Metadata Strategies - Data Squared

Peter Aiken, Ph.D.
Metadata
1
• DAMA International President 2009-2013
• DAMA International Achievement Award 2001 (with
Dr. E. F. "Ted" Codd
• DAMA International Community Award 2005
Peter Aiken, Ph.D.
• 33+ years in data management
• Repeated international recognition
• Founder, Data Blueprint (datablueprint.com)
• Associate Professor of IS (vcu.edu)
• DAMA International (dama.org)
• 10 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 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
2Copyright 2017 by Data Blueprint Slide #

1. In the context of data management
2. What is it and why is it important?
3. Major types & subject areas
4. Benefits, application & sources
5. Implementation considerations
6. Guiding principles & building blocks
7. Specific teachable example
8. Take Aways, References and Q&A
Metadata
 
 
 
UsesUsesReuses
What is data management?
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?
Sources
 
Data
Engineering
 
Data  
Delivery
 
Data 
Storage
 
Resources 
(optimized for reuse) 
Data Governance
AnalyticInsight
You can accomplish
Advanced Data Practices
without becoming proficient
in the Foundational Data
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 Practices
Data Platform/Architecture
Data Governance Data Quality
Data Operations
Data Management Strategy
Technologies
Capabilities
Copyright 2017 by Data Blueprint Slide # 6

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
Manage data coherently
Manage data assets professionally
Data life cycle
management
Organizational support
Data  
Quality
Data Management Strategy is often the weakest link
Data architecture
implementation
Data  
Governance
Data  
Management 
Strategy
Data  
Operations
Platform 
Architecture
Supporting 
Processes
Maintain fit-for-purpose data,
efficiently and effectively
Manage data coherently
Manage data assets professionally
Data life cycle
management
Organizational support
Data  
Quality
3 3
33
1

DataManagement 
BodyofKnowledge(DMBoK)
DataManagement 
BodyofKnowledge(DMBoKV2)

Metadata Management
from The DAMA Guide to the Data Management Body of Knowledge © 2009 by DAMA International
Metadata

Meta data, Meta-data, or metadata
• 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, the term is "metadata"
Data
About
Data

UsesSources  
Metadata Governance
 
Metadata
Engineering
 
Metadata
Delivery
Metadata 
Storage
Metadata Practices
• If metadata is data then what technologies and techniques
should we use to manage it?
• Data management technologies and techniques
• Your organization's networking group allocates the
responsibility for knowing:
– All the devices permitted to logon to your network
– Locations of  
all permitted  
access points
• This responsibility  
belongs to a  
named  
individual(s)
Tracking network users and access points 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
4. a. Beyond; transcending; more comprehensive: metalinguistics.  
b. At a higher state of development: metazoan.
Definitions
• 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
• 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

Definition (continued)
• 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

Defining Metadata
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
Data
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)
Library Book
WhereWhy
What How
Who
When

Outlook Example
"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?"
Why Metadata Matters
• 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

Metadata
Typically Managed Architectures
• Process Architecture
– Arrangement of inputs -> transformations = value -> outputs
– Typical elements: Functions, activities, workflow, events, cycles, products, procedures
• Systems Architecture
– Applications, software components, interfaces, projects
• Business Architecture
– Goals, strategies, roles, organizational structure, location(s)
• Security Architecture
– Arrangement of security controls relation to IT Architecture
• Technical Architecture/Tarchitecture
– Relation of software capabilities/technology stack
– Structure of the technology infrastructure of an enterprise, solution or system
– Typical elements: Networks, hardware, software platforms, standards/protocols
• Data/Information Architecture
– Arrangement of data assets supporting organizational strategy
– Typical elements: specifications expressed as entities, relationships, attributes,
definitions, values, vocabularies

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
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

Classification
Names
Model
Names
*Horizontal integration lines
are shown for example purposes
only and are not a complete set.
Composite, integrative rela-
tionships connecting every cell
horizontally potentially exist.
Audience
Perspectives
Enterprise
Names
Classification
Names
Audience
Perspectives
© 1987-2011 John A. Zachman, all rights reserved. Zachman® and Zachman International® are registered trademarks of John A. Zachman
™
C o m p o s i t e I n t e g r a t i o n s
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Version 3.0
A l i g n m e n t
A l i g n m e n t
How Where Who WhenWhat Why
Process
Flows
Distribution
Networks
Responsibility
Assignments
Timing
Cycles
Inventory
Sets
Motivation
Intentions
Operations
Instances
(Implementations)
The
Enterprise
The
Enterprise
Enterprise
Perspective
(Users)
Executive
Perspective
(Business Context
Planners)
Business Mgmt
Perspective
(Business Concept
Owners)
Architect
Perspective
(Business Logic
Designers)
Engineer
Perspective
(Business Physics
Builders)
Technician
Perspective
(Business Component
Implementers)
Scope
Contexts
(Scope Identification
Lists)
Business
Concepts
(Business Definition
Models)
System
Logic
(System
Representation Models)
Technology
Physics
(Technology
Specification Models)
Tool
Components
(Tool Configuration
Models)
e.g. e.g. e.g. e.g. e.g. e.g.
e.g.: primitive e.g.: composite model:
model:
Forecast Sales
Plan Production
Sell Products
Take Orders
Train Employees
Assign Territories
Develop Markets
Maintain Facilities
Repair Products
Record Transctns
Material Supply Ntwk
Product Dist. Ntwk
Voice Comm. Ntwk
Data Comm. Ntwk
Manu. Process Ntwk
Office Wrk Flow Ntwk
Parts Dist. Ntwk
Personnel Dist. Ntwk
etc., etc.
General Mgmt
Product Mgmt
Engineering Design
Manu. Engineering
Accounting
Finance
Transportation
Distribution
Marketing
Sales
Product Cycle
Market Cycle
Planning Cycle
Order Cycle
Employee Cycle
Maint. Cycle
Production Cycle
Sales Cycle
Economic Cycle
Accounting Cycle
Products
Product Types
Warehouses
Parts Bins
Customers
Territories
Orders
Employees
Vehicles
Accounts
New Markets
Revenue Growth
Expns Reduction
Cust Convenience
Customer Satis.
Regulatory Comp.
New Capital
Social Contribution
Increased Yield
Increased Qualitye.g. e.g. e.g. e.g. e.g. e.g.
Operations Transforms
Operations In/Outputs
Operations Locations
Operations Connections
Operations Roles
Operations Work Products
Operations Intervals
Operations Moments
Operations Entities
Operations Relationships
Operations Ends
Operations Means
Process
Instantiations
Distribution
Instantiations
Responsibility
Instantiations
Timing
Instantiations
Inventory
Instantiations
Motivation
Instantiations
List: Timing Types
Business Interval
Business Moment
List: Responsibility Types
Business Role
Business Work Product
List: Distribution Types
Business Location
Business Connection
List: Process Types
Business Transform
Business Input/Output
System Transform
System Input /Output
System Location
System Connection
System Role
System Work Product
System Interval
System Moment
Technology Transform
Technology Input /Output
Technology Location
Technology Connection
Technology Role
Technology Work Product
Technology Interval
Technology Moment
Tool Transform
Tool Input /Output
Tool Location
Tool Connection
Tool Role
Tool Work Product
Tool Interval
Tool Moment
List: Inventory Types
Business Entity
Business Relationship
System Entity
System Relationship
Technology Entity
Technology Relationship
Tool Entity
Tool Relationship
List: Motivation Types
Business End
Business Means
System End
System Means
Technology End
Technology Means
Tool End
Tool Means
Timing IdentificationResponsibility IdentificationDistribution IdentificationProcess Identification
Timing DefinitionResponsibility DefinitionDistribution DefinitionProcess Definition
Process Representation Distribution Representation Responsibility Representation Timing Representation
Process Specification Distribution Specification Responsibility Specification Timing Specification
Inventory Identification
Inventory Definition
Inventory Representation
Inventory Specification
Inventory Configuration Process Configuration Distribution Configuration Responsibility Configuration Timing Configuration
Motivation Identification
Motivation Definition
Motivation Representation
Motivation Specification
Motivation Configuration
Metadata

7 Metadata Benefits
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.
Metadata for Semistructured Data
• 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

Metadata for Unstructured Data: Examples
• 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)
EnveraBusinessValue

The Real Value of Metadata
Specific Example
• 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
}

Metadata
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.

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
components, and for controlling the
use or existence of E-R instance.
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
Roles and Responsibilities
• 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

Metadata …
• Isn't
– Is not a noun
– One persons data is another's metadata
• Is more of a verb?
– Represents a use of existing facts, rather than a type of data itself
• A gerund
– a form that is derived from  
a verb but that functions as a noun
– e.g., asking in do you mind my asking you?
– Describes a use of data - not a type of data
• Describes the use of some attributes  
of data to understand or manage that  
same data from a different  
(usually higher) level of abstraction
• Value proposition
– Is this data worth including within the scope of our metadata practices?
Keep the proper focus
• Wrong question:
– Is this metadata?
• Right question:
– Should we include this  
data item within the  
scope of our  
metadata  
practices?

Definition of Bed
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
Purpose statement incorporates motivations
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."
Draft

MetadataImplementationPhases
F o r 1 m a n a g e a b l e b u s i n e s s p r o b l e m !
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

Technology
• 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
Whither the "data dictionary?
• 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
– (Dave Eddy - deddy@davideddy.com)

Build Your Own Metadata Repository
Sample
Low-tech
Repository

FTI Metadata Model

Metadata
Goals and Principles
• Provide organizational
understanding of terms
and usage
• Integrate metadata from
diverse sources
• Provide easy, integrated
access to metadata
• Ensure metadata quality
and security

Activities
• Understand metadata requirements
• Define the metadata architecture
• Develop and maintain standards
• Implement a managed environment
• Create and maintain metadata
• Integrate metadata
• Management metadata repository-like functionality
• Distribute and deliver metadata
• Query, report and analyze metadata
Activities: Metadata Standards Types
• 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

Primary Deliverables
• Metadata repository-like  
functionality
• Quality metadata
• Metadata analysis
• Data lineage
• Change impact analysis
• Metadata control procedures
• Metadata models and architecture
• Metadata management operational analysis
15 Guiding Principles
1. Establish and maintain a clear approach, policies,  
and goals for metadata management and usage
2. Secure sustained commitment, funding, and vocal support
from senior management
3. Take an enterprise perspective to ensure future extensibility,
but implement through iterative and incremental delivery
4. Develop your people and process before evaluating,
purchasing, and installing technologies
5. Create or adopt standards to ensure enterprise
interoperability
6. Ensure effective acquisition approaches for both internal
and external metadata
7. Maximize user access since a solution that is not accessed
or is under-accessed will not show business value

15 Guiding Principles, continued
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
Metadata

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
• 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

• To organize iTunes
– I create a "New Smart Playlist" for
Artist's containing "Miles Davis"
• 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

• The same:
–Interface
–Processing
–Data Structures
• are applied to
–Podcasts
–Movies
–Books
–.pdf files
• Economies of scale
are enormous
Metadata

Metadata Take Aways
• 'Data about data'
• Metadata unlocks the value of data, and therefore requires
management attention [Gartner 2011]
• Metadata is less about what and more about how
• Metadata is the language of data governance
• Metadata defines the essence of integration challenges
UsesSources  
Metadata Governance
 
Metadata
Engineering
 
Metadata
Delivery
Metadata Practices
Metadata 
Storage
References & Recommended Reading

References, cont’d

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Metadata Strategies - Data Squared

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