DataEd Webinar: Metadata Strategies

Copyright 2019 by Data Blueprint Slide # 1
Peter Aiken, Ph.D.
F
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Strategies
M
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Practices
• DAMA International President 2009-2013 / 2018
• DAMA International Achievement Award 2001
(with Dr. E. F. "Ted" Codd
• DAMA International Community Award 2005
• I've been doing this a long time
• My work is recognized as useful
• Associate Professor of IS (vcu.edu)
• Founder, Data Blueprint (datablueprint.com)
• DAMA International (dama.org)
• CDO Society (iscdo.org)
• 11 books and dozens of articles
• Experienced w/ 500+ data
management practices worldwide
• Multi-year immersions
– US DoD (DISA/Army/Marines/DLA)
– Nokia
– Deutsche Bank
– Wells Fargo
– Walmart …
Peter Aiken, Ph.D.
PETER AIKEN WITH JUANITA BILLINGS
FOREWORD BY JOHN BOTTEGA
MONETIZING
DATA MANAGEMENT
Unlocking the Value in Your Organization’s
Most Important Asset.
2Copyright 2019 by Data Blueprint Slide #

About Infogix
• Innovating data solutions since 1982
• Data Governance; Data Quality and Data Analytics
• Large and mid-size customers world-wide:
• Organizations rely on Infogix so they can trust their
data
• Average customer tenure > 18 years
“ I n d u s t r i e s t h a t t h r i v e o n d a t a ”

1. It is not just the technical metadata
• Technical Metadata is generally the easy metadata
• Labelling data to preserve context, provenance and semantic relatedness
is harder – and more valuable
2. Do not be afraid of the detail
• Data has metadata; metadata has metadata (meta metadata); and…
3. Do not forget the governance metadata
• Your governance metamodel supports scaling, accountability,
transparency, etc.
My top 5 Observations from our Customers

3
4. A good data model is not enough model
Traditional enterprise
data models can
capture many
relationships, but is the
data they store:
•Discoverable?
•Understandable?
•Accessible?
•Usable?

5. Metadata is
critical to the future
state “vision”
If your organization’s
data is to support
transformative
change
You will have
more metadata
than data!
Also without metadata
no Ai is going to get this
joke!

Copyright 2019 by Data Blueprint Slide #
Metadata Strategies: Foundational Practices
• 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?
– Specific teachable example using iTunes
1. Metadata is a gerund so don't try to treat it as a noun
– Metadata is a use of data, not a type of data
2. Metadata is the language of data governance
– Make metadata the language of data governance
3. Treat glossaries/repositories as capabilities not technology
– Cyclic approaches do not start with technologies
• 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
• 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"
Meta data, Meta-data, or metadata?Meta data, Meta-dataMeta data

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.
Uses
What is data management?
Sources
Data
Engineering
Data
Delivery
Data
Storage
Data
Assets
(optimized)
Insight
Note: does not well-depict data reuse
"Understanding the current and future data needs
of an enterprise and making that data effective
and efficient in supporting business activities."
Reuse
Specialized Team Skills
Data Governance

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)
Another perspective
HR Head
| 
––––––––––––
Section Heads
| 
–––––––––––––––––––––
(more) Group Heads
| 
––––––––––––––––––––––––––––––––––
(many) Individuals
and in this corner - Dave!

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
4. a. Beyond; transcending; more comprehensive: metalinguistics.
b. At a higher state of development: metazoan.
Hedy Lamarr
• Google celebrated her
101st Birthday on 11/8/2015
– Tablets for fizzy drinks
– Improved stop light design
• Invention of “frequency
hopping” radio
– By jumping from one radio
frequency to another rapidly,
only a receiver that shares the
key can find the transmission
– Prevent interference with the radio
guidance controls of torpedoes
• U.S. Patent 2,292,387 (w/ George Antheil)
• Associated traffic analysis
– Looking at other elements of a communication
when you don’t know the actual content
– Time/duration of a message
– Location of transmitters
– Detect specific operator “fists”
– Identify the operator and you could identify a
specific ship or military unit, locate it with
direction finding, and then track its activity over time
https://theconversation.com/how-wwi-codebreakers-taught-your-gas-meter-to-snitch-on-you-29924

Data
About
Data
Metadata yields …
Sources
Data
Engineering
Data
Delivery
Data
Storage
Data
Assets
(optimized)
Data Governance
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!
Reuse

DataManagement
BodyofKnowledge(DMBoK)
DataManagement
BodyofKnowledge(DMBoKV2)

Metadata Management
from The DAMA Guide to the Data Management Body of Knowledge © 2009 by DAMA International
• 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
• To organize iTunes
– I create a "New Smart Playlist"
for Artist's containing "Miles
Davis"
Example:
iTunes Metadata

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
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 …
Sources
Data
Engineering
Data
Delivery
Data
Storage
Data
Assets
(optimized)
Reuse
Data Governance
Valuable information about your iTunes assets:
• Do we have these specific Miles Davis recordings?
• Most my played Miles Davis recording
• Can I listen to the entire album before dinner?
Yes!
Bitches Brew
2¢/each
Not easily!

As a topic, Data is ...
Wally Easton Playing Piano
https://www.youtube.com/watch?v=NNbPxSvII-Q
Complex & detailed
• Outsiders do not
want to hear about
or discuss any
aspects of
challenges/solutions
• Most are unqualified
re: architecture/
engineering
Taught inconsistently
• Focus is on
technology
• Business impact is
not addressed
Not well understood
• Lack of standards/
poor literacy/
unknown
dependencies
• (Re)learned by
every
workgroup
• 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?"

• Examples of information architecture achievements that happened
well before the information age:
– Page numbering
– Alphabetical order
– Table of contents
– Indexes
– Lexicons
– Maps
– Diagrams
Pre-Information Age Metadata
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."
Remove the structure and things fall apart rapidly

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
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

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

Why data structure problems have been difficult?
Student
Data
Base
Master

Proposed
Data Model
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?

Keep the proper focus
• Wrong question:
– Is this metadata?
• Right question:
– Should we include this
data item within the
scope of our
metadata
practices?

Data
Data
Data
Information
Fact Meaning
Request
[Built on definitions from Dan Appleton 1983]
Intelligence
Strategic Use
Data
Data
Data Data
“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
A Model Defining 3 Important Concepts
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
Encountergoverneddatamoredirectly←|→Encountergoverneddatalessdirectly
Moretimeisdedicated←|→Lesstimeisdedicated
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/SystemsDevelopment
Data/feedback
Changes
Action
R
esources
Ideas
Data/Feedback
Components comprising the data community
DIP Implementation
DataLeadership
Feedback
Feedback
Data
Governance
Data
Improvement
DataStewards
DataCommunityParticipants
DataGenerators/DataUsers
Data
Things
Happen
Organizational
Things
Happen
DIPs
Data
Improves
Over
Time
Data
Improves As
A Result of
Focus

10 DG Worst Practices
1. Buy-in but not Committing:
Business vs. IT
2. Ready, Fire, Aim
3. Trying to Solve World Hunger or
Boil the Ocean
4. The Goldilocks Syndrome
5. Committee Overload
6. Failure to Implement
7. Not Dealing with Change
Management
8. Assuming that Technology Alone
is the Answer
9. Not Building Sustainable and
Ongoing Processes
10. Ignoring “Data Shadow Systems”

Metadata yields …
Sources
Data
Engineering
Data
Delivery
Data
Storage
Data
Assets
(optimized)
Reuse
Data Governance
Valuable information about your data assets:
• Do we have these specific (or this class of) data assets?
• What is the quality of …
• Can these data assets be provided more granularly?
Yes!
Not suitable!
35¢/apiece
Not easily!
Definition of Bed

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."
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
Draft
NTB

Uneven Conversations
0
25
50
75
100
Customers Vendors
Very
Knowledgeable
Not
Knowledgeable Tech-knowledgeGap
Student Activities File
• Question:
– Suppose we want to decrease the price of Swimming from $50 to $40?
• Answer:
– Change to data items such as ‘swimming’ requires examination of every single
record
– Will not catch spelling errors
– This is usually undesirable and unintended
Row
Student
ID
Activity Fee
2 150 Swiming $50
3 175 Squash $75
4 200 Swimming $40
5 327 Scuba $175
$40

Row Activity Activity
Code
Fee
1 Skiing TRF $200
2 Squash XGY $75
3 Swimming WRE $40
4 Scuba N2R $150
How Should it be Done? (Joining Tables)
Row Student ID Activity Code
2 150 WRE
3 175 XGY
4 200 WRE
5 327 N2R
...
(1 of these provides context for many of these)
• Data from the two tables is joined to provide requested information
• Student 150 is properly registered for swimming
• The inconsistent fee issue with swimming has been resolved
• Fee on orange table is a better engineered solution
Swimming WRE $40
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?

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
– Examples
• 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
– Examples
• 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

Design Patterns
• Why are the restrooms in the same place in each building?
• What about the electrical wiring?
• HVAC? Floorplans? ...
• Architecture design patterns (spoke and hub,
hub of hubs, warehouse, cloud, MDM,
changing tires, portal)
http://dmreview.com/article_sub.cfm?articleID=1000941 used with permission
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
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
automated Business Application.
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)

• 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
Shoshana Zuboff, The Age of Surveillance Capitalism
Who knows
Who decides and
Who decides, who decides?
Widespread Ignorance

EnveraBusinessValueProposition
The Real Value of Metadata

• Foundations for Evidence-Based
Policymaking Act (H.R._4174,_S._2046)
• Purpose: To amend titles 5 and 44,
United States Code,
1. to require Federal evaluation activities
2. improve Federal data management, and
3. for other purposes.
• Opposition?
– FEPA Gives Bureaucrats, Private Parties,
Hackers A Data Gold Mine
https://truthinamericaneducation.com/privacy-issues-state-longitudinal-data-systems/fepa-gives-
bureaucrats-private-parties-hackers-data-gold-mine/
– "A lifelong data citizen surveillance
system from coming into fruition"
https://youtu.be/Qr14ZmrQGu0
• Passed House 356–17 21-12-2018
• Passed Senate unanimously 21-12-2018
• Signed by the President 14-1-2019
FEPA/H.R. 4174
https://www.congress.gov/bill/115th-congress/house-bill/4174/text
Title I - Federal Evidence-Building Activities
• All federal agencies are required to:
– Manage its data according to industry best practices
– Regularly analyze the data
– Use the results to inform policymaking
• Future federal decision making process
– Specify in advance the open data sets that the
policy evaluation will consider
– Publish in advance the model that is proposed to
be used in the evaluation
– Policy may only be changed in ways supportable
by the evidence

Title II - Open Government Data Act
• Open
– Guidance To Make Data Open By Default
– Federal Agency Responsibilities To Make Data Open By Default
– Requires all non-sensitive government data to be made available in open and
machine-readable formats by default
• Data inventory and Federal data catalogue
– Comprehensive Data Inventory
– Public Data Assets
– Federal Data Catalogue
Title II - Open Government Data Act (continued)
• Establishes Chief Data Officers (CDO) at federal agencies
– Distinct from the CIO role
– Nonpolitical
– Objective qualifications
• Responsibilities
– Set standards for data formats, Negotiate terms for data sharing, and develop
processes for data publishing;
– Coordinate with any agency officials responsible for using, protecting,
disseminating, and generating data;
– Review the impact of agency IT infrastructure on data accessibility and
coordinate with agency Chief Information Officer to reduce barriers that inhibit
data accessibility;
– Maximize the use of data in the agency to support evidence-based analysis,
cybersecurity, and operational improvement;
– Acquire and maintain training and certification related to confidential information
protection and statistical efficiency; and
– Be responsible for overall data lifecycle management.

Title III - Confidential Information Protection and Statistical Efficiency Act
• Aka
– Confidential Information Protection and Statistical Efficiency Act of 2018
– Improve public perception of governmental information
• Definitions
– Agent (towards objective qualifications)
– Business data (wide scope)
• Coordination
– Breaches
– Reporting
– Processing
– Confidence
• Statistical Efficiency
– Improving interoperability's within Government
– Specifically for DSAs: Census, BEA & BLS
– Generally: BJS, BTS, ERS, EIA, NASS, NCES, NCHS
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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UsesSources
Metadata Governance
Metadata
Engineering
Metadata
Delivery
Metadata Practices
Metadata
Storage
• '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

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