What gets measured, gets managed; but what gets governed, generates real value. That's one major reason why data governance has risen to a top priority for most organizations. Another reason is the rapid onboarding of big data, which often comes from beyond the traditional firewall. And then there are the authorities: issues like privacy, security and fiduciary responsibility are combining to make data governance a must-have. Register for this episode of The Briefing Room to hear veteran Analyst Dr. Robin Bloor explain why governance should be viewed as a positive change agent for the modern enterprise. He'll be briefed by Ron Huizenga of IDERA, who will discuss a practical, model-based approach to enterprise data governance, with a focus on Master Data Management.

1. The Briefing Room
Tuesday, May 2, 2017 @ 4 ET
Tweet with #BriefR

3. Governance
• Carrots & Sticks
• Control Points
• Pragmatism
• Durability
• Balancing Act
• Transparency
• Enforceability
• Chinese Handcuffs

4. 1© 2017 IDERA, Inc. All rights reserved.
THE MODEL ENTERPRISE:
A BLUEPRINT FOR ENTERPRISE DATA GOVERNANCE
MAY 2, 2017
Ron Huizenga
Senior Product Manager, Enterprise Architecture & Modeling
@DataAviator

5. 2© 2016 IDERA, Inc. All rights reserved. Proprietary and confidential. 2© 2017 IDERA, Inc. All rights reserved.
AGENDA
§ Governance Overview
§ Definitions
§ Master Data
§ Data lineage & life cycle
§ Master Data Management (MDM)
§ Importance of Data Models
§ Data quality
Data
Governance
Data
Architecture
Management
Data
Development
Database
Operations
Management
Data Security
Management
Reference &
Master Data
Management
Data
Warehousing
& Business
Intelligence
Management
Document &
Content
Management
Metadata
Management
Data Quality
Management

6. 3© 2016 IDERA, Inc. All rights reserved. Proprietary and confidential. 3© 2017 IDERA, Inc. All rights reserved.
ER/STUDIO ENTERPRISE TEAM EDITION 2016+
ER/Studio Software
Architect
ER/Studio Business
Architect
ER/Studio Repository
& Team Server
ER/Studio Data
Architect

7. 4© 2016 IDERA, Inc. All rights reserved. Proprietary and confidential. 4© 2017 IDERA, Inc. All rights reserved.
DMBOK: DEFINITIONS
§ Data Governance
• The exercise of authority, control and shared decision making (planning,
monitoring and enforcement) over the management of data assets.
§ Master Data
• Synonymous with reference data. The data that provides the context for
transaction data. It includes the details (definitions and identifiers) of internal
and external objects involved in business transactions. Includes data about
customers, products, employees, vendors, and controlled domains (code
values).
§ Master Data Management
• Processes that ensure that reference data is kept up to date and coordinated
across an enterprise. The organization, management and distribution of
corporately adjudicated data with widespread use in the organization.

8. 5© 2016 IDERA, Inc. All rights reserved. Proprietary and confidential. 5© 2017 IDERA, Inc. All rights reserved.
MASTER DATA CLASSIFICATION CONSIDERATIONS
§ Behavior
§ Life Cycle
§ Complexity
§ Value
§ Volatility
§ Reuse

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MASTER DATA - BEHAVIOR
§ Can be described by the way it interacts with other data
§ Master data is almost always involved with transactional data
§ Often a noun/verb relationship between the master data item and the
transaction
• Master data are the nouns
• Customer
• Product
• Transactional data capture the verbs
• Customer places order
• Product sold on order
§ Same type of relationships are shared between facts and dimensions in a data
warehouse
• Master data are the dimensions
• Transactions are the facts

10. 7© 2016 IDERA, Inc. All rights reserved. Proprietary and confidential. 7© 2017 IDERA, Inc. All rights reserved.
MASTER DATA - LIFECYCLE
§ Describes how a master data element is created, read, updated, deleted (CRUD)
§ Many factors come into play
• Business rules
• Business processes
• Applications
§ There may be more than 1 way a particular master data element is created
§ Need to model:
• Business process
• Data lineage
• Data flow
• Integration
• Include Extract Transform and Load (ETL) for data warehouse/data marts and staging
areas

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BUSINESS PROCESS & DATA CRUD

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MASTER DATA – COMPLEXITY, VALUE
§ Complexity
• Very simple entities are rarely a challenge to manage
• The less complex an element, the less likely the need to manage change
• Likely not master data elements
• Possibly reference data
− States/Provinces
− Units of measure
− Classification references
§ Value
• Value and complexity interact
• The higher value a data element is to an organization the more likely it will be
considered master data

13. 10© 2016 IDERA, Inc. All rights reserved. Proprietary and confidential. 10© 2017 IDERA, Inc. All rights reserved.
MASTER DATA - VOLATILITY
§ Level of change in characteristics describing a master data element
• Frequent change = high volatility
• Infrequent change = low volatility
§ Sometimes referred to as stability
• Frequent change = unstable
• Infrequent change = stable

14. 11© 2016 IDERA, Inc. All rights reserved. Proprietary and confidential. 11© 2017 IDERA, Inc. All rights reserved.
MASTER DATA - REUSE
§ Master data elements are often shared across a number of systems
§ Can lead to inconsistency and errors
• Multiple systems
• Which is the “version of truth”
• Spreadsheets
• Private data stores
§ An error in master data can cause errors in
• All the transactions that use it
• All the applications that use it
• All reports and analytics that use it
§ This is one of the primary reasons for “Master Data Management”

15. 12© 2016 IDERA, Inc. All rights reserved. Proprietary and confidential. 12© 2017 IDERA, Inc. All rights reserved.
WHAT IS MASTER DATA MANAGEMENT?
§ The processes, tools and technology required to create and maintain consistent
and accurate lists of master data
§ Includes both creating and maintaining master data
§ Often requires fundamental changes in business process
§ Not just a technological problem
§ Some of the most difficult issues are more political than technical
§ Organization wide MDM may be difficult
• Many organizations begin with critical, high value elements
• Grow and expand
§ MDM is not a project
• Ongoing
• Continuous improvement

16. 13© 2016 IDERA, Inc. All rights reserved. Proprietary and confidential. 13© 2017 IDERA, Inc. All rights reserved.
MDM ACTIVITIES
§ Identify sources of master data
§ Identify the producers and
consumers of the master data
§ Collect and analyze metadata
about for your master data
§ Appoint data stewards
§ Implement a data-governance
program and council
§ Develop the master-data model
§ Choose a toolset
§ Design the infrastructure
§ Generate and test the master data
§ Modify the producing and
consuming systems
§ Be sure to incorporate versioning
and auditing

17. 14© 2016 IDERA, Inc. All rights reserved. Proprietary and confidential. 14© 2017 IDERA, Inc. All rights reserved.
IMPORTANCE OF DATA MODELS
§ Full Specification
• Logical
• Physical
§ Persistence Boundaries
• Business Data Objects
§ Descriptive metadata
• Names
• Definitions (data dictionary)
• Notes
§ Implementation characteristics
• Data types
• Keys
• Indexes
• Views
§ Business Rules
• Relationships (referential
constraints)
• Value Restrictions (constraints)
§ Security Classifications + Rules
§ Governance Metadata
• Master Data Management classes
• Data Quality classifications
• Retention policies

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DATA DICTIONARY – METADATA EXTENSIONS

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ER/STUDIO – METADATA ATTACHMENT SETUP

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UNIVERSAL MAPPINGS

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UNIVERSAL MAPPINGS
§ Ability to link “like” or related objects
• Within same model file
• Across separate model files
§ Entity/Table level
§ Attribute/Column level

22. 19© 2016 IDERA, Inc. All rights reserved. Proprietary and confidential. 19© 2017 IDERA, Inc. All rights reserved.
SUMMARY
§ Master Data Management is a critical aspect of Data Governance
§ Master Data Characteristics
• Behavior
• Lifecycle
• Complexity
• Volatility
• Reuse
§ MDM is an ongoing, continuous improvement discipline, not a project
§ Data models & metadata constitute the blueprint for data governance
§ Mapping the processes that utilize the data is imperative to defining the data life
cycle
§ Achieving data maturity is a journey

23. 20© 2016 IDERA, Inc. All rights reserved. Proprietary and confidential. 20© 2016 IDERA, Inc. All rights reserved.
THANKS!
Any questions?
You can find me at:
ron.huizenga@idera.com
@DataAviator

24. The Governance Game
Robin Bloor, PhD

25. Big Data Means Big Governance
The analytical opportunity of BIG
DATA is clear – there are already many
profitable uses
Nevertheless, all data needs to be
GOVERNED

26. The Data Governance Challenge
Data Sources
Metadata Management
Data meaning
Data compliance
Data provenance & lineage
Data cleansing
Data security
Data audit record
Data life-cycle mgt
Data Governance is a perpetual
process

27. The Growth of Compliance
u International
– GRC (Governance, Risk,
Compliance)
– ISO (standards)
u US Government:
– SOX
– GLBA
– HIPAA
– FISMA
– FERPA
u Europe
– GDPR (Data protection laws)
with variances
– New: The right to be forgotten

28. The Full Data Lake Picture
Data
Cleansing
Data
Security
Ingest
Metadata
Mgt
Real-Time
Apps
Transform &
Aggregate
Search &
Query
BI, Visual'n
& Analytics
Other
Apps
Data Lake
Mgt
Data
Governance
DATA LAKE
To
Databases
Data Marts
Other Apps
Archive
Life Cycle
Mgt Extracts
Servers, Desktops, Mobile, Network Devices, Embedded
Chips, RFID, IoT, The Cloud, Oses, VMs, Log Files, Sys
Mgt Apps, ESBs, Web Services, SaaS, Business Apps,
Office Apps, BI Apps, Workflow, Data Streams, Social...

29. The Need For Data Modeling & MDM

30. Points To Note
u The more complex the
data universe the more
you need a model.
u In theory it is a view of
the data universe. In
practice it is part of it.
u Beginning: Modeling is
top-down and bottom
up. You build in both
directions
u It is not and never can
be a project. It is an on-
going activity.

31. The Net Net
Because IT and data management is
evolving so quickly, governance and
data modeling must also evolve
quickly

32. u Agile modeling clearly requires effective
collaboration between all data users at every
level. How does your technology help with
cultural issues?
u Which data stores and databases do you
support aside form the usual relational
sources? (Hadoop, NoSQL, unstructured,
etc.)offer for NoSQL databases?
u How do you accommodate the IoT?

33. u If you do not do MDM already, how do you start
and what are the immediate business benefits?
u Do you model data flows (consider, for example,
real-time analytics)?
u Where do you see current/future competition
emerging from in the modeling or governance
market?