Essential Metadata Strategies

© Copyright 2021 by Peter Aiken Slide # 1
peter.aiken@anythingawesome.com +1.804.382.5957 Peter Aiken, PhD
The Importance
of Metadata
Leveraging Strategies
Peter Aiken, Ph.D.
• I've been doing this a long time
• My work is recognized as useful
• Associate Professor of IS (vcu.edu)
• Institute for Defense Analyses (ida.org)
• DAMA International (dama.org)
• MIT CDO Society (iscdo.org)
• Anything Awesome (plusanythingawesome.com)
• Experienced w/ 500+ data
management practices worldwide
• Multi-year immersions
– US DoD (DISA/Army/Marines/DLA)
– Nokia
– Deutsche Bank
– Wells Fargo
– Walmart
– HUD …
• 12 books and
dozens of articles
https://anythingawesome.com
+
• DAMA International President 2009-2013/2018/2020
• DAMA International Achievement Award 2001
(with Dr. E. F. "Ted" Codd
• DAMA International Community Award 2005

3
© Copyright 2021 by Peter Aiken Slide #
Program
The Importance of M
etadata
• Defining metadata in the
context of data management
– Defining data management
– What do we mean by using data as metadata
and why is this important? (Hint: leverage)
– Specific teachable example using iTunes/Music
• S1: Metadata is a gerund–do not treat it as a noun
– Metadata is a use of data, not a type of data
• S2: Enforce metadata to be the language of data governance
– Make metadata the language of data governance
• S3: Treat glossaries/repositories as capabilities not technology
– Cyclic approaches do not start with technologies
• S4: Build from metadata building blocks
– Many many many resources available to jump-start metadata efforts
• Benefits, application & sources
– Understand that metadata defines the essence of organizational interoperability
• Take Aways, References and Q&A
Meta data
• 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
• So, the term is "metadata"
• There is a copyright on
the term "metadata," but
it has not been enforced
Meta data, meta-data
Meta data, meta-data, metadata

Blind Persons and the Elephant
https://anythingawesome.com http://www.dailymirror.lk/print/opinion/editorial-we-need-to-become-channels-of-peace/172-27164
It is like a fan!
It is like a snake!
It is like a wall!
It is like a rope!
It is like a tree!
Unrefined
data management
definition
Sources
Uses
Data Management

More refined
data management
definition
Sources
Reuse
Data Management
➜ ➜
Better still data management definition
Sources
➜ Use
➜Reuse
➜
Formal Data Reuse Management

Blind Persons and the Elephant
https://anythingawesome.com http://www.dailymirror.lk/print/opinion/editorial-we-need-to-become-channels-of-peace/172-27164
Presentation
Data Engineering
Data Science
Story Telling
Storage
Innovations
The prefix meta-
1. Situated behind: metacarpus.
2. a. Later in time: metestrus.
b. At a later stage of development: metanephros.
3. a. Change; transformation: metachromatism.
b. Alternation: metagenesis.
4. a. Beyond; transcending; more comprehensive:
metalinguistics.
b. At a higher state of development: metazoan.
5. Having undergone metamorphosis: metasomatic.
6. a. Derivative or related chemical substance: metaprotein.
b. Of or relating to one of three possible isomers of a benzene ring
with two attached chemical groups, in which the carbon atoms with
attached groups are separated by one unsubstituted carbon atom:
meta-dibromobenzene.
Definition of the prefix meta- (Emphasis added – source: American Heritage English Dictionary © 1993 Houghton Mifflin).
Meta
10
4. a. Beyond; transcending; more comprehensive: metalinguistics.
b. At a higher state of development: metazoan.

https://plusanythingawesome.com
Analogy: a library card catalog
• Identifies
– What books are in the library,
and
– Where they are located
• Search by
– Subject area
– Author, or
– Title
• Catalog shows
– Author
– Subject tags
– Publication date and
– Revision history
• Determine which books will
meet the reader’s
requirements
• Without the catalog, finding
things is difficult, time
consuming and frustrating
from The DAMA Guide to the Data Management Body
of Knowledge © 2009 by DAMA International

Data
About
Data
The most likely managed metadata in your organization
• Tracking network users and access points is metadata
• 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)

Leverage is an Engineering Concept
• Using proper engineering techniques, a human can lift a bulk that
is weighs much more than the human
1 kg
10 kg
11 kg
A wholistic approach to obtaining data leverage
Organizational
Data
Knowledge workers
supplemented by
data professionals
Process
Guided by strategy
https://www.computerhope.com/jargon/f/framework.htm
People
Technology
Reducing ROT increases data leverage
Metadataisaprimarymeansofapplyingleveragetodata

Data Leverage is a multi-use concept
• Permits organizations to better manage their data
– Within the organization, and
– With organizational data exchange partners
– In support of the organizational mission
• Leverage is enabled by metadata
– Obtained by implementation of data-centric
technologies, processes, and human skill sets
– Focus on the non-ROT data
• The bigger the organization, the greater potential leverage exists
• Treating data more asset-like simultaneously
– Lowers organizational IT costs and
– Increases organizational knowledge worker productivity
Separating the Wheat from the Chaff
Is well organized data worth more?

Pre-Information Age Metadata
• Examples of information architecture achievements that
happened well before the information age:
– Page numbering
– Alphabetical order
– Table of contents
– Indexes
– Lexicons
– Maps
– Diagrams
Example from: How to make sense of any mess
by Abby Covert (2014) ISBN: 1500615994
"While we can arrange things
with the intent to communicate
certain information, we can't
actually make information. Our
users do that for us."
https://www.youtube.com/watch?v=60oD1TDzAXQ&feature=emb_logo
https://www.youtube.com/watch?v=r10Sod44rME&t=1s
https://www.youtube.com/watch?v=XD2OkDPAl6s
Remove the structure and things fall apart rapidly
• Better organized data increases in value

Separating the Wheat from the Chaff
• Better organized data increases in value
• Poor data management practices are costing
organizations money/time/effort
• 80% of organizational data is ROT
– Redundant
– Obsolete
– Trivial
• The question is which
data to eliminate?
– Most enterprise data is
never analyzed
https://plusanythingawesome.com 21
Metadata yields …
Valuable information about your data assets:
• Do we have these specific (or this class of) data assets?
• What is the quality of …
• What will be the cost to improve this class of data assets?
• Can these data assets be provided more granularly?
• … (increasing insight)
Yes!
Not suitable! 35¢/apiece
Not easily!

Metadata
Management
23
Data
Management
Body
of
Knowledge
(DM
BoK
V2)
Practice
Areas
from The DAMA Guide to the Data Management Body of Knowledge 2E © 2017 by DAMA International
Metadata Management
from The DAMA Guide to the Data Management Body of Knowledge © 2009 by DAMA International
24

• Example:
- iTunes Metadata
• Insert a recently purchased CD
• iTunes can:
- Count the number of tracks (25)
- Determine the length of each track
Example:
iTunes Metadata
• 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
Example:
iTunes Metadata

Example: iTunes Metadata
• To organize iTunes
– I create a "New Smart Playlist" for
Artist's containing "Miles Davis"
Example:
iTunes Metadata
• Notice I didn't get the
desired results
• I already had another
Miles Davis
recording
• 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
• Or not?
Example:
iTunes Metadata

• The same:
– Interface
– Processing
– Data
Structures
• are applied to
– Podcasts
– Movies
– Books
– .pdf files
• Economies of
scale are
enormous
Example:
iTunes Metadata
Metadata yields …
Valuable information about your iTunes assets:
• Do we have these specific Miles Davis recordings?
• Most my played Miles Davis recording
• What will be the cost to improve this class of data assets?
• Can I listen to the entire album before dinner?
Yes!
Bitches Brew
2¢/each
Not easily!

31
Program
The Importance of M
etadata
• If others do not understand
what you do then you are
perceived with a cost bias
• If others understand what you
do then you can be perceived
with a value bias
Comprehension by others is critical!

Defining Metadata
Adapted from Brad Melton
Where
How
Who
When
Data
Metadata is any
combination of any
circle and the data
in the center that
unlocks the value
of the data!
What
Why

Outlook Example
"Outlook" metadata is used
to navigate/manage email
What: "Subject"
How: "Priority"
Where: "USERID/Inbox",
"USERID/Personal"
Why: "Body"
When: "Sent" & "Received”
• Find the important stuff/weed out junk
• Organize for future access/outlook rules
• Imagine how managing e-mail (already non-trivial)
would change if Outlook did not make use of
metadata Who:"To" & "From?"
Definitions
• Metadata is
– Everywhere in every data management activity and integral
to all IT systems and applications.
– To data what data is to real life. Data reflects real life transactions, events,
objects, relationships, etc. Metadata reflects data transactions, events, objects,
relations, etc.
– The data that describe the structure and workings of an
organization’s use of information, and which describe the
systems it uses to manage that information.
[quote from David Hay's book, page 4]
– Data describing various facets of a data asset, for the
purpose of improving its usability throughout its life cycle [Gartner 2010]
– Metadata unlocks the value of data, and therefore requires management attention
[Gartner 2011]
• Metadata Management is
– The set of processes that ensure proper creation, storage, integration, and
control to support associated use of metadata

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
• It is 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?

0 500 1000 1500 2000 2500
Manage Positions(2%)
Plan Careers (~5%)
AdministerTraining (~5%)
Plan Successions(~5%
Manage Com
petencies(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
0 500 1000 1500 2000 2500
Plan Careers (~5%)
Manage Com
petencies(20%)
TargetSystem (3.9%)
EDI Manager (.9%)
0 500 1000 1500 2000 2500
Plan Careers (~5%)
Manage Com
petencies(20%)
TargetSystem (3.9%)
EDI Manager (.9%)
Why data structure problems have been difficult?

Student
Data
Base
Master
Proposed
Data Model

Business Goals Model
Defines the mission of the
enterprise, its long-range goals,
and the business policies and
assumptions that affect its
operations.
Business Rules Model
Records rules that govern the
operation of the business and the
Business Events that trigger
execution of Business Processes.
Enterprise Structure Model
Defines the scope of the enterprise
to be modeled. Assigns a name to the
model that serves to qualify each
component of the model.
Extension Support Model
Provides for tactical Information
Model extensions to support special
tool needs.
Info Usage Model
Specifies which of the
Entity-Relationship Model
component instances are used by
other Information Model
components.
Global Text Model
Supports recording of extended
descriptive text for many of the
Information Model components.
DB2 Model
Refines the definition of a Relational
Database design to a DB2-specific
design.
IMS Structures Model
Defines the component structures
and elements and the application
program views of an IMS Database.
Flow Model
Specifies which of the Entity
Relationship Model component
instances are passed between
Process Model components.
Applications Structure Model
Defines the overall scope of an automated
Business Application, the components of the
application and how they fit together.
Data Structures Model
Defines the data structures and their
elements used in an automated
Business Application.
Application Build Model
Defines the tools, parameters and
environment required to build an
automated Business Application.
Derivations/Constraints Model
Records the rules for deriving legal
values for instances of
components, and for controlling the
use or existence of E-R instance.
Defines the Business Entities, their
properties (attributes) and the
relationships they have with other
Business Entities.
Organization/Location Model
Records the organization structure
and location definitions for use in
describing the enterprise.
Process Model
Defines Business Processes, their
sub processes and components.
Relational Database Model
Describes the components of a
Relational Database design in
terms common to all SAA
relational DBMSs.
Test Model
Identifies the various file (test
procedures, test cases, etc.)
affiliated with an automated
business Application for use in
testing that application.
Library Model
Records the existence of
non-repository files and the role they
play in defining and building an
Panel/Screen Model
Identifies the Panels and Screens and
the fields they contain as elements
used in an automated Business
Application.
Program Elements Model
Identifies the various pieces and
elements of application program
source that serve as input to the
application build process.
Value Domain Model
Defines the data characteristics
and allowed values for
information items.
Strategy Model
Records business strategies to
resolve problems, address goals,
and take advantage of business
opportunities. It also records
the actions and steps to be taken.
Resource/Problem Model
Identifies the problems and needs
of the enterprise, the projects
designed to address those needs,
and the resources required.
Process Model
Extension
Support Model
Application
Structure
Model
DB2 Model
Relational
Database
Model
Global Text
Model
Strategy
Model
Derivations/
Constriants
Model
Application
Build Model
Test Model
Panel/ Screen
Model
IMS Structure
Model
Data
Structure
Model
Program
Elements
Model
Business
Model
Goals
Organization/
LocationModel
Resource/
Problem
Model
Enterprise
Structure
Model
Entity-
Relationship
Model
Info Usage
Model
Value Domain
Model
Flow Model
Business
Rules Model
Library
Model
IBM's AD/Cycle Information Model
Keep the proper focus
• Wrong question:
– Is this metadata?
• Right question:
– Would we obtain value from
this data item if we include
it in the scope of our
metadata practices?

45
Program
The Importance of M
etadata
Data
Data
Data
Information
Fact Meaning
Request
[Built on definitions from Dan Appleton 1983]
Intelligence
Strategic Use
Data
Data
Data Data
A Model Defining 3 Important Concepts
“You can have data without information, but
you cannot have information without data”
— Daniel Keys Moran, Science Fiction Writer
1. Each FACT combines with one or more MEANINGS.
2. Each specific FACT and MEANING combination is referred to as a DATUM.
3. An INFORMATION is one or more DATA that are returned in response to a specific REQUEST
4. INFORMATION REUSE is enabled when one FACT is combined with more than one MEANING.
5. INTELLIGENCE is INFORMATION associated with its STRATEGIC USES.
6. DATA/INFORMATION must formally arranged into an ARCHITECTURE.
Wisdom & knowledge are
often used synonymously
Useful Data

Data Strategy and Data Governance in Context
Organizational
Strategy
Data Strategy
IT Projects
Organizational Operations
Data Governance
Data asset
support for
organizational
strategy
What the data
assets do to support
strategy
How well the data
strategy is working
Operational
feedback
How data is
delivered by IT
How IT supports
strategy
Other aspects of
organizational strategy
Data Strategy and Governance in Strategic Context
Organizational
Strategy
Data Strategy Data Governance
Data asset
support for
organizational
strategy
What the data assets do
to support strategy
How well the data
strategy is working
(Business Goals)
(Metadata)
IT Projects
How data is
delivered by IT

Data Governance & Data Stewards
Data Strategy
Data
Governance
What the data
assets do to support
strategy
How well the data
strategy is working
(Business Goals)
(Metadata)
Data Stewards
What is the
most effective
use of steward
investments?
(Metadata)
Progress,
plans,
problems
Domain expertise is less ← | → Domain expertise is greater
Roles more formally defined ← |→ Roles less formally defined
Encounter
governed
data
more
directly
←
|
→
Encounter
governed
data
less
directly
More
time
is
dedicated
←
|
→
Less
time
is
dedicated
IT/Systems Development
Leadership
(data decision makers)
Stewards
(data trustees)
Guidance
Decisions
Participants/Experts
(data subject matter experts)
Other Sources/Uses
(data makers & consumers)
IT/Systems
Development
Data/feedback
Changes
Action
R
e
s
o
u
r
c
e
s
Ideas
Data/Feedback
Components comprising the data community

Leadership
(data decision makers)
Stewards
(data trustees)
Guidance
Decisions
Participants/Experts
(data subject matter experts)
Other Sources/Uses
(data makers & consumers)
Data/feedback
Changes
Action
R
e
s
o
u
r
c
e
s
Ideas
Data/Feedback
Basic Version
Metadata yields …
Valuable information about your data governance assets & processes:
• Do we have a shared understanding of our goals?
• Are we and IT focused on similar goals
• How cost effective are we being?
• What kind of metadata do we find most valuable?
• … (increasing insight)
Yes!
Yes
2¢/each
Supply Chain

53
Program
The Importance of M
etadata
Definition of Bed

Purpose statement incorporates motivations
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
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.")
55
Draft
NTB

NokiaTermBank
Uneven Conversations
0
25
50
75
100
Customers Vendors
Very
Knowledgeable
Not
Knowledgeable
T
e
c
h
-
k
n
o
w
l
e
d
g
e
G
a
p

FTI Metadata Model
Build Your Own Metadata Repository

Sample Low-tech Repository
For each column …

Where does this key function as a primary key?
Where does this key function as a foreign key?

Where does this key function as an attribute?
Metadata yields …
Valuable information about your data assets:
• Do we have these specific (or this class of) data assets?
• Is this data item used elsewhere?
• What did cost to acquired this set of assets?
• Can these data assets be share securely?
• … (a model for how your information should be managed)
Yes!
Nowhere!
35¢/apiece
Not easily!

67
Program
The Importance of M
etadata
Architecture
• Things
– (components)
data structures
• The functions of the things
– (individually)
sources and uses of data
• How the things interact
– (as a system, towards a goal)
Efficiencies/effectiveness

How are components expressed as architectures?
A B
C D
A B
C D
A
D
C
B
Intricate
Dependencies
Purposefulness
• Details are
organized into
larger
components
• Larger
components are
organized into
models
• Models are
organized into
architectures
(comprised of
architectural
components)
How are data structures expressed as architectures?
• Attributes are organized into entities/objects
– Attributes are characteristics of "things"
– Entitles/objects are "things" whose
information is managed in support of strategy
– Example(s)
• Entities/objects are organized into models
– Combinations of attributes and entities are
structured to represent information requirements
– Poorly structured data, constrains organizational
information delivery capabilities
– Example(s)
• Models are organized into architectures
– When building new systems, architectures are used to plan development
– More often, data managers do not know what existing architectures are and -
therefore - cannot make use of them in support of strategy implementation
• Why no examples?
Intricate
Dependencies
Purposefulness
THING
Thing.Id #
Thing.Description
Thing.Status
Thing.Sex.To.Be.Assigned
Thing.Reserve.Reason

Data architectures are composed of data models
Design Patterns
• Why are the restrooms in the same place on each floor?
• What about the electrical wiring?
• HVAC? Floorplans? ...
• Architecture design patterns (spoke and hub,
hub of hubs, warehouse, cloud, MDM,
changing tires, portal)

Meta Data Models
Metadata Data Model
SCREEN
ELEMENT
screen element id #
data item id #
screen element descr.
INTERFACE
ELEMENT
interface element id #
data item id #
interface element descr.
INPUT
ELEMENT
input element id #
data item id #
input element descr.
OUTPUT
ELEMENT
output element id #
data item id #
output element descr.
MODEL
VIEW
model view element id #
data item id #
model view element des.
DEPENDENCY
dependency elem id #
data item id #
process id #
dependency description
CODE
code id #
data item id #
stored data item #
code location
INFORMATION
information id #
data item id #
information descr.
information request
PROCESS
process id #
data item id #
process description
USER TYPE
user type id #
data item id #
information id #
user type description
LOCATION
location id #
information id #
printout element id #
process id #
stored data items id #
user type id #
location description
PRINTOUT
ELEMENT
printout element id #
data item id #
printout element descr.
STORED DATA ITEM
stored data item id #
data item id #
location id #
stored data description
DATA ITEM
data item id #
data item description
74

Business Goals Model
Defines the mission of the
enterprise, its long-range goals,
and the business policies and
assumptions that affect its
operations.
Business Rules Model
Records rules that govern the
operation of the business and the
Business Events that trigger
execution of Business Processes.
Enterprise Structure Model
Defines the scope of the enterprise
to be modeled. Assigns a name to the
model that serves to qualify each
component of the model.
Extension Support Model
Provides for tactical Information
Model extensions to support special
tool needs.
Info Usage Model
Specifies which of the
component instances are used by
other Information Model
components.
Global Text Model
Supports recording of extended
descriptive text for many of the
Information Model components.
DB2 Model
Refines the definition of a Relational
Database design to a DB2-specific
design.
IMS Structures Model
Defines the component structures
and elements and the application
program views of an IMS Database.
Flow Model
Specifies which of the Entity
Relationship Model component
instances are passed between
Process Model components.
Applications Structure Model
Defines the overall scope of an automated
Business Application, the components of the
application and how they fit together.
Data Structures Model
Defines the data structures and their
elements used in an automated
Business Application.
Application Build Model
Defines the tools, parameters and
environment required to build an
Derivations/Constraints Model
Records the rules for deriving legal
values for instances of
components, and for controlling the
use or existence of E-R instance.
Defines the Business Entities, their
properties (attributes) and the
relationships they have with other
Business Entities.
Organization/Location Model
Records the organization structure
and location definitions for use in
describing the enterprise.
Process Model
Defines Business Processes, their
sub processes and components.
Relational Database Model
Describes the components of a
Relational Database design in
terms common to all SAA
relational DBMSs.
Test Model
Identifies the various file (test
procedures, test cases, etc.)
affiliated with an automated
business Application for use in
testing that application.
Library Model
Records the existence of
non-repository files and the role they
play in defining and building an
Panel/Screen Model
Identifies the Panels and Screens and
the fields they contain as elements
used in an automated Business
Application.
Program Elements Model
Identifies the various pieces and
elements of application program
source that serve as input to the
application build process.
Value Domain Model
Defines the data characteristics
and allowed values for
information items.
Strategy Model
Records business strategies to
resolve problems, address goals,
and take advantage of business
opportunities. It also records
the actions and steps to be taken.
Resource/Problem Model
Identifies the problems and needs
of the enterprise, the projects
designed to address those needs,
and the resources required.
Process Model
Extension
Support Model
Application
Structure
Model
DB2 Model
Relational
Database
Model
Global Text
Model
Strategy
Model
Derivations/
Constriants
Model
Application
Build Model
Test Model
Panel/ Screen
Model
IMS Structure
Model
Data
Structure
Model
Program
Elements
Model
Business
Model
Goals
Organization/
LocationModel
Resource/
Problem
Model
Enterprise
Structure
Model
Entity-
Relationship
Model
Info Usage
Model
Value Domain
Model
Flow Model
Business
Rules Model
Library
Model
IBM's AD/Cycle Information Model
Metadata for Semistructured Data
• Metadata describes both structured
and semi-structured data
– You cannot convert unstructured data into
structured data
– Better description: Non-tabular data ➜
tabular data
• Semi-structured data
– Any data that is not in a database or data
file, including documents or other media
data
• Metadata for semi-structured 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
• 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)

Metadata patterns yield …
Valuable comparisons and 'starting foundations':
• Do we have to create a pharmacy billing system from scratch?
• Will the proposed software 'fit'?
• Do industry best practices exist?
• Has anyone published a model implementing GPDR?
No!
Yes!
Yes
Not yet!
78
Program
The Importance of M
etadata

+
• 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
Why Metadata Matters
"It's just
metadata"
https://anythingawesome.com 80
Envera Business Value Proposition

The Real Value of Metadata
OPEN Government Data Act
• Signed on 1/14/19
• Foundations for
Evidence-Based
Policymaking (FEBP)
Act (H.R._4174,_S._2046)
• Title II, which includes the
Open,
Public,
Electronic, and
Necessary (OPEN)
Government Data Act (Title II)
– All federal data is open by default
– Non-political CDOs are required
– Use of open data and open
models required in policy
evolution
– Penalties are higher than HIPPA
https://www.congress.gov/bill/115th-congress/house-bill/4174/text

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.
Program
The Importance of M
etadata

• 'Data about data'
• Metadata unlocks the value
of data, and therefore requires
management attention [Gartner]
• Metadata is less about what and more about how
• Metadata must be the language of data governance
• Metadata defines the essence of most business challenges
• Should we include this data item within
the scope of our metadata practices?
Metadata Take Aways
References & Recommended Reading

References, cont’d

Upcoming Events
Necessary Prerequisites to Data Success:
Exorcising the Seven Deadly Data Sins
9 November 2021
Data Management vs. Data Governance Program
14 December 2021
Data Strategy Best Practices
11 January 2022
Brought to you by:
Time: 19:00 UTC (2:00 PM NYC) | Presented by: Peter Aiken, PhD
Note: In this .pdf, clicking any webinar title opens the registration link

Event Pricing
• 20% off
directly from the publisher on
select titles
• My Book Store @
http://plusanythingawwsome.com
• Enter the code
"anythingawesome" at the
Technics bookstore checkout
where it says to
"Apply Coupon"
anythingawesome

Peter.Aiken@AnythingAwesome.com +1.804.382.5957
Thank You!
Book a call with Peter to discuss anything - https://anythingawesome.com/OfficeHours.html
+ =

What is a Data Catalog?
Data Catalog: Model (Ontology)
Dataset
Party
Data
Elements
Format
Periodicity
Concept
Dataset
Dataset
Group
frequency
part of
format
derived
from
publisher
element of
topic
License Type
licensed under

Data Catalog: Controlled Vocabularies
Dataset
Party
Data
Elements
Periodicity
Concept
Dataset
Dataset
Group
frequency
part of
format
derived
from
publisher
element of
topic
License Type
licensed under
Format
Data Catalog: Actual Statements
Dataset001
Acme
Loan ID
CSV
Quarterly
Loans
Dataset002
Loan
Reports
frequency
part of
format
derived
from
publisher
element of
topic
Some License
licensed under

§ A Knowledge Graph represents a knowledge domain
§ It represents knowledge as RDF graphs
– A network of nodes and links
– Not tables of rows and columns
– Uses RDF graph data model
§ It represents facts (data) and models (metadata)
in the same way
– Flexible and extensible
– Rich rules and inferencing
§ It is based on open standards, from top to bottom
– Readily connects to knowledge in private and public clouds
There can be different types and instances of Knowledge Graphs
What is a Knowledge Graph?
Value Proposition for Knowledge Graph
▪ Flexibility – can express any data and metadata
▪ Composability – ease of incremental evolution
▪ Connectivity - bridges all data and metadata
“silos” for seamless data governance
▪ Future proof - based on standards
▪ Expressivity - the most comprehensive “out of
the box” models
▪ Integrability - the most complete, open and
flexible APIs
▪ Smart - integrates reasoning and machine
learning
Delivers Knowledge-driven
Data Governance

What is a Data Fabric?
“In a data fabric approach, one of the most important components is the
development of a dynamic, composable and highly emergent knowledge
graph that reflects everything that happens to your data. This core concept
in the data fabric enables the other capabilities that allow for dynamic
integration and data use case orchestration. It then renders all of the
metadata available through a catalog interface.”
Gartner, “How to Activate Metadata to Enable a Composable Data Fabric”, July 2020
The use of knowledge graph technology makes it possible
for a data catalog to support the data fabric.
Knowledge Graph based Data Catalog is a
Critical Component of the Data Fabric Design
dynamic, composable and highly emergent knowledge
catalog interface”.
graph

TOPQUADRANT COMPANY
Established in 2001, offers market’s most
complete and mature Information Governance
solution based on Knowledge Graphs
TOPBRAID EDG
⎼Released in 2016
⎼Multiple packages
⎼Available on premise and as SaaS
TOPBRAID COMPOSER
⎼Released in 2006
About TopQuadrant 200+
Customers
70+
TopBraid EDG
Customers
of the customers
are Fortune 500
40%
of the customers
are Public listed
60%
THOUGHT LEADERSHIP
§ Strong commitment to standards-
based approaches to data semantics

Resources
To Learn More:
§ Contact us at: info@topquadrant.com
Short Videos:
§ TopBraid EDG “Quick Grok” Videos
https://www.topquadrant.com/knowledge-assets/videos/
§ TopBraid EDG Animated Video
https://www.topquadrant.com/project/edg_agile_modular/
Blog:
§ https://www.topquadrant.com/the-semantic-ecosystems-journal/
Webinar Recordings, Slides, Q&A:
§ https://www.topquadrant.com/knowledge-assets/topquadrant-webinars/
New White Paper
§ https://www.topquadrant.com/knowledge-assets/whitepapers/

Essential Metadata Strategies

More Related Content

What's hot (20)

Similar to Essential Metadata Strategies (20)

More from DATAVERSITY (20)

Recently uploaded (20)

Essential Metadata Strategies