Data-Ed Online Webinar: Metadata Strategies

Metadata Management Strategies
Copyright 2014 by Data Blueprint
Good systems development often depends on multiple data
management disciplines that provide a solid foundation. One of
these is metadata. While much of the discussion around metadata
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: November 11, 2014
Time: 2:00 PM ET/11:00 AM PT
Presenter: Peter Aiken, Ph.D.

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MONETIZING
DATA MANAGEMENT
Unlocking the Value in Your Organization’s
Most Important Asset.
PETER AIKEN WITH JUANITA BILLINGS
FOREWORD BY JOHN BOTTEGA
Peter Aiken, Ph.D.
4
The Case for the
Chief Data Officer
Recasting the C-Suite to Leverage
Your Most Valuable Asset
Peter Aiken and
Michael Gorman
• 30+ years data management
experience
• Multiple international awards/
recognition
• Founder, Data Blueprint (datablueprint.com)
• Associate Professor of IS, VCU (vcu.edu)
• (Past) President, DAMA Int. (dama.org)
• 9 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, Walmart, and the
Commonwealth of Virginia

Peter Aiken, Ph.D.
10124 W. Broad Street, Suite C
Glen Allen, Virginia 23060
804.521.4056

Whither the "data dictionary?
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• 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 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)

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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
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Uses
What is data management?
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Sources
Data Governance
Data
Engineering
Data
Delivery
Data
Storage
Specialized Team Skills
• Data management practices connect data sources and
uses in an organized and efficient manner
– Storage
– Engineering
– Delivery
– Governance
• When executed, engineering, storage, and delivery
implement governance

DMM℠ Structure
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Five Integrated DM Practice Areas
Manage data coherently.
Share data across boundaries.
Data Program
Coordination
Organizational Data
Integration
Assign responsibilities for data.
Data Stewardship Data Development
Engineer data delivery systems.
Maintain data availability.
Data Support
Operations
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DMM℠ Structure
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Maslow's Hierarchiy of Needs
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Data Management Practices Hierarchy
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)
Technologies Capabilities
Advanced
Data
Practices
• MDM
• Mining
• Big Data
• Analytics
• Warehousing
• SOA
Foundational Data Management Practices
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Data Governance Data Quality
Data Platform/Architecture
Data Management Strategy
Data Operations

Data Management Body of Knowledge
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Data
Management
Functions

DAMA DM BoK & CDMP
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• 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

Metadata Management from The DAMA Guide to the Data Management Body of Knowledge © 2009 by DAMA International
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What is a Strategy?
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• Current use derived from military
• "a pattern in a stream of decisions" [Henry Mintzberg]
• "a system of finding, formulating, and developing a
doctrine that will ensure long-term success if followed
faithfully [Vladimir Kvint]

Meta data, Meta-data, or metadata
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• 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"

Definitions
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• 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

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Analogy: a library card catalog
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• 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)
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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

Defining Metadata
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Who
What How
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
Why Where
When

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)
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Who
What How
Data
Library Book
Why Where
When

Outlook Example
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"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?"

What is the structure of metadata practices?
Uses
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Sources
Metadata
Engineering
Metadata
Delivery
Metadata Governance
Metadata Practices
Metadata
Storage
• Metadata practices connect data sources and uses in an
organized and efficient manner
– Storage: repository, glossary, models, lineage - often multiple
technologies
– 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

Polling Question #1
• My organization began using or is planning to use a formal
approach to metadata management
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a) Last year (2013)
b) This year (2014)
c) Next year (2015)
d) Not at all

Polling Question #1 (from last year)
• My organization began using or is planning to use a formal
approach to metadata management
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a) Last year (2012) 38%
b) This year (2014) 13%
c) Next year (2015) 14%
d) Not at all 15%

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Types of Metadata: Process Metadata
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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

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

Types of Metadata: Business Metadata
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• 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

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
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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

Types of Metadata: Data Stewardship
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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: Provenance
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• 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

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
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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.
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Metadata for Semistructured Data
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• 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
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• 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)

4. Enterprise data model } from The DAMA Guide to the Data Management Body of Knowledge © 2009 by DAMA International
Specific Example
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• Four metadata
sources:
1. Existing reference
models (i.e., ADRM)
2. Conceptual model
created two years ago
3. Existing systems (to be
reverse engineered)

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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
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• 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

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
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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

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
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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

Metadata History: Current Decade
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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• 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.

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.
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– https://www.eff.org/deeplinks/2013/06/why-metadata-matters

Metadata Strategy
drivers, issues, and information requirements for the enterprise Metadata program
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• 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
• 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

Metadata Strategy Implementation Phases
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Polling Question #2
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• Compliance laws have influenced my organization to pay
more attention to and/or put more resources into:
a) Data quality improvement efforts 29%
b) Metadata management efforts 6%
c) Database management, in general 27%
d) No impact 13%

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

Activities
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• 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

Activities: Metadata Standards Types
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• 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

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
Warehouse Process Warehouse Operation
Transformation OLAP Data
Mining
Information
Visualization
Business
Nomenclature
Object
Model Relational Record Multidimensional XML
Business
Information Data Types Keys and
Type
Expression Indexes
Mapping
Software
Deployment
Object Model
Management
Analysis
Resource
Foundation
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data constructs.
• The CWM Metamodel

Information Management Metamodel (IMM)
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• 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

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

Roles and Responsibilities
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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

Technology
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• 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

Polling Question #3
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• Do you use metadata models and/or modeling tools to
support your information quality efforts?
a) Yes 49%
b) No 39%

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15 Guiding Principles
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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

15 Guiding Principles, continued
9. Measure content and usage
10.Leverage XML, messaging and web services
11. Establish and maintain enterprise-wide business involvement
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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
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
• Insert a recently
purchased CD
• iTunes can:
– Count the
number of tracks
(25)
– Determine the
length of each
track
09/10/12 66
Example: iTunes Metadata
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• 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
09/10/12 67
77

• To organize iTunes
– I create a "New
Smart Playlist" for
Artist's containing
"Miles Davis"
09/10/12 68
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• 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
09/10/12 6979

• The same:
–Interface
–Processing
–Data Structures
• are applied to
–Podcasts
–Movies
–Books
–.pdf files
• Economies of
scale are
enormous
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• Metadata unlocks the value of data, and therefore requires
management attention [Gartner 2011]
Uses
Metadata Take Aways
83
Metadata
Engineering
Metadata
Delivery
• Metadata is the language of data governance
• Metadata defines the essence of integration challenges
Sources
Metadata Governance
Metadata Practices
Metadata
Storage

Data Management Body of Knowledge
84
Data
Management
Functions

Metadata Management Summary
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References & Recommended Reading
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References, cont’d
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88

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Polling Question #4
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• My organization began using or is planning to use a
metadata repository (purchased or homegrown)
a) Last year (2013)
b) This year (2014)
c) Next year (2015)
d) Not applicable

Questions?
+ =
It’s your turn!
Use the chat feature or Twitter (#dataed) to submit
your questions to Peter now.
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Upcoming Events
92
Data Systems Integration & Business
Value Pt. 2: Cloud
August 13, 2013 @ 2:00 PM ET/11:00 AM PT
Data Systems Integration & Business
Value Pt. 3: Warehousing
September 10, 2013 @ 2:00 PM ET/11:00 AM PT
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Data-Ed Online Webinar: Metadata Strategies

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