Watch the companion webinar at:http://embt.co/CollabDL As a data professional, you should be reviewing the enterprise use of common business terms in your organization to assess the breadth of use (and potential misuse) of the enterprise business glossary. While that work by itself does not resolve differences via harmonization or establish standards across the organization, documenting the reference data and data element metadata is the first step in sharing knowledge about business terms and identifying the business impacts of variant use, the numerous places the business terms are interpreted in different processes, and the degradation of corporate value. In this webinar, David Loshin will discuss the concept of collaboration for establishing a chain of definition that is both transparent to the entire user community yet leverages a virtual approach to “crowdsourcing” metadata standardization that helps to: + Map agreed-to definitions to the different uses in the different data sets and applications + Enable consistent interpretation of data element meanings from an end-to-end perspective + Reduce and eventually eliminate misunderstandings about business term semantics + Provide a means for analyzing impacts of evolving or changing definitions

1. Using Collaboration for
Metadata, Semantics, and Lineage
David Loshin
Knowledge Integrity, Inc.
www.knowledge-integrity.com
1© 2014 Knowledge Integrity, Inc.
www.knowledge-integrity.com
(301)754-6350

2. Design in a Vacuum
p Original application design and
development executed by project
teams independently supporting
lines of business
p Function-oriented application
design
n Multiple, inconsistent uses of the
same or similar terms
n Few or no controls on structural
design
n Little interaction regarding
consistency of semantics
p Variation of content meanings in
different contexts creates
difficulty in information integration
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3. Resolution, After-the-Fact?
p As data sets stream from
operational systems into a
data warehouse, the
impacts of variation are
magnified
n Aggregations may reflect
questionable results
n Inconsistencies propagate
into reports and analyses
n Questions are raised about
the trustworthiness of the
data
p Often the data is not
“wrong,” just misinterpreted
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Data Warehouse
Data
Integration

4. Unstructured Data and Misunderstanding Semantics
p Outside of a structured context, character strings may have
ambiguous meanings
n #MDM = Master Data Management or Mobile Device Management?
p As we seek to incorporate more data into the enterprise, the
need for semantic consistency grows
Yay, 4 more years of obama
Yay, 4 more years of obama!
Yay, 4 more years of obama #sarcasm
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5. Resolving Semantic Variance
Metadata
Management
HarmonizationGovernance
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But each by itself will not address the challenges

6. © 2014 Knowledge Integrity, Inc.
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Metadata Management
Concepts
Business
Terms
Definitions Semantics
Business Definitions
Conceptual
Domains
Value
Domains
Reference
Tables
Mappings
Reference Metadata
Critical
Data Elements
Data Element
Definitions
Data Formats Aliases/Synonyms
Data Elements
Entity Models Relational Tables
Master Object
Directory
Information Architecture
p Metadata repositories are good for managing the inventory but
n They require significant start-up effort
n They remain useful only as long as the metadata is current
n They do not always present the best opportunities for accessibility

7. Data Harmonization
p Harmonization drives consistency but is difficult without an
inventory of data elements and corresponding lineage
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Identify inconsistent
definitions, conflicts
in data domains,
format variations
Extract data definitions
from current guidance
documents, etc. and
categorize definitions
using standard
terminology
Integrate data
elements into a
single reference
source, then
combine common
data elements
Identify authoritative
source for definitions
Assign names using
Naming Convention
Document in
metadata Registry
Identify Anomalies
Resolve and
Standardize
Integrate & CollateExtract & Collect
Regulations
Policies
Documents
Forms

8. What is Data Governance?
p Mapping business value drivers to
data expectations and objectives,
granting oversight and
accountability, and verifying
performance of compliance with
corporate information policies
n Processes prescribed for operations,
n Procedures for day-to-day
observance
n Oversight for verifying compliance
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Accountability
Data Policies
Processes &
Best Practices
Information
Standards
Business
Policy
Roles and
responsibilities
Program management
Data policies and
standards
Business intelligence
Business terminology
Data quality
Auditability

9. Coming to Terms…
p Data Governance institutes the policies and procedures for
resolving conceptual differences about data semantics:
n Manage lineage to drive harmonization
n Active metadata management for defining data concepts
n Centralize metadata engagement
n Continuous attention to consistency
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(301)754-6350
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Map Lineage Differentiate Harmonize Assess Impact Communicate

10. Lineage for Tracking Use of Common Business Terms
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Business
Term
Use Definition
Data Element
Concept
Data
Element
Data
Element
Data Element
Concept
Data
Element
Data
Element
Data
Element
Use Definition
Conceptual
Domain
Value
Domain
Conceptual
Domain
Value
Domain
Conceptual
Domain
Value
Domain
Conceptual
Domain
Value
Domain
Conceptual
Domain
Value
Domain
Data
Element
Type
First VARCHAR(25)
Middle VARCHAR(25)
Last VARCHAR(30)
SocialSec CHAR(9)
Data
Element
Type
FirstName VARCHAR(35)
LastName VARCHAR(40)
SSN CHAR(11)
Telephone VARCHAR(20)

11. Impact Analysis and Data Lineage
p A collaborative platform allows
interested individuals to track those
business terms, data element concepts,
and reference metadata relevant to their
business processes
p A comprehensive directory links usage
and definitions to processes and
applications
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Social
Security
Number
Identifier
Unique number assigned by
Social Security Administration
Authentication
Last four digits of number
assigned by the Social
Security Administration
Identifier
Unique number assigned by
the company
Customer ID

12. Active Metadata Management
p The root cause of data semantic
variance is isolated design
p Eliminate variance by providing a
shared framework for posting and
discussing semantics
p Enable collaboration about
n Business terms
n Definitions
n Conceptual domains
n Value domains
n Data element concepts
n Data elements
n Entities
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Metadata

13. Facilitate Agreement About Definitions
p Logging business terms
and their definitions in a
centralized repository
provides a shared resource
for definitions
p Interaction among
interested parties drives
continuous resolution
p New application
development can rely on
existing definitions with
existing “community
support”
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14. Metadata Becomes the Hub for Agreement
Shared
Metadata
Repository
Transaction
Processing
Operational
Systems
Querying
and
Reporting
Advanced
Analytics
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15. Check Out These Resources!
p www.knowledge-integrity.com
p www.dataqualitybook.com
p If you have questions,
comments, or suggestions,
please contact me
David Loshin
301-754-6350
loshin@knowledge-integrity.com
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www.knowledge-integrity.com
(301)754-6350
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