1.
Peter Aiken, Ph.D.
Metadata Management Strategies
10124 W. Broad Street, Suite C
Glen Allen, Virginia 23060
804.521.4056
Metadata Management Strategies
Copyright 2016 by Data Blueprint
Good systems development often depends on multiple
data management disciplines to provide a solid
foundation. One of these is metadata. While much of
the discussion focuses on understanding metadata itself
along with its associated technologies, this perspective
often represents a typical tool-and-technology focus,
which has not achieved significant results to date. A
more relevant question when considering pockets of
metadata is whether to include them in the scope of
organizational metadata practices. By understanding
what it means to include items in the scope of your
metadata practices, you can begin to build systems that
allow you to practice sophisticated ways to advance
data management and supported business initiatives
with a demonstrable ROI. After a bit of practice in this
manner you can position your organization to better
exploit any and all metadata technologies in support of
business strategy
Date: May 10, 2016
Time: 2:00 PM ET/11:00 AM PT
Presenter: Peter Aiken, Ph.D.
Data
WhereWhy
What How
Who
When
Data

2.
Executive Editor at DATAVERSITY.net
3Copyright 2016 by Data Blueprint Slide #
Shannon Kempe
Commonly Asked Questions
4Copyright 2016 by Data Blueprint Slide #
1) Will I get copies of the
slides after the event?
2) Is this being recorded?

3.
Get Social With Us!
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Like Us on Facebook
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#dataed
• 30+ years in data management
• Repeated international recognition
• Founder, Data Blueprint (datablueprint.com)
• Associate Professor of IS (vcu.edu)
• DAMA International (dama.org)
• 9 books and dozens of articles
• Experienced w/ 500+ data
management practices
• Multi-year immersions:
– US DoD (DISA/Army/Marines/DLA)
– Nokia
– Deutsche Bank
– Wells Fargo
– Walmart
– …
Peter Aiken, Ph.D.
• DAMA International President 2009-2013
• DAMA International Achievement Award 2001 (with
Dr. E. F. "Ted" Codd
• DAMA International Community Award 2005
PETER AIKEN WITH JUANITA BILLINGS
FOREWORD BY JOHN BOTTEGA
MONETIZING
DATA MANAGEMENT
Unlocking the Value in Your Organization’s
Most Important Asset.
The Case for the
Chief Data Officer
Recasting the C-Suite to Leverage
Your MostValuable Asset
Peter Aiken and
Michael Gorman
6Copyright 2016 by Data Blueprint Slide #

4.
James Q. Wilson
7
Copyright 2016 by Data Blueprint
• We are to make the best and sanest
use of (our resource) - we must first
adopt a sober view of man ('s
capabilities) … that permits
reasonable things to be
accomplished, foolish things
abandoned and utopian things
forgotten
8Copyright 2016 by Data Blueprint Slide #
Business Goals Model
Defines the mission of the
enterprise, its long-range goals,
and the business policies and
assumptions that affect its
operations.
Business Rules Model
Records rules that govern the
operation of the business and the
Business Events that trigger
execution of Business Processes.
Enterprise Structure Model
Defines the scope of the enterprise
to be modeled. Assigns a name to the
model that serves to qualify each
component of the model.
Extension Support Model
Provides for tactical Information
Model extensions to support special
tool needs.
Info Usage Model
Specifies which of the
Entity-Relationship Model
component instances are used by
other Information Model
components.
Global Text Model
Supports recording of extended
descriptive text for many of the
Information Model components.
DB2 Model
Refines the definition of a Relational
Database design to a DB2-specific
design.
IMS Structures Model
Defines the component structures
and elements and the application
program views of an IMS Database.
Flow Model
Specifies which of the Entity
Relationship Model component
instances are passed between
Process Model components.
Applications Structure Model
Defines the overall scope of an automated
Business Application, the components of the
application and how they fit together.
Data Structures Model
Defines the data structures and their
elements used in an automated
Business Application.
Application Build Model
Defines the tools, parameters and
environment required to build an
automated Business Application.
Derivations/Constraints Model
Records the rules for deriving legal
values for instances of
Entity-Relationship Model
components, and for controlling the
use or existence of E-R instance.
Entity-Relationship Model
Defines the Business Entities, their
properties (attributes) and the
relationships they have with other
Business Entities.
Organization/Location Model
Records the organization structure
and location definitions for use in
describing the enterprise.
Process Model
Defines Business Processes, their
sub processes and components.
Relational Database Model
Describes the components of a
Relational Database design in
terms common to all SAA
relational DBMSs.
Test Model
Identifies the various file (test
procedures, test cases, etc.)
affiliated with an automated
business Application for use in
testing that application.
Library Model
Records the existence of
non-repository files and the role they
play in defining and building an
automated Business Application.
Panel/Screen Model
Identifies the Panels and Screens and
the fields they contain as elements
used in an automated Business
Application.
Program Elements Model
Identifies the various pieces and
elements of application program
source that serve as input to the
application build process.
Value Domain Model
Defines the data characteristics
and allowed values for
information items.
Strategy Model
Records business strategies to
resolve problems, address goals,
and take advantage of business
opportunities. It also records
the actions and steps to be taken.Resource/Problem Model
Identifies the problems and needs
of the enterprise, the projects
designed to address those needs,
and the resources required.
Process Model
Extension
Support Model
Application
Structure
Model
DB2 Model
Relational
Database
Model
Global Text
Model
Strategy
Model
Derivations/
Constriants
Model
Application
Build Model
Test Model
Panel/ Screen
Model
IMS Structure
Model
Data
Structure
Model
Program
Elements
Model
Business
Model
Goals
Organization/
LocationModel
Resource/
Problem
Model
Enterprise
Structure
Model
Entity-
Relationship
Model
Info Usage
Model
Value Domain
Model
Flow Model
Business
Rules Model
Library
Model
IBM's AD/Cycle Information Model

5.
Whither the "data dictionary?
9
Copyright 2016 by Data Blueprint
• The classic "data dictionary" pretty much died by the early '90s
- ... they ever-so-kindly renamed "data dictionary" to "metadata repository" & then promptly
went belly up. Ask pretty much and IBMer today if they've ever heard of AD/Cycle or
RepositoryManager... guaranteed response will be a blank stare.
• My calculation says 5% survival rate from 1973 to 2003
• Metadata has morphed into a meaningless buzzword …
- Yet organizations are suffering from unprecedented amounts of new forms of seriously
unmanaged metadata.
• I've essentially given up on trying to grok what metadata is other
than "required buzzword" (Dave Eddy - deddy@davideddy.com)
Metadata Management Strategies
10
Copyright 2016 by Data Blueprint
1. Data Management Overview
2. What is metadata and why is it important?
3. Major metadata types & subject areas
4. Metadata benefits, application & sources
5. Metadata strategies & implementation
6. Metadata building blocks
7. Guiding Principles
8. Specific teachable example
9. Take Aways, References and Q&A
Tweeting now:
#dataed

6.
UsesUsesReuses
What is data management?
11Copyright 2016 by Data Blueprint Slide #
Sources
Data
Engineering
Data
Delivery
Data
Storage
Specialized Team Skills
Data Governance
Understanding the current
and future data needs of an
enterprise and making that
data effective and efficient in
supporting
business activities
Aiken, P, Allen, M. D., Parker, B., Mattia, A.,
"Measuring Data Management's Maturity:
A Community's Self-Assessment"
IEEE Computer (research feature April 2007)
Data management practices connect
data sources and uses in an
organized and efficient manner
• Engineering
• Storage
• Delivery
• Governance
When executed,
engineering, storage, and
delivery implement governance
Note: does not well-depict data reuse
What is data management?
12Copyright 2016 by Data Blueprint Slide #
Sources
Data
Engineering
Data
Delivery
Data
Storage
Specialized Team Skills
Resources
(optimized for reuse)
Data Governance
AnalyticInsight
Specialized Team Skills

7.
Data$Management$
Strategy
Data Management Goals
Corporate Culture
Data Management Funding
Data Requirements Lifecycle
Data
Governance
Governance Management
Business Glossary
Metadata Management
Data
Quality
Data Quality Framework
Data Quality Assurance
Data
Operations
Standards and Procedures
Data Sourcing
Platform$&$
Architecture
Architectural Framework
Platforms & Integration
Supporting$
Processes
Measurement & Analysis
Process Management
Process Quality Assurance
Risk Management
Configuration Management
Component Process$Areas
DMM℠ Structure of
5 Integrated
DM Practice Areas
Data architecture
implementation
Data
Governance
Data
Management
Strategy
Data
Operations
Platform
Architecture
Supporting
Processes
Maintain fit-for-purpose data,
efficiently and effectively
13Copyright 2016 by Data Blueprint Slide #
Manage data coherently
Manage data assets professionally
Data life cycle
management
Organizational support
Data
Quality
You can accomplish
Advanced Data Practices
without becoming proficient
in the Foundational Data
Management Practices
however this will:
• Take longer
• Cost more
• Deliver less
• Present
greater
risk
(with thanks to Tom DeMarco)
Data Management Practices Hierarchy
Advanced
Data
Practices
• MDM
• Mining
• Big Data
• Analytics
• Warehousing
• SOA
Foundational Data Management Practices
Data Platform/Architecture
Data Governance Data Quality
Data Operations
Data Management Strategy
Technologies
Capabilities
14Copyright 2016 by Data Blueprint Slide #

8.
DataManagementBodyofKnowledge
15
Copyright 2016 by Data Blueprint
Data
Management
Functions
Metadata Management
from The DAMA Guide to the Data Management Body of Knowledge © 2009 by DAMA International
16
Copyright 2016 by Data Blueprint

9.
Metadata Management Strategies
17
Copyright 2016 by Data Blueprint
1. Data Management Overview
2. What is metadata and why is it important?
3. Major metadata types & subject areas
4. Metadata benefits, application & sources
5. Metadata strategies & implementation
6. Metadata building blocks
7. Guiding Principles
8. Specific teachable example
9. Take Aways, References and Q&A
Tweeting now:
#dataed
What is a Strategy?
18
Copyright 2016 by Data Blueprint
• Current use derived from military
• "a pattern in a stream of decisions" [Henry Mintzberg]

10.
Meta data, Meta-data, or metadata
19
Copyright 2016 by Data Blueprint
• In the history of language, whenever two words are
pasted together to form a combined concept initially, a
hyphen links them
• With the passage of time,
the hyphen is lost. The
argument can be made
that that time has passed
• There is a copyright on
the term "metadata," but
it has not been enforced
• So, term is "metadata"
The prefix meta-
1. Situated behind: metacarpus.
2. a. Later in time: metestrus.
b. At a later stage of development: metanephros.
3. a. Change; transformation: metachromatism.
b. Alternation: metagenesis.
4. a. Beyond; transcending; more comprehensive: metalinguistics.
b. At a higher state of development: metazoan.
5. Having undergone metamorphosis: metasomatic.
6. a. Derivative or related chemical substance: metaprotein.
b. Of or relating to one of three possible isomers of a benzene ring
with two attached chemical groups, in which the carbon atoms with
attached groups are separated by one unsubstituted carbon atom:
meta-dibromobenzene.
Definition of the prefix meta- (Emphasis added – source: American Heritage English Dictionary © 1993 Houghton Mifflin).
Meta
20Copyright 2016 by Data Blueprint Slide #

11.
Definitions
21
Copyright 2016 by Data Blueprint
• Metadata is
– Everywhere in every data management activity and integral
to all IT systems and applications.
– To data what data is to real life. Data reflects real life transactions, events,
objects, relationships, etc. Metadata reflects data transactions, events,
objects, relations, etc.
– The data that describe the structure and workings of an
organization’s use of information, and which describe the
systems it uses to manage that information.
[quote from David Hay's book, page 4]
• Data describing various facets of a data asset, for the purpose
of improving its usability throughout its life cycle [Gartner 2010]
• Metadata unlocks the value of data, and therefore requires
management attention [Gartner 2011]
• Metadata Management is
– The set of processes that ensure proper creation, storage, integration, and
control to support associated use of metadata
Analogy: a library card catalog
22
Copyright 2016 by Data Blueprint
• Identifies
– What books are in the library, and
– Where they are located
• Search by
– Subject area
– Author, or
– Title
• Catalog shows
– Author
– Subject tags
– Publication date and
– Revision history
• Determine which books will
meet the reader’s requirements
• Without the catalog, finding
things is difficult, time
consuming and frustrating
from The DAMA Guide to the Data Management Body of
Knowledge © 2009 by DAMA International

12.
Definition (continued)
23
Copyright 2016 by Data Blueprint
• Metadata is the card catalog in a
managed data environment
• Abstractly, Metadata is the descriptive
tags or context on the data (the
content) in a managed data
environment
• Metadata shows business and
technical users where to find
information in data repositories
• Metadata provides details on where the
data came from, how it got there, any
transformations, and its level of quality
• Metadata provides assistance with
what the data really means and how to
interpret it
from The DAMA Guide to the Data Management Body of Knowledge © 2009 by DAMA International
Defining Metadata
24
Copyright 2016 by Data Blueprint
Metadata is any
combination of
any circle and the
data in the center
that unlocks the
value of the data!
Adapted from Brad Melton
Data
WhereWhy
What How
Who
When
Data

13.
Library Metadata Example
Libraries can operate
efficiently through careful
use of metadata (Card
Catalog)
Who: Author
What: Title
Where: Shelf Location
When: Publication Date
A small amount of
metadata (Card Catalog)
unlocks the value of a large
amount of data (the
Library)
25
Copyright 2016 by Data Blueprint
Data
WhereWhy
What How
Who
When
Library Book
Outlook Example
26
Copyright 2016 by Data Blueprint
"Outlook" metadata is used to navigate/manage email
What: "Subject"
How: "Priority"
Where: "USERID/Inbox",
"USERID/Personal"
Why: "Body"
When: "Sent" & "Received”
• Find the important stuff/weed out junk
• Organize for future access/outlook rules
• Imagine how managing e-mail (already non-trivial)
would change if Outlook did not make use of
metadata Who:"To" & "From?"

14.
Metadata Defined …
• Metadata …
• isn't
• is not a noun
• is more of a verb
• Describes a use of data -
not a type of data
• Describes the use of some attributes
of data to understand or manage the
data from a different (usually higher)
level of abstraction
27Copyright 2016 by Data Blueprint Slide #
Why Metadata Matters
28
Copyright 2016 by Data Blueprint
• They know you rang a phone sex service at 2:24 am and spoke for 18
minutes. But they don't know what you talked about.
• They know you called the suicide prevention hotline from the Golden Gate
Bridge. But the topic of the call remains a secret.
• They know you spoke with an HIV testing service, then your doctor, then
your health insurance company in the same hour. But they don't know what
was discussed.
• They know you received a call from the local NRA office while it was
having a campaign against gun legislation, and then called your senators
and congressional representatives immediately after. But the content of
those calls remains safe from government intrusion.
• They know you called a gynecologist, spoke for a half hour, and then
called the local Planned Parenthood's number later that day. But nobody
knows what you spoke about.
– https://www.eff.org/deeplinks/2013/06/why-metadata-matters

15.
Entity: BED
Data Asset Type: Principal Data Entity
Purpose: This is a substructure within the Room
substructure of the Facility Location. It contains
information about beds within rooms.
Source: Maintenance Manual for File and Table
Data (Software Version 3.0, Release 3.1)
Attributes: Bed.Description
Bed.Status
Bed.Sex.To.Be.Assigned
Bed.Reserve.Reason
Associations: >0-+ Room
Status: Validated
• A purpose statement describing why the organization is maintaining information
about this business concept;
• Sources of information about it;
• A partial list of the attributes or characteristics of the entity; and
• Associations with other data items; this one is read as "One room contains zero
or many beds."
29Copyright 2016 by Data Blueprint Slide #
A sample data entity and associated metadata
Metadata Management Strategies
30
Copyright 2016 by Data Blueprint
1. Data Management Overview
2. What is metadata and why is it important?
3. Major metadata types & subject areas
4. Metadata benefits, application & sources
5. Metadata strategies & implementation
6. Metadata building blocks
7. Guiding Principles
8. Specific teachable example
9. Take Aways, References and Q&A
Tweeting now:
#dataed

16.
Metadata Management Strategies
31
Copyright 2016 by Data Blueprint
1. Data Management Overview
2. What is metadata and why is it important?
3. Major metadata types & subject areas
4. Metadata benefits, application & sources
5. Metadata strategies & implementation
6. Metadata building blocks
7. Guiding Principles
8. Specific teachable example
9. Take Aways, References and Q&A
Tweeting now:
#dataed
Types of Metadata: Process Metadata
32
Copyright 2016 by Data Blueprint
• Process Metadata is...
– Data that defines and describes the characteristics of other system
elements, e.g. processes, business rules, programs, jobs, tools, etc.
• Examples of Process metadata:
– Data stores and data involved
– Government/regulatory bodies
– Organization owners and stakeholders
– Process dependencies
and decomposition
– Process feedback
loop and documentation
– Process name
from The DAMA Guide to the Data Management Body of Knowledge © 2009 by DAMA International

17.
Business Process Metadata
33
Copyright 2016 by Data Blueprint
Who: Created the
documentation?
What: Are the
important
dependencies
among the
processes?
How: Do the business
processes
interact with
each other?
Data
WhereWhy
What How
Who
When
Email
Messag
e
Types of Metadata: Business Metadata
34
Copyright 2016 by Data Blueprint
• Business Metadata describe
to the end user what data are
available, what they mean and
how to retrieve them.
• Included are:
– Business names and definitions of subject and concept areas,
entities, attributes
– Attribute data types and other attribute properties
– Range descriptions, calculations, algorithms and business rules
– Valid domain values and their definitions
from The DAMA Guide to the Data Management Body of Knowledge © 2009 by DAMA International

18.
Types of Metadata: Technical & Operational Metadata
35
Copyright 2016 by Data Blueprint
• Technical and operational metadata provides developers
and technical users with information about their systems
• Technical metadata includes…
– Physical database table and column names, column properties, other
properties, other database object properties and database storage
• Operational metadata is targeted at IT operations users’
needs, including…
– Information about data movement, source and target systems, batch
programs, job frequency, schedule anomalies, recovery and backup
information, archive rules and usage
• Examples of Technical & Operational metadata:
– Audit controls and balancing information
– Data archiving and retention rules
– Encoding/reference table conversions
– History of extracts and results
from The DAMA Guide to the Data Management Body of Knowledge © 2009 by DAMA International
Types of Metadata: Data Stewardship
36
Copyright 2016 by Data Blueprint
• Data stewardship metadata is about...
– Data stewards, stewardship processes, and responsibility
assignments
• Data stewards…
– Assure that data and Metadata are accurate, with high quality
across the enterprise.
– Establish and monitor data sharing.
• Examples of Data stewardship metadata:
– Business drivers/goals
– Data CRUD rules
– Data definitions – business and technical
– Data owners
– Data sharing rules and agreements/contracts
– Data stewards, roles and responsibilities
from The DAMA Guide to the Data Management Body of Knowledge © 2009 by DAMA International

19.
Types of Metadata: Provenance
37
Copyright 2016 by Data Blueprint
• Provenance:
– the history of ownership of a valued object or work of art or
literature" [Merriam Webster]
– For each datum, this is the description of:
• Its source (system or person or department),
• Any derivation used, and
• The date it was created.
– Examples of Data Provenance:
• The programs or
processes by which
it was created
• Its owner
• The steward responsible
for its quality
• Other roles and
responsibilities
• Rules for sharing it
from The DAMA Guide to the Data Management Body of Knowledge © 2009 by DAMA International
Metadata Subject Areas
Subject Areas Components
1) Business Analytics Data definitions, reports, users, usage, performance
2) Business Architecture Roles and organizations, goals and objectives
3) Business Definitions
Business terms and explanations for a particular concept,
fact, or other item found in an organization
4) Business Rules Standard calculations and derivation methods
5) Data Governance
Policies, standards, procedures, programs, roles,
organizations, stewardship assignments
6) Data Integration
Sources, targets, transformations, lineage, ETL
workflows, EAI, EII, migration/conversion
7) Data Quality Defects, metrics, ratings
8) Document Content
Management
Unstructured data, documents, taxonomies, ontologies,
name sets, legal discovery, search engine indexes
38
Copyright 2016 by Data Blueprint
from The DAMA Guide to the Data Management Body of Knowledge © 2009 by DAMA International

20.
Metadata Subject Areas, continued
Subject Areas Components
9) Information Technology
Infrastructure
Platforms, networks, configurations, licenses
10)Conceptual data models
Entities, attributes, relationships and rules, business
names and definitions.
11)Logical Data Models
Files, tables, columns, views, business definitions,
indexes, usage, performance, change management
12)Process Models
Functions, activities, roles, inputs/outputs, workflow,
timing, stores
13)Systems Portfolio and IT
Governance
Databases, applications, projects, and programs,
integration roadmap, change management
14)Service-oriented
Architecture (SOA)
information:
Components, services, messages, master data
15)System Design and
Development
Requirements, designs and test plans, impact
16)Systems Management
Data security, licenses, configuration, reliability, service
levels
39
Copyright 2016 by Data Blueprint
from The DAMA Guide to the Data Management Body of Knowledge © 2009 by DAMA International
Metadata Management Strategies
40
Copyright 2016 by Data Blueprint
1. Data Management Overview
2. What is metadata and why is it important?
3. Major metadata types & subject areas
4. Metadata benefits, application & sources
5. Metadata strategies & implementation
6. Metadata building blocks
7. Guiding Principles
8. Specific teachable example
9. Take Aways, References and Q&A
Tweeting now:
#dataed

21.
7 Metadata Benefits
41
Copyright 2016 by Data Blueprint
1. Increase the value of strategic information (e.g. data warehousing,
CRM, SCM, etc.) by providing context for the data, thus aiding
analysts in making more effective decisions.
2. Reduce training costs and lower the impact of staff turnover through
thorough documentation of data context, history, and origin.
3. Reduce data-oriented research time by assisting business analysts
in finding the information they need in a timely manner.
4. Improve communication by bridging the gap between business users
and IT professionals, leveraging work done by other teams and
increasing confidence in IT system data.
5. Increased speed of system development’s time-to-market by
reducing system development life-cycle time.
6. Reduce risk of project failure through better impact analysis at
various levels during change management.
7. Identify and reduce redundant data and processes, thereby reducing
rework and use of redundant, out-of-data, or incorrect data.
from The DAMA Guide to the Data Management Body of Knowledge © 2009 by DAMA International
Metadata for Semistructured Data
42
Copyright 2016 by Data Blueprint
• Unstructured data
– Any data that is not in a database or data file, including documents or other
media data
• Metadata describes both structured and unstructured data
• Metadata for unstructured data exists in many formats,
responding to a variety of different requirements
• Examples of Metadata repositories describing unstructured data:
– Content management applications
– University websites
– Company intranet sites
– Data archives
– Electronic journals collections
– Community resource lists
• Common method for classifying Metadata in unstructured
sources is to describe them as descriptive metadata, structural
metadata, or administrative metadata
from The DAMA Guide to the Data Management Body of Knowledge © 2009 by DAMA International

22.
Metadata for Unstructured Data: Examples
43
Copyright 2016 by Data Blueprint
• Examples of descriptive metadata:
– Catalog information
– Thesauri keyword terms
• Examples of structural metadata
– Dublin Core
– Field structures
– Format (audio/visual, booklet)
– Thesauri keyword labels
– XML schemas
• Examples of administrative metadata
– Source(s)
– Integration/update schedule
– Access rights
– Page relationships (e.g. site navigational design)
Specific Example
44
Copyright 2016 by Data Blueprint
• Four metadata
sources:
1. Existing reference
models (i.e., ADRM)
2. Conceptual model
created two years ago
3. Existing systems (to be
reverse engineered)
4. Enterprise data model
}
from The DAMA Guide to the Data Management Body of Knowledge © 2009 by DAMA International

23.
The Real Value of Metadata
45Copyright 2016 by Data Blueprint Slide #
Metadata Management Strategies
46
Copyright 2016 by Data Blueprint
1. Data Management Overview
2. What is metadata and why is it important?
3. Major metadata types & subject areas
4. Metadata benefits, application & sources
5. Metadata strategies & implementation
6. Metadata building blocks
7. Guiding Principles
8. Specific teachable example
9. Take Aways, References and Q&A
Tweeting now:
#dataed

24.
Metadata Strategy Implementation Phases
47
Copyright 2016 by Data Blueprint
Investing in Metadata?
• How can IT staff convince managers to plan,
budget, and apply resources for metadata
management?
• What is metadata
and why is it
important?
• What technologies
are involved?
• Internet and intranet
technologies are
part of the answer
and will get the
immediate attention of management.
48Copyright 2016 by Data Blueprint Slide #

25.
Metadata Strategy
49
Copyright 2016 by Data Blueprint
• Metadata Strategy is
– A statement of direction in Metadata management by the enterprise
– A statement of intend that acts as a reference framework for the development
teams
– Driven by business objectives and prioritized by the business value they bring to
the organization
• Build a Metadata strategy from a set of defined components
• Primary focus of Metadata strategy
– gain an understanding of and consensus on the organization’s key business
drivers, issues, and information requirements for the enterprise Metadata program
• Need to understand how well the current environment meets these
requirements now and in the future
• Metadata strategy objectives define the organization’s future
enterprise metadata architecture and recommend logical progression
of phased implementation steps
• Only 1 in 10 organizations has a documented, board approved data
strategy
Keep the proper focus
50
Copyright 2016 by Data Blueprint
• Wrong question:
– Is this metadata?
• Right question:
– Should we include this
data item within the
scope of our
metadata
practices?

26.
Extraction
Sources
Copyright 2013 by Data Blueprint
Organized Knowledge 'Data'
Improved Quality Data
Data Organization Practices
Metadata Practices will be inextricably intertwined with
Data Quality and Master Data and Knowledge
Management, (among other functions)
Operational Data
Data Quality
Engineering
Master Data
Management
Practices
Suspected/
Identified
Data
Quality
Problems
Routine Data Scans
Master Data Catalogs
Routine Data Scans
Knowledge
Management
Practices
Data that might benefit from
Master Management
51
System 2
System 3
System 4
System 5
System 6
System 1
Existing
Metadata Strategy - Reduce the Number of Systems
52Copyright 2016 by Data Blueprint Slide #

27.
System 2
System 3
System 4
System 5
System 6
System 1
Existing New
Transformations
Data Store
Generated
Programs
System-to-System Program
Transformation Knowledge
Transformations
Transformations
Transformations
Metadata Strategy - Reduce the Number of Systems
53Copyright 2016 by Data Blueprint Slide #
FTI Metadata Model
54Copyright 2016 by Data Blueprint Slide #

28.
Build Your Own Metadata Repository
55Copyright 2016 by Data Blueprint Slide #
Sample
Low-tech
Repository
56Copyright 2016 by Data Blueprint Slide #

29.
Copyright 2013 by Data Blueprint
57
© Copyright 2004 by Data Blueprint - all rights reserved!90 - www.datablueprint.com
MetadataMetadata EngineeringEngineering SolutionSolution
New System
Legacy System
#1: Payroll
Legacy System
#2: Personnel
Data mapping,
quality, &
transformation
Data mapping,
quality, &
transformation
Metadata Engineering Analyses
Steps Illustration
• Determine what type of metadata to derive for
specific system implementation requirements.
– Implementation needs indicate a requirement to understand the
workflow metadata by obtaining a list of all combinations of
stepname instances along with each associated component,
business process, and home pages.
58Copyright 2016 by Data Blueprint Slide #

30.
© Copyright 2004 by Data Blueprint - all rights reserved!90 - www.datablueprint.com
MetadataMetadata EngineeringEngineering SolutionSolution
New System
Legacy System
#1: Payroll
Legacy System
#2: Personnel
Data mapping,
quality, &
transformation
Data mapping,
quality, &
transformation
Metadata Engineering Analyses
Steps Illustration
59Copyright 2016 by Data Blueprint Slide #
2. Formulate a query designed
to extract specific metadata.
– Using SQL extract from the system
all unique combinations of
homepages, processes, components,
and step names resulting in a 13044
line report.
© Copyright 2004 by Data Blueprint - all rights reserved!90 - www.datablueprint.com
MetadataMetadata EngineeringEngineering SolutionSolution
New System
Legacy System
#1: Payroll
Legacy System
#2: Personnel
Data mapping,
quality, &
transformation
Data mapping,
quality, &
transformation
Metadata Engineering Analyses
Steps Illustration
60Copyright 2016 by Data Blueprint Slide #
3. Export the extracted metadata
to a spreadsheet for subsequent complexity analysis and
validation.
– Saving the query results into .xls format, permits further statistical
analysis of the metadata to determine information about metadata
relationships, complexity and occurrence frequency in order to
confirm the extracted metadata correctness.

31.
Example Query Outputs
61Copyright 2016 by Data Blueprint Slide #
Bibiana Duet's
© Copyright 2004 by Data Blueprint - all rights reserved!90 - www.datablueprint.com
MetadataMetadata EngineeringEngineering SolutionSolution
New System
Legacy System
#1: Payroll
Legacy System
#2: Personnel
Data mapping,
quality, &
transformation
Data mapping,
quality, &
transformation
Metadata Engineering Analyses
Steps Illustration
62Copyright 2016 by Data Blueprint Slide #
4. Import the validated metadata
into Repository
– Once validated, the
process metadata
is moved into the
MS-Access
database as a
new stand-alone
table.

32.
© Copyright 2004 by Data Blueprint - all rights reserved!90 - www.datablueprint.com
MetadataMetadata EngineeringEngineering SolutionSolution
New System
Legacy System
#1: Payroll
Legacy System
#2: Personnel
Data mapping,
quality, &
transformation
Data mapping,
quality, &
transformation
Metadata Engineering Analyses
Steps Illustration
63Copyright 2016 by Data Blueprint Slide #
5. Integrate the new metadata with the existing metadata.
– The new table is formally associated with the existing metadata,
linking processes to menugroups via
homepages and
stepnames to
menubars and
panels via
menuitems.
© Copyright 2004 by Data Blueprint - all rights reserved!90 - www.datablueprint.com
MetadataMetadata EngineeringEngineering SolutionSolution
New System
Legacy System
#1: Payroll
Legacy System
#2: Personnel
Data mapping,
quality, &
transformation
Data mapping,
quality, &
transformation
Metadata Engineering Analyses
Steps Illustration
64Copyright 2016 by Data Blueprint Slide #
6. Provide the resulting, richer metadata to the requesting
user, verifying the metadata and its utility addressing the
implementation need.
– Enhance Repository reporting capabilities, publish the next version,
and work directly with the user group requesting the metadata in
order ensure that the metadata is accurate and that it meet their
needs.

33.
© Copyright 2004 by Data Blueprint - all rights reserved!90 - www.datablueprint.com
MetadataMetadata EngineeringEngineering SolutionSolution
New System
Legacy System
#1: Payroll
Legacy System
#2: Personnel
Data mapping,
quality, &
transformation
Data mapping,
quality, &
transformation
Metadata Engineering Analyses
Steps Illustration
65Copyright 2016 by Data Blueprint Slide #
• Last step usually resulted in additional
requests for metadata
• Cycle begins again
• Extraction was repeated with many analysis variations
– changing the source of the analysis inputs to include different
• system objects
• system documentation
• metadata
• Report on associations among any set of systems objects
• Define and document metadata and relate these to other
metadata
Metadata Uses: Requirements
• Systematically determine the requirements that the
PeopleSoft enterprise software could meet
• Document discrepancies between system
capabilities and organizational needs
• Panels presented to users in JAD-like sessions that
were organized using system structure metadata
• Functional users determined and certified the
overall system functionality
• Associating requirements with components
• Discrepancies were noted for subsequent
investigation and resolution
66Copyright 2016 by Data Blueprint Slide #

34.
Metadata Uses: System Changes ...
• Evaluated proposed system changes, modifications,
and enhancements
• Metadata types used to assess the magnitude of
proposed changes
• For example: what are number of panels requiring
modification if a given field length was doubled?
• Analyze the costs of changing the system versus
changing the organizational processes
67Copyright 2016 by Data Blueprint Slide #
Metadata Uses: Practice Analysis
• Identify gaps between the DP&T/DOA business
requirements and PeopleSoft
• Process components were mapped to user activities
and workgroup practices
• Users focused their attention on relevant portions of the
system
• For example, the payroll clerks accessed the metadata
to determine which panels 'belonged' to them.
68Copyright 2016 by Data Blueprint Slide #

35.
Metadata Uses: Realignment
• Realignment addressed gaps between functionality and
existing work practices
• Once users understood the system’s functionality and
navigate through process component steps
• Compared the system’s inputs and outputs with their own
information needs
• If gaps existed, metadata used to assess the relative
magnitude of proposed changes
• Forecast system customization costs
• Evidence for changing the business practice instead of the
system
69Copyright 2016 by Data Blueprint Slide #
Metadata Uses: Training
• Training specialists used mappings to determine
relevant combinations of panels, menuitems, and
menubars
• Display panels in the sequence expected by the system
users
• Users were able to swiftly become familiar with their
'areas'
• Screen session recording and playback capabilities
70Copyright 2016 by Data Blueprint Slide #

36.
Metadata Uses: Additional Metadata
• Metadata describing LS1 & LS2
• Metadata supporting data conversion
– initial motivation for the metadata development
– each decision to convert a data item was recorded, permitting the
tracking of the number of data items that had been mapped,
converted, and to what they had been converted
• Associations with system batch reporting programs called
SQRs
• User and user type metadata
71Copyright 2016 by Data Blueprint Slide #
Metadata Uses: Database Design
• CASE tool integrated to extract the database design
information directly from the physical database
• Integrated into TheMAT
• Decomposition of the physical database into logical user
views
• Document how user requirements were implemented by
the system
• Planning security access levels and privileges
72Copyright 2016 by Data Blueprint Slide #

37.
Metadata Uses: Statistical Analysis
• Guiding metadata-based data integration from the two
legacy systems
• For example, the ERD information was used to map the
legacy system data into PeopleSoft data structures
• Statistical summaries described the new system to
users
73Copyright 2016 by Data Blueprint Slide #
0 500 1000 1500 2000 2500
Manage Positions(2%)
Plan Careers (~5%)
AdministerTraining (~5%)
Plan Successions(~5%
Manage Competencies(20%)
Recruit Workforce (62%)
DevelopWorkforce(29.9%)
AdministerWorkforce(28.8%)
CompensateEmployees(23.7%)
MonitorWorkplace(8.1%)
DefineBusiness(4.4%)
TargetSystem (3.9%)
EDI Manager (.9%)
TargetSystem Tools(.3%)
Administer Workforce
Metadata Uses
74Copyright 2016 by Data Blueprint Slide #

38.
Comparing Mapping Approaches
75Copyright 2016 by Data Blueprint Slide #
Legacy System New System
Analysis is unfocused
(many to many)
(one to many)(many to one)
Analysis is structured with the form of the problem
dictating the form of the solution
Marco & Jennings's Complete Meta Data Model
76Copyright 2016 by Data Blueprint Slide #
Source:http://dmreview.com/article_sub.cfm?articleID=1000941 used with permission

39.
Metadata Management Strategies
77
Copyright 2016 by Data Blueprint
1. Data Management Overview
2. What is metadata and why is it important?
3. Major metadata types & subject areas
4. Metadata benefits, application & sources
5. Metadata strategies & implementation
6. Metadata building blocks
7. Guiding Principles
8. Specific teachable example
9. Take Aways, References and Q&A
Tweeting now:
#dataed
Goals and Principles
78
Copyright 2016 by Data Blueprint
from The DAMA Guide to the Data Management Body of Knowledge © 2009 by DAMA International
• Provide organizational
understanding of terms
and usage
• Integrate Metadata from
diverse sources
• Provide easy, integrated
access to metadata
• Ensure Metadata quality
and security

40.
Activities
79
Copyright 2016 by Data Blueprint
• Understand Metadata requirements
• Define the Metadata architecture
• Develop and maintain Metadata
standards
• Implement a managed Metadata
environment
• Create and maintain metadata
• Integrate metadata
• Management Metadata repositories
• Distribute and deliver metadata
• Query, report and analyze metadata
from The DAMA Guide to the Data Management Body of Knowledge © 2009 by DAMA International
Activities: Metadata Standards Types
80
Copyright 2016 by Data Blueprint
• Two major types:
– Industry or
consensus
standards
– International
standards
• High level
framework can
show
– How standards are
related
– How they rely on
each other for
context and usage
from The DAMA Guide to the Data Management Body of Knowledge © 2009 by DAMA International

41.
Activities: Noteworthy Metadata Standards Types
Warehouse Process Warehouse Operation
Transformation OLAP
Data
Mining
Information
Visualization
Business
Nomenclature
Object
Model
Relational Record Multidimensional XML
Business
Information
Data Types Expression
Keys and
Indexes
Type
Mapping
Software
Deployment
Object Model
Management
Analysis
Resource
Foundation
81
Copyright 2016 by Data Blueprint
• Common Warehouse Metadata (CWM):
• Specifies the interchange of Metadata among data
warehousing, BI, KM, and portal technologies.
• Based on UML and depends on it to represent object-
oriented data constructs.
• The CWM Metamodel
Information Management Metamodel (IMM)
82
Copyright 2016 by Data Blueprint
• Object Management
Group Project to replace
CWM
• Concerned with:
– Business Modeling
• Entity/relationship metamodel
– Technology modeling
• Relational Databases
• XML
• LDAP
– Model Management
• Traceability
– Compatibility with related
models
• Semantics of business
vocabulary and business rules
• Ontology Definition Metamodel
• Based on Core model
• Used to translate from
one model to another

42.
Primary Deliverables
83
Copyright 2016 by Data Blueprint
• Metadata repositories
• Quality metadata
• Metadata analysis
• Data lineage
• Change impact analysis
• Metadata control procedures
• Metadata models and architecture
• Metadata management operational analysis
from The DAMA Guide to the Data Management Body of Knowledge © 2009 by DAMA International
Roles and Responsibilities
84
Copyright 2016 by Data Blueprint
• Suppliers:
– Data Stewards
– Data Architects
– Data Modelers
– Database Administrators
– Other Data Professionals
– Data Brokers
– Government and Industry Regulators
• Participants:
– Metadata Specialists
– Data Integration Architects
– Data Stewards
– Data Architects and Modelers
– Database Administrators
– Other DM Professionals
– Other IT Professionals
– DM Executives
– Business Users
• Consumers:
– Data Stewards
– Data Professionals
– Other IT Professionals
– Knowledge Workers
– Managers and Executives
– Customers and Collaborators
– Business Users
from The DAMA Guide to the Data Management Body of Knowledge © 2009 by DAMA International

43.
Technology
85
Copyright 2016 by Data Blueprint
• Metadata repositories
• Data modeling tools
• Database management systems
• Data integration tools
• Business intelligence tools
• System management tools
• Object modeling tools
• Process modeling tools
• Report generating tools
• Data quality tools
• Data development and administration tools
• Reference and mater data management tools
from The DAMA Guide to the Data Management Body of Knowledge © 2009 by DAMA International
Metadata Management Strategies
86
Copyright 2016 by Data Blueprint
1. Data Management Overview
2. What is metadata and why is it important?
3. Major metadata types & subject areas
4. Metadata benefits, application & sources
5. Metadata strategies & implementation
6. Metadata building blocks
7. Guiding Principles
8. Specific teachable example
9. Take Aways, References and Q&A
Tweeting now:
#dataed

44.
15 Guiding Principles
87
Copyright 2016 by Data Blueprint
1. Establish and maintain a Metadata strategy and
appropriate policies, especially clear goals and
objectives for Metadata management and usage
2. Secure sustained commitment, funding, and vocal support from
senior management concerning Metadata management for the
enterprise
3. Take an enterprise perspective to ensure future extensibility, but
implement through iterative and incremental delivery
4. Develop a Metadata strategy before evaluating, purchasing, and
installing Metadata management products
5. Create or adopt Metadata standards to ensure interoperability of
Metadata across the enterprise
6. Ensure effective Metadata acquisition for internal and external
metadata
7. Maximize user access since a solution that is not accessed or is
under-accessed will not show business value
from The DAMA Guide to the Data Management Body of Knowledge © 2009 by DAMA International
15 Guiding Principles, continued
88
Copyright 2016 by Data Blueprint
8. Understand and communicate the necessity of Metadata and
the purpose of each type of metadata; socialization of the
value of Metadata will encourage business usage
9. Measure content and usage
10.Leverage XML, messaging and web services
11.Establish and maintain enterprise-wide business involvement
in data stewardship, assigning accountability for metadata
12.Define and monitor procedures and processes to ensure
correct policy implementation
13.Include a focus on roles, staffing,
standards, procedures, training, & metrics
14.Provide dedicated Metadata experts
to the project and beyond
15.Certify Metadata quality
from The DAMA Guide to the Data Management Body of Knowledge © 2009 by DAMA International

45.
Who is
Joan Smith?
89Copyright 2016 by Data Blueprint Slide #
© Copyright 06/05/10 by Data Blueprint - all rights reserved!PMS4- - datablueprint.com
Select an Attribute to
get a list of values
Double-click a value to
see rows with that value

46.
Metadata Management Strategies
91
Copyright 2016 by Data Blueprint
1. Data Management Overview
2. What is metadata and why is it important?
3. Major metadata types & subject areas
4. Metadata benefits, application & sources
5. Metadata strategies & implementation
6. Metadata building blocks
7. Guiding Principles
8. Specific teachable example
9. Take Aways, References and Q&A
Tweeting now:
#dataed
Example: iTunes Metadata
• Example:
– iTunes
Metadata
• Insert a recently
purchased CD
• iTunes can:
– Count the
number of
tracks (25)
– Determine the
length of each
track
92Copyright 2016 by Data Blueprint Slide #

47.
Example: iTunes Metadata
• When connected to the
Internet iTunes connects to
the Gracenote(.com) Media
Database and retrieves:
– CD Name
– Artist
– Track Names
– Genre
– Artwork
• Sure would be a pain to
type in all this information
93Copyright 2016 by Data Blueprint Slide #
Example: iTunes Metadata
• To organize iTunes
– I create a "New Smart Playlist" for
Artist's containing "Miles Davis"
94Copyright 2016 by Data Blueprint Slide #

48.
Example: iTunes Metadata
• Notice I didn't get the desired results
• I already had another Miles Davis recording in iTunes
• Must fine-tune the request to get the desired results
– Album contains "The complete birth of the cool"
• Now I can move the playlist "Miles Davis" to a folder
95Copyright 2016 by Data Blueprint Slide #
• The same:
–Interface
–Processing
–Data Structures
• are applied to
–Podcasts
–Movies
–Books
–.pdf files
• Economies of scale
are enormous
Example: iTunes Metadata
96Copyright 2016 by Data Blueprint Slide #

49.
Metadata Management Strategies
97
Copyright 2016 by Data Blueprint
1. Data Management Overview
2. What is metadata and why is it important?
3. Major metadata types & subject areas
4. Metadata benefits, application & sources
5. Metadata strategies & implementation
6. Metadata building blocks
7. Guiding Principles
8. Specific teachable example
9. Take Aways, References and Q&A
Tweeting now:
#dataed
Uses
Metadata Take Aways
98
Copyright 2016 by Data Blueprint
• Metadata unlocks the value of data, and therefore requires
management attention [Gartner 2011]
• Metadata is the language of data governance
• Metadata defines the essence of integration challenges
Sources
Metadata Governance
Metadata
Engineering
Metadata
Delivery
Metadata Practices
Metadata
Storage
Specialized Team Skills

50.
DataManagement
BodyofKnowledge
99
Copyright 2016 by Data Blueprint
Data
Management
Functions
Metadata Management Summary
from The DAMA Guide to the Data Management Body of Knowledge © 2009 by DAMA International
100
Copyright 2016 by Data Blueprint

51.
101Copyright 2016 by Data Blueprint Slide #
Business Goals Model
Defines the mission of the
enterprise, its long-range goals,
and the business policies and
assumptions that affect its
operations.
Business Rules Model
Records rules that govern the
operation of the business and the
Business Events that trigger
execution of Business Processes.
Enterprise Structure Model
Defines the scope of the enterprise
to be modeled. Assigns a name to the
model that serves to qualify each
component of the model.
Extension Support Model
Provides for tactical Information
Model extensions to support special
tool needs.
Info Usage Model
Specifies which of the
Entity-Relationship Model
component instances are used by
other Information Model
components.
Global Text Model
Supports recording of extended
descriptive text for many of the
Information Model components.
DB2 Model
Refines the definition of a Relational
Database design to a DB2-specific
design.
IMS Structures Model
Defines the component structures
and elements and the application
program views of an IMS Database.
Flow Model
Specifies which of the Entity
Relationship Model component
instances are passed between
Process Model components.
Applications Structure Model
Defines the overall scope of an automated
Business Application, the components of the
application and how they fit together.
Data Structures Model
Defines the data structures and their
elements used in an automated
Business Application.
Application Build Model
Defines the tools, parameters and
environment required to build an
automated Business Application.
Derivations/Constraints Model
Records the rules for deriving legal
values for instances of
Entity-Relationship Model
components, and for controlling the
use or existence of E-R instance.
Entity-Relationship Model
Defines the Business Entities, their
properties (attributes) and the
relationships they have with other
Business Entities.
Organization/Location Model
Records the organization structure
and location definitions for use in
describing the enterprise.
Process Model
Defines Business Processes, their
sub processes and components.
Relational Database Model
Describes the components of a
Relational Database design in
terms common to all SAA
relational DBMSs.
Test Model
Identifies the various file (test
procedures, test cases, etc.)
affiliated with an automated
business Application for use in
testing that application.
Library Model
Records the existence of
non-repository files and the role they
play in defining and building an
automated Business Application.
Panel/Screen Model
Identifies the Panels and Screens and
the fields they contain as elements
used in an automated Business
Application.
Program Elements Model
Identifies the various pieces and
elements of application program
source that serve as input to the
application build process.
Value Domain Model
Defines the data characteristics
and allowed values for
information items.
Strategy Model
Records business strategies to
resolve problems, address goals,
and take advantage of business
opportunities. It also records
the actions and steps to be taken.Resource/Problem Model
Identifies the problems and needs
of the enterprise, the projects
designed to address those needs,
and the resources required.
Process Model
Extension
Support Model
Application
Structure
Model
DB2 Model
Relational
Database
Model
Global Text
Model
Strategy
Model
Derivations/
Constriants
Model
Application
Build Model
Test Model
Panel/ Screen
Model
IMS Structure
Model
Data
Structure
Model
Program
Elements
Model
Business
Model
Goals
Organization/
LocationModel
Resource/
Problem
Model
Enterprise
Structure
Model
Entity-
Relationship
Model
Info Usage
Model
Value Domain
Model
Flow Model
Business
Rules Model
Library
Model
IBM's AD/Cycle Information Model
References & Recommended Reading
102
Copyright 2016 by Data Blueprint
from The DAMA Guide to the Data Management Body of Knowledge © 2009 by DAMA International

52.
References, cont’d
103
Copyright 2016 by Data Blueprint
from The DAMA Guide to the Data Management Body of Knowledge © 2009 by DAMA International
References, cont’d
104
Copyright 2016 by Data Blueprint
from The DAMA Guide to the Data Management Body of Knowledge © 2009 by DAMA International

53.
References, cont’d
105
Copyright 2016 by Data Blueprint
from The DAMA Guide to the Data Management Body of Knowledge © 2009 by DAMA International
Questions?
It’s your turn!
Use the chat feature or Twitter (#dataed) to submit
your questions to Peter now.
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Copyright 2016 by Data Blueprint

54.
Upcoming Events
107
Copyright 2016 by Data Blueprint
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of the business and IT to achieve a shared understanding of
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