The first step towards understanding what data assets mean for your organization is understanding what those assets mean for each other. Metadata—literally, data about data—is one of many data management disciplines inherent in good systems development, and is perhaps the most mislabeled and misunderstood out of the lot. Understanding metadata and its associated technologies as more than just straightforward technological tools can provide powerful insight, the efficiency of organizational practices, and can also enable you to combine more sophisticated data management techniques in support of larger and more complex business initiatives.
In this webinar, we will:
Illustrate how to leverage metadata in support of your business strategy
Discuss foundational metadata concepts based on the DAMA Guide to Data Management Book of Knowledge (DAMA DMBOK)
Enumerate guiding principles for and lessons previously learned from metadata and its practical uses
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Metadata Strategies - Data Squared
1. 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
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2. 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
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Metadata
UsesUsesReuses
What is data management?
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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
3.
What is data management?
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Sources
Data
Engineering
Data
Delivery
Data
Storage
Resources
(optimized for reuse)
Data Governance
AnalyticInsight
Specialized Team Skills
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
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4. 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
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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
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Manage data coherently
Manage data assets professionally
Data life cycle
management
Organizational support
Data
Quality
3 3
33
1
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DataManagement
BodyofKnowledge(DMBoK)
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DataManagement
BodyofKnowledge(DMBoKV2)
7. 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"
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Data
About
Data
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UsesSources
Metadata Governance
Metadata
Engineering
Metadata
Delivery
Metadata
Storage
Specialized Team Skills
Metadata Practices
• If metadata is data then what technologies and techniques
should we use to manage it?
• Data management technologies and techniques
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• 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
12. Defining Metadata
Metadata is any
combination of
any circle and the
data in the center
that unlocks the
value of the data!
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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)
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Library Book
WhereWhy
What How
Who
When
13. 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?"
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
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14. 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
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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
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20. 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
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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.
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22. 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?
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Keep the proper focus
• Wrong question:
– Is this metadata?
• Right question:
– Should we include this
data item within the
scope of our
metadata
practices?
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23. Definition of Bed
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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."
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Draft
24. MetadataImplementationPhases
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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
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26. Build Your Own Metadata Repository
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Sample
Low-tech
Repository
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FTI Metadata Model
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32. 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
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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
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33. • To organize iTunes
– I create a "New Smart Playlist" for
Artist's containing "Miles Davis"
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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
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34. • The same:
–Interface
–Processing
–Data Structures
• are applied to
–Podcasts
–Movies
–Books
–.pdf files
• Economies of scale
are enormous
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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
68Copyright 2017 by Data Blueprint Slide #
Metadata
38. Upcoming Events
Your Data Strategy
January 9, 2018 @ 2:00 PM ET/11:00 AM PT
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75Copyright 2017 by Data Blueprint Slide #