DataEd Online: Let's Talk Metadata Strategies and Successes

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Welcome!
Let’s Talk Metadata:
Strategies and Successes

Date: September 11,
2012
Time: 2:00 PM ET
Presented by: Dr. Peter Aiken

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Meet Your Presenter: Dr. Peter Aiken
• Internationally recognized thought-leader in
the data management field with more than 30
years of experience
• Recipient of multiple international awards
• Founding Director of Data Blueprint
(http://datablueprint.com)
• Associate Professor of Information Systems at
Virginia Commonwealth University
(http://vcu.edu)
• President of DAMA International
(http://dama.org)
• DoD Computer Scientist, Reverse Engineering
Program Manager/
Office of the Chief Information Officer
• Visiting Scientist, Software Engineering
Institute/Carnegie Mellon University
• 7 books and dozens of articles
• Experienced w/ 500+ data management
practices in 20 countries #data
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Let’s Talk Metadata:
Strategies and
Successes

Let’s Talk Metadata: Strategies and Successes
DATA BLUEPRINT 10124-C W. BROAD ST, GLEN ALLEN, VA 23060 EDUCATION 9/11/2012

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Outline
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 examples
9. Take Aways, References and Q&A
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The DAMA Guide to the Data Management Body of Knowledge
• Published by
DAMA
International
– The professional
association for
Data Managers
(40 chapters
worldwide)
• DMBoK organized
around
– Primary data
management
functions focused
around data
delivery to the
organization
– Organized around
several Data
environmental Management
elements Functions
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The DAMA Guide to the Data Management Body of Knowledge

Amazon:

http://
www.amazon.com/
DAMA-Guide-
Management-
Knowledge-DAMA-
DMBOK/dp/
0977140083
Or enter the terms
"dama dm bok" at
the Amazon search
engine

Environmental Elements
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Data Management

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Data Management
Manage data coherently.

Data Program
Coordination
Share data across boundaries.
Organizational
Data Integration

Data Data
Stewardship Development

Assign responsibilities for data.
Engineer data delivery systems.

Data Support
Operations
Maintain data availability.

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Organizational DM Functions and their Inter-relationships
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Data management
processes and
infrastructure
Organizational Strategies Implementation
Data Program Guidance
Coordination
Goals

Combining multiple Organizational Integrated
assets to produce Data Integration Models Achieve sharing of data
extra value
within a business area

Organizational-
entity subject area Data Data
data Stewardship Development
integration Standard
Data

Application
Models & Designs

Direction Provide reliable Data Support Data
access to data
Operations Business Asset Use
Feedback Data
Leverage data in organizational activities

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

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Outline
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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"
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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 2010]
Metadata Management is …
• … the set of processes that ensure proper creation, storage,
integration, and control to support associated use of metadata
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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
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Definition (continued)
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• 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
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Defining Metadata
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Who

Metadata is any What How
combination of
any circle and the Data
data in the center
that unlocks the
value of the data! Why Where

When
Adapted
from
Brad
Melton

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Library Metadata Example
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Libraries can operate efficiently through careful use of metadata (Card Catalog)

Who: Author
What: Title Who
Where: Shelf
Location What How
When: Publication
Date
Library

A small amount of Why Where
metadata (Card
Catalog) unlocks the
value of a large amount When
of data (the Library)
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Outlook Example
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Who
"Outlook" metadata is
used to navigate and
manage email What How
Imagine how Email
managing e-mail Data
Messages
(already non-trivial)
would change if
Outlook did not make Why Where
use of metadata
When

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Outlook Example, continued
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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

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Metadata practices connect data sources and
uses in an organized and efficient manner
Metadata Practices
Metadata Metadata Metadata
Engineering Storage Delivery
Sources Uses
Metadata Governance

• What is the structure of metadata practices?
– 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
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Metadata Practices will be inextricably intertwined with
TITLE
ExtracKon
Sources
Data Quality and Master Data and Knowledge
Management, (among other functions)
Knowledge
Organized
Knowledge
'Data'
Management
PracKces
RouKne
Data
Scans Data
OrganizaKon
PracKces
Metadata(Prac8ces((dashed lines not in existence)
( (

Metadata( Metadata( Metadata(
Engineering( Storage( Delivery(
Sources( ( Uses(
Metadata(Governance(

Data
that
might
beneﬁt
from

Suspected/ Master
Management
IdenKﬁed
Master
Data
Catalogs

Data

Master
Data

Quality

Management
Problems Data
Quality
PracKces
Engineering

RouKne
Data
Scans
Improved
Quality
Data
OperaKonal
Data

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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

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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
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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
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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
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Polling Question #1
What have been the driving factors in focusing on
metadata within the last decade?

a. Recent entry of smaller vendors into the market
b. Challenges related to addressing regulatory requirements
c. Declination to the existing Metadata standards

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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
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Types of Metadata: Process Metadata

• 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
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Business Process Metadata
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Who
Who: Created the
documentation?
What: Are the important What How
dependencies
among the Data
processes?
How: Do the business
processes Why Where
interact with
each other?
When
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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
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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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Types of Metadata: Data Stewardship
• 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
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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.
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Metadata Subject Areas
Subject
Areas Components
1) Business Analytics Data definitions, reports, users, usage, performance

2) Business Architecture Roles and organizations, goals and objectives
Business terms and explanations for a particular
3) Business Definitions concept, fact, or other item found in an organization

4) Business Rules Standard calculations and derivation methods

Policies, standards, procedures, programs, roles,
5) Data Governance organizations, stewardship assignments

Sources, targets, transformations, lineage, ETL
6) Data Integration workflows, EAI, EII, migration/conversion

7) Data Quality Defects, metrics, ratings

Unstructured data, documents, taxonomies,
8) Document Content ontologies, name sets, legal discovery, search engine
Management indexes
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Metadata Subject Areas, continued
Subject
Areas Components
9) Information Technology
Platforms, networks, configurations, licenses
Infrastructure
Entities, attributes, relationships and rules, business
10) Conceptual data models
names and definitions.
Files, tables, columns, views, business definitions,
11) Logical Data Models
indexes, usage, performance, change management
Functions, activities, roles, inputs/outputs, workflow,
12) Process Models
timing, stores
13) Systems Portfolio and IT Databases, applications, projects, and programs,
Governance integration roadmap, change management
14) Service-oriented
Architecture (SOA) Components, services, messages, master data
information:
15) System Design and
Requirements, designs and test plans, impact
Development
Data security, licenses, configuration, reliability,
16) Systems Management
service levels
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Benefits of Metadata
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
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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
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Polling Question #2
Which is not an example of descriptive metadata?

a. Catalog information
b. Thesauri keyword terms
c. Thesauri keyword labels

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Metadata for Unstructured Data: Examples
Examples of descriptive
metadata:
• Catalog information
• Thesauri keyword terms Examples of
administrative metadata
• Source(s)
• Integration/update schedule
Examples of structural • Access rights
metadata • Page relationships (e.g. site
• Dublin Core navigational design)
• Field structures
• Format (audio/visual, booklet)
• Thesauri keyword labels
• XML schemas
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Sources of Metadata
Primary Sources:
• Virtually anything named in an organization

Secondary sources:
• Other Metadata repositories, accessed using
bridge software
• CASE tools, ETL tools

Many data management tools create and use
repositories for their own use.

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}
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Specific Example
Four metadata
sources: ADRM

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 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
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Metadata Strategy Implementation Phases

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Metadata Management

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ü ü ü ü ü ü ü
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Goals and Principles
1. Provide organizational
understanding of terms
and usage
2. Integrate Metadata
from diverse sources
3. Provide easy,
integrated access to
metadata
4. Ensure Metadata
quality and security
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Activities
1) Understand Metadata requirements
2) Define the Metadata architecture
3) Develop and maintain Metadata standards
4) Implement a managed Metadata
environment
5) Create and maintain metadata
6) Integrate metadata
7) Management Metadata repositories
8) Distribute and deliver metadata
9) Query, report and analyze metadata
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Activities: Metadata Standards Types
• Two major types exist:
1) Industry or consensus
standards
2) International standards

• High level framework shows how
standards are related and how
they rely on each other for
context and usage:

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Activities: Noteworthy Metadata Standards Types
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
Management Warehouse
Process Warehouse
Opera=on
Data
InformaKon
Business

Analysis TransformaKon OLAP
Mining VisualizaKon Nomenclature
Object

Resource RelaKonal Record MulKdimensional XML
Model
Business
Keys
and
Type
SoTware

Data
Types Expression
FoundaKon InformaKon Indexes Mapping Deployment
Object
Model

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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

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The Information Management Metamodel...
•

Based
on
Core
model.
•

Used
to
translate
from
one
model
to
another.

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Primary Deliverables
• Metadata repositories

• Quality metadata

• Metadata analysis
• Data lineage

• Change impact analysis

• Metadata control procedures

• Metadata models and architecture

• Metadata management operational analysis
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Roles and Responsibilities
Suppliers:
– Data Stewards
– Data Architects
– Data Modelers
– Database Administrators
– Other Data Professionals
– Data Brokers
– Government and Industry
Regulators

Participants:
– Metadata Specialists Consumers:
– Data Integration Architects
– Data Stewards • Data Stewards
– Data Architects and Modelers • Data Professionals
– Database Administrators • Other IT Professionals
– Other DM Professionals • Knowledge Workers
– Other IT Professionals • Managers and Executives
– DM Executives
– Business Users • Customers and Collaborators
• Business Users

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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
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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 DAMA Guidebusiness value of Knowledge © 2009 by DAMA International
from The
show to the Data Management Body
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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
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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

PRODUCED

09/10/12
©

TITLE

• 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
PRODUCED

09/10/12
©

TITLE

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

PRODUCED

09/10/12
©

TITLE

• 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"
PRODUCED
BY CLASSIFICATION
DATE SLIDE

to a folder
09/10/12 ©

TITLE

• The same:
– Interface
– Processing
– Data Structures
• are applied to
– Podcasts
– Movies
– Books
– .pdf files
• Economies of scale
PRODUCED
BY are enormous 70
CLASSIFICATION

09/10/12
DATE SLIDE

09/10/12
©
BLUEPRINT 10124-C Blueprint - all rights ST, GLEN ALLEN, VA 23060 EDUCATION

TITLE
Questions?

+ =

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

PRODUCED

09/10/12
©

DataEd Online: Let's Talk Metadata Strategies and Successes

DataEd Online: Let's Talk Metadata Strategies and Successes

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