implementation
cost
The most common approach pulls limited
master data from application systems and
stores in a hub.

24. Data Governance with Registry (or Federated) MDM Architecture
Application #1
Application DB contains:
Business key + some attributes
Application #2
Application DB contains:
Business key + some attributes
Master Data Hub
System of Record: MDM Hub
System of Entry: Application
Timing: Batch
MDM Hub stores
• Business keys
• Limited attributes
• Stewards at the application level to manage
data quality
• Potential for differentiated stewardship
roles (application vs. data vs. business)
• Clear communication on batch timing
• Reporting may source from application or Hub
database depending on timing requirements

25. Repository MDM Architecture
Application #1
Application DB contains:
Only business key
Application #2
Application DB contains:
Only business key
Master Data Repository
System of Record: Repository
System of Entry: Repository
Timing: Realtime
Repository stores
• All business keys
• All attributes
+ -
No versioning across
applications
May be hard to modify
OTS applications.
Less duplication of data Difficult to model
completeness and
usability
Cost
All master data is in the repository, with
other systems modified to access repository

26. Repository MDM Architecture
Application #1
Application DB contains:
Only business key
Application #2
Application DB contains:
Only business key
Master Data Repository
System of Record: MDM Repository
System of Entry: MDM Repository
Timing: Realtime
MDM Repository stores
• All business keys
• All attributes
• Technical Stewards at the integration points,
responsible for ensuring that input and output
match
• Implementing data quality rules
• Monitoring quality and performance
• Issue management processes to support
integration issues
• Repository is primary reporting data source

27. Coexistence MDM Architecture
Application #1
Application DB contains:
Master data records
Application #2
Application DB contains:
Master data records
Master Data Hub
System of Record: MDM Hub
System of Entry: Application
Timing: Realtime, potential latency
Hub
• Enhances data
• Publishes data back to apps
+ -
Consolidated master
data
Master data is focused
on referential use
Documents lineage Cost of integration
Easier to monitor
processes
A coexistence architecture will ingest data
from source systems, then publishes it back
to other source systems.

28. Coexistence MDM Architecture
Application #1
Application DB contains:
Master data records
Application #2
Application DB contains:
Master data records
Master Data Hub
System of Record: MDM Hub
System of Entry: Application
Timing: Realtime, potential latency
Hub
• Enhances data
• Publishes data back to apps
• Federated stewardship
• Quality controls in place at MDM hub
• Reporting source is MDM Hub primary, other
attributes from applications as needed
• Technical stewardship of integrations required

29. Transaction MDM Architecture
Application #1
Application DB contains:
Calls to MDM Hub
Application #2
Application DB contains:
Calls to MDM Hub
Master Data Hub
System of Record: MDM Hub
System of Entry: MDM Hub
Timing: Realtime, potential latency
Hub publishes data to apps
+ -
Single point of entry Cost of integration
Centralized control
Simplified lineage
Easier to monitor
processes
In a transactional style, the MDM hub
becomes the sole entry system, then
publishes this data to applications.

30. Transaction MDM Architecture
Application #1
Application DB contains:
Calls to MDM Hub
Application #2
Application DB contains:
Calls to MDM Hub
Master Data Hub
System of Record: MDM Hub
System of Entry: MDM Hub
Timing: Realtime, potential latency
Hub publishes data to apps
• A centralized team of stewards can support
master data
• MDM main reporting source
• Strong support of quality
• Technical stewardship of integrations required

31. An enterprise’s current state data architecture and data movement/integration patterns inform MDM, and provide requirements for
determining the MDM architecture patterns needed to support the implementation of MDM
– Examples: MDM publish/subscribe, MDM message-based integration, MDM transaction interception, MDM BI/Analytics, MDM data warehouse
patterns
MDM Bi/Analytics Pattern and Insights
– In a BI/Analytics pattern, there is an opportunity to determine whether insights gained in BI and analytics systems have relevance to the MDM system
– Can lead to a two-way integration, where results of insights can be used to update attributes in the MDM hub [e.g., customer attributes]
MDM Data Warehouse Patterns and the EDW
– This pattern describes the integration between MDM solution and data warehouses
– The DWs (typically) are treated as downstream systems that do not provide master data back to the MDM platform
– We often find that MDM data can source conformed slowly changing types of dimensions within the warehouses
– Impacts to existing integration / ETL can often be made much simpler, especially in the case of conformed dimensions with multiple sources
• DWs often serve as a merge/consolidation mechanism in the absence of MDM
– No feedback from DWs to the MDM hub
– Extracts/Rest APIs from the MDM hub require data to be in a consistent and well-formed state to support bulk extraction methods typically used in
DW ETL
Architecturally, Where does MDM Fit In?

32. DATA
MGMT
SOURCES STOREINGEST PREP & TRAIN MODEL & SERVE
Modern Cloud Analytics Data Architecture: Governed Delivery with Quality
Big Data Storage
Core Deposits, Member
Information
Analytical
Services
Enterprise
Data WarehouseInternet/Mobile Banking
and Bill Pay
Rel Mgmt, Contact
Tracking, Referral
Tracking
Imaged Statements
Structured Data
Stage and Archive Loaders
Unstructured Data
Unstructured Data Loaders
ODS /
Archive
Transform Transform
Check Images
Debit Card
Processing
Analytic Enablement
Streaming
Analytics
Machine
Learning
Cognitive
Analytics
Data
Exploration
Business Intelligence
Platform
Dimensional
Data Storage
G/L, Fixed Assets, Accts
Payable, Acct Receivable
Universal
Semantic Layer
Data Quality Metadata Management Data Architecture Data Security & Privacy
Data Lineage
DQ Corrective Actions
DATA GOVERANCE PROGRAM MANAGEMENT
MDM Hub
MDM Solution Patterns

33. RapidDG
5-6 week engagement
Business case development
Charter development
Competency level analysis
9-12 month roadmap
Data Governance Service Offerings
CCGDG (includes RapidDG)
9-12 month engagement
Alignment to organizations
strategic plan
Competency/marker level
analysis
Marker level strategic roadmap
DG Consultative Services
Variable

34. Questions before demo?

35. Demo
Microsoft Master Data Services

36. SQL Server MDS 2017 Overview
https://docs.microsoft.com/en-us/sql/master-data-
services/master-data-services-overview-mds?view=sql-server-
2017
Server requirements: Pre-Installation and Post Installation Tasks
– IIS, SQL Server 2017
• Other years supported, but 2017 cleanest
https://docs.microsoft.com/en-us/sql/master-data-
services/install-windows/install-master-data-services?view=sql-
server-2017
Licensing / cost:
– Enterprise (license fees) and Developer Edition (free)
Client operating requirements (IE, Silverlight Plugin)
MDS: Setup

37. MDS: Functional Overview
High level overview of the MDS functions
– MDS Service
– User access to master data
• Data Explorer in the Web UI
• Data Explorer Add-In in Excel
– MDS Database
– SSIS connections via:
• the subscriptions views
• Direct table connections

38. Demo
MDS

39. Start with Data Governance
Ensure Data Quality
Make sure you’ve defined data ownership
Master your first Master Data Entity
– Define key attributes
– Identify business & data quality rules
– Begin modeling out an architecture that fits your organization
• Determine the needed architecture patterns
– Choose appropriate systems based on scale and environment
• Excel
• Master Data Services
• Larger enterprise systems
Next Steps: Evolution of an MDM Program

40. Networking, Q&A, and Discussion

41. Thank You!