Data Systems Integration & Business Value Pt. 1: Metadata

Copyright 2013 by Data Blueprint
Data Systems Integration & Business Value Part 1: Metadata
Certain systems are more data focused than others. Usually their
primary focus is on accomplishing integration of disparate data. In
these cases, failure is most often attributable to the adoption of a
single pillar (silver bullet). The three webinars in the Data Systems
Integration and Business Value series are designed to illustrate that
good systems development more often depends on at least three
DM disciplines (pie wedges) in order to provide a solid foundation.
Much of the discussion of metadata focuses on understanding it and
the associated technologies. While these are important, they
represent a typical tool/technology focus and this has not achieved
significant results to date. A more relevant question when
considering pockets of metadata is: Whether to include them in the
scope 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 their data management and supported
business initiatives. After a bit of practice in this manner
you can position your organization to better exploit any
and all metadata technologies.
Date: July 9, 2013
Time: 2:00 PM ET/11:00 AM PT
Presenter: Peter Aiken, Ph.D.
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Peter Aiken, PhD
• 25+ years of experience in data
management
• Multiple international awards &
recognition
• Founder, Data Blueprint (datablueprint.com)
• Associate Professor of IS, VCU (vcu.edu)
• President, DAMA International (dama.org)
• 8 books and dozens of articles
• Experienced w/ 500+ data
management practices in 20 countries
• Multi-year immersions with
organizations as diverse as the
US DoD, Nokia, Deutsche Bank,
Wells Fargo, and the Commonwealth
of Virginia

Data Systems Integration & Business
Value Part 1: Metadata
Presented by Peter Aiken, Ph.D.
10124 W. Broad Street, Suite C
Glen Allen, Virginia 23060
804.521.4056

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
Outline
9. Take Aways, References and Q&A
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Data Program
Coordination
Feedback
Data
Development
Standard
Data
Five Integrated DM Practice Areas
Organizational Strategies
Goals
Business
Data
Business Value
Application
Models &
Designs
Implementation
Direction
Guidance
7
Organizational
Data Integration
Data
Stewardship
Data Support
Operations
Data
Asset Use
Integrated
Models
Leverage data in organizational activities
Data management
processes and
infrastructure
Combining multiple
assets to produce
extra value
Organizational-entity
subject area data
integration
Provide reliable data
access
Achieve sharing of data within a
business area

Five Integrated DM Practice Areas
Manage data coherently.
Share data across boundaries.
Assign responsibilities for data.
Engineer data delivery systems.
Maintain data availability.
Data Program
Coordination
Organizational Data
Integration
Data Stewardship Data Development
Data Support
Operations
8

• 5 Data management
practices areas / data
management
basics ...
• ... are necessary but
insufficient
prerequisites to
organizational data
leveraging
applications that is
self actualizing data or
advanced data
practices
Hierarchy of Data Management Practices (after Maslow)
Basic Data Management Practices
– Data Program Management
– Organizational Data Integration
– Data Stewardship
– Data Development
– Data Support Operations
http://3.bp.blogspot.com/-ptl-9mAieuQ/T-idBt1YFmI/AAAAAAAABgw/Ib-nVkMmMEQ/s1600/maslows_hierarchy_of_needs.png
Advanced
Data
Practices
• Cloud
• MDM
• Mining
• Big Data
• Analytics
• Warehousing
• SOA
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Data Management
Body of
Knowledge
10
Data
Management
Functions

• Data Management Body of Knowledge
(DMBOK)
– Published by DAMA International, the
professional association for
Data Managers (40 chapters worldwide)
– Organized around primary data management
functions focused around data delivery to the
organization and several environmental
elements
• Certified Data Management
Professional (CDMP)
– Series of 3 exams by DAMA International and
ICCP
– Membership in a distinct group of
fellow professionals
– Recognition for specialized knowledge in a
choice of 17 specialty areas
– For more information, please visit:
• www.dama.org, www.iccp.org
DAMA DM BoK & CDMP
11

Metadata Management
from The DAMA Guide to the Data Management Body of Knowledge © 2009 by DAMA International
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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, term is "metadata"
15

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 new 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
16

Analogy: Card catalog in a library
• Card catalog identifies what books
are stored in the library and where
they are located in the building
• Users can search for books by
subject area, author, or title
• Catalog shows author, subject tags, publication date and
revision history of each book
• Card catalog information helps determine which books will
meet the reader’s needs
• Without this catalog resource, finding books in the library
would be difficult, time consuming and frustrating
• Readers may search many incorrect books before finding
the right book if a catalog does not exist
17

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
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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
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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 Metadata Example
Libraries can operate efficiently through careful
use of metadata (Card Catalog)
20
Data
WhereWhy
What How
Who
When
Library
Book

Outlook Example
"Outlook" metadata is
used to navigate and
manage email
Imagine how
managing e-mail
(already non-trivial)
would change if
Outlook did not make
use of metadata
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Data
WhereWhy
What How
Who
When
Email

Message

Who: "To" & "From"
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
Outlook Example, continued
22

Uses
What is the structure of metadata practices?
• Metadata practices connect data sources and uses in an
organized and efficient manner
– Storage: repository, glossary, models, lineage - currently multiple
technologies are used
– Engineering: identifying/harvesting/normalizing/administer evolving
metadata structures
– Delivery: supply/access/portal/definition/lookup search identify/ensure
required metadata supplies to meet business needs
– Governance: ensure proper/creation/storage/integration/control to support
effective use
• When executed, engineering and delivery implement governance
Sources
Metadata Governance
Metadata
Engineering
Metadata
Delivery
Metadata Practices
Metadata
Storage
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Specialized Team Skills

Extraction
Sources
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)
Opera<onal
Data
Data
Quality

Engineering
Master
Data

Management
Prac<ces
Suspected/
Iden<ﬁed

Data

Quality

Problems
Routine Data Scans
Master Data Catalogs
Routine Data Scans
Knowledge
Management
Prac<ces
Data
that
might
beneﬁt
from

Master
Management
24

Polling Question #1
• My organization began using or is planning to use a formal
approach to metadata management
a) Last year (2012)
b) This year (2013)
c) Next year (2014)
d) Not at all
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• 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
Types of Metadata: Process Metadata
28

Business Process Metadata
Who: Created the
documentation?
What: Are the important
dependencies
among the
processes?
How: Do the business
processes
interact with each
other?
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Data
WhereWhy
What How
Who
When
Email

Message

Types of Metadata: Business Metadata
• 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
30

Types of Metadata: Technical & Operational Metadata
• 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
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• 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
Types of Metadata: Data Stewardship
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Types of Metadata: Provenance
• 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
33

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
34

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
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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.
38

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
39

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)
40

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
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Metadata History 1990-2008
• The history of Metadata management tools and products
seems to be a metaphor for the lack of a methodological
approach to enterprise information management:
• Lack of standards and proprietary nature of most managed
Metadata solutions cause many organizations to avoid
focusing on metadata
• This limits organizations’ ability to develop a true
enterprise information management environment
• Increased attention given to information and its importance
to an organization’s operations and decision-making will
drive Metadata management products and solutions to
become more standardized
• More recognition to the need for a methodological
approach to managing information and metadata
44

Metadata History: The 1990s
• Business managers began to recognize the value of
Metadata repositories
• Newer tools expanded the scope
• Potential benefits identified during this period include:
– Providing semantic layer between company’s system and business
users
– Reducing training costs
– Making strategic information more valuable as aid in decision
making
– Creating actionable information
– Limiting incorrect decisions
45

Metadata History: Mid-to late 1990s
• Metadata becomes more relevant to corporations who were
struggling to understand their information resources caused by:
– Y2K deadline
– Emerging data warehousing initiatives
– Growing focus around the World Wide Web
• Beginning of efforts to try to standardize Metadata definition
and exchange between applications in the enterprise
• Examples of standardization:
– 1995: CASE Definition Interchange Facility (CDIF)
– 1995: Dublin Core Metadata Elements
– 1994 – 1999: First parts of ISO 11179 standard for Specification and
Standardization of Data Elements were published
– 1998: Common Warehouse Metadata Model (CWM)
– 1995: Metadata Coalitions’ (MDC) Open Information Model
– 2000: Both standards merged into CSM. Many Metadata repositories
began promising adoption of CWM standard
46

Metadata History: 21st Century
• Update of existing Metadata repositories for deployment on
the web
• Introduction of products to support CWM
• Vendors begin focusing on Metadata as an additional product
offering
• Few organizations purchase or develop Metadata repositories
• Effective enterprise-wide Managed Metadata Environments
are rare due to:
– Scarcity of people with real world skills
– Difficulty of the effort
– Less than stellar success of some of the initial efforts at some
companies
– Stagnation of the tool market after the initial burst of interest in late 90s
– Still less than universal understanding of the business benefits
– Too heavy emphasis on legacy applications and technical metadata
47

Metadata History: Current Decade
• Focus on need for and importance of metadata
• Focus on how to incorporate Metadata beyond traditional
structured sources and include semistructured sources
• Driving factors:
– Recent entry of larger vendors into the market
– Challenges related to addressing regulatory requirements, e.g.
Sarbanes-Oxley, and privacy requirements with unsophisticated tools
– Emergence of enterprise-wide initiatives, e.g. information
governance, compliance, enterprise architecture, automated
software reuse
– Improvements to the existing Metadata standards, e.g. RFP release
of new OMG standard Information Management Metamodel (IMM),
which will replace CWM
– Recognition at the highest levels that information is an asset that
must be actively and effectively managed
48

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
49

Metadata Strategy
• 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
50

Polling Question #2
• Compliance laws have influenced my organization to pay
more attention to and/or put more resources into:
a) Data quality improvement efforts
b) Metadata management efforts
c) Database management, in general
d) No impact
51

Metadata Strategy Implementation Phases
52

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Goals and Principles
55
• Provide organizational
understanding of terms
and usage
• Integrate Metadata from
diverse sources
• Provide easy, integrated
access to metadata
• Ensure Metadata quality
and security

Polling Question #3
• My organization began using or is planning to use a
metadata repository (purchased or homegrown)
a) Last year (2012)
b) This year (2013)
c) Next year (2014)
d) Not applicable
56

Activities
• 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
57

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
58

• 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
Activities: Noteworthy Metadata Standards Types
Warehouse
ProcessWarehouse
ProcessWarehouse
Process Warehouse
Opera;onWarehouse
Opera;onWarehouse
Opera;on
Transforma<onTransforma<on OLAP
Data

Mining
Informa<on

Visualiza<on
Business

Nomenclature
Object
Model Rela<onal Record Mul<dimensionalMul<dimensional XML
Business

Informa<on
Data
Types Expression
Keys
and

Indexes
Type
Mapping
SoOware

Deployment
Object
ModelObject
ModelObject
ModelObject
ModelObject
ModelObject
Model
Management
Analysis
Resource
Founda<on
59

Information Management Metamodel (IMM)
• 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
60

• Metadata repositories
• Quality metadata
• Metadata analysis
• Data lineage
• Change impact analysis
• Metadata control procedures
• Metadata models and architecture
• Metadata management operational analysis
Primary Deliverables
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• 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
Roles and Responsibilities
62

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
63

Polling Question #4
• Do you use metadata models and/or modeling tools to
support your information quality efforts?
a) Yes
b) No
64

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15 Guiding Principles
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
67

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
15 Guiding Principles, continued
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Copyright 2013 by Data Blueprint 6609/10/12
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
71

• 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
72

• To organize iTunes
– I create a "New Smart
Playlist" for Artist's
containing "Miles Davis"
73

6909/10/12
• 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
74

7009/10/12
• The same:
–Interface
–Processing
–Data Structures
• are applied to
–Podcasts
–Movies
–Books
–.pdf files
• Economies of scale
are enormous
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Uses
Metadata Take Aways
• 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
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Specialized Team Skills

Metadata Management Summary
79

References & Recommended Reading
80

References, cont’d
81

82

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Questions?
It’s your turn!
Use the chat feature or Twitter (#dataed) to submit
your questions to Peter now.
+ =
84

Value Pt. 2: Cloud
August 13, 2013 @ 2:00 PM ET/11:00 AM PT
Value Pt. 3: Warehousing
September 10, 2013 @ 2:00 PM ET/11:00 AM PT
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Data Systems Integration & Business Value Pt. 1: Metadata

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Data Systems Integration & Business Value Pt. 1: Metadata