Geek Sync | Data Architecture and Data Governance: A Powerful Data Management Duo

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Data Architecture and Data
Governance: A Powerful
Data Management Duo
April 3, 2019
Presented by: Kelle O’Neal

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Hello!
§ Kelle is a veteran leader and accomplished advisor in
the enterprise information management sector. She is
also a sought-after industry thought leader, public
speaker, author and trainer. Kelle’s strong background in
customer relationship management, enterprise software
and systems integration uniquely positions her to excel
in helping organizations of all sizes and complexities
successfully execute on Data Governance,
Organizational Change Management, Master Data
Management, Data Insights and Analytics and other
information management initiatives.
Kelle O’Neal
Founder & CEO
© 2019 First San Francisco Partners | All rights reserved | www.firstsanfranciscopartners.com

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By the end of this webinar, you’ll know…
§ Why aligning data architecture and data governance is important
§ The key intersections of people, processes and technology between
data architecture and data governance
§ How data architecture and data governance work together to
enforce standards
§ The capabilities that data governance can apply to data architecture
without interfering
§ How your project/development methodologies can drive alignment

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What is Data Architecture?

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Data Architecture and Information Architecture defined
DATA ARCHITECTURE
§ The capability to plan for and
manage the structure of
environments where data is stored
from an enterprise perspective. This
not only covers data and data stores,
but also related components,
services and metadata stores. Data
movement and integration is
included in Data Architecture.
INFORMATION ARCHITECTURE
§ The capability to understand and
manage business information as
such, without any consideration
about how it will be stored (or not
stored) as data. Efforts typically
focus on semantics, taxonomies,
classification, hierarchies, business
rules, business views, conceptual
models, and ontologies.

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Scope of Data Architecture practice areas
§ Data Architecture is a collection of different practice areas
§ Our clients tend to focus on just a few of these at a time
Components and Services
(including tools)
Data
Environments
Data Layers
Data Stores
Conceptual
Model Stds
Data
Classification
Business
Views and
Ontologies
Data
Standards
Data Model
Standards
Data
Movement
Data
Integration
Subject Area
Models
Global
Hierarchies
Business Definition and other
Metadata

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Primary goals of Data Architecture
§ Promote extraction of business value
from the enterprise data resource
§ Reduce enterprise data asset
complexity making it easier to maintain
§ Make future changes easy to support
§ Support efficiency
§ Mitigate operational risk
§ Promote data management maturity
Facilitate data sharing and integration
Promote implementation patterns for
different settings
Enforce standards
Separate concerns – no tight coupling
Support component reuse
Support security, privacy, retention

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Why Data Architecture is important
Internal pressures:
§ Desire to understand customer at any
time from any channel
§ Data Quality issues are persistent
§ Balance of old mainframe systems
with new technologies
§ Movement to the cloud and losing
control of data
§ Data Volumes are increasing
§ Mobile apps enabling data to be
created and accessed anywhere
§ Project oriented approach to
addressing issues/opportunities
External pressures:
§ Greater amounts of new data regulations
§ Increasing Customer Demands – my
information anywhere at any time
§ Technology and market changes outpacing
ability to respond

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What is Data Governance?

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Data Governance defined
§ Data Governance is the organizing
framework for establishing the strategy,
objectives and policy for effectively
managing corporate data.
§ It consists of the processes, policies,
organization and technologies required to
manage and ensure the availability,
usability, integrity, consistency,
auditability and security of your data.
Communication
and Metrics
Data
Strategy
Data Policies
and Processes
Data
Standards
and
Modeling
A Data
Governance
Program consists of
the inter-workings
of strategy,
standards, policies
and communication

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Why Data Governance is important
Data must be available and accessible
• Consistent, cohesive, standardized
Trusted data must be high quality and integrity
• Created, recorded, reported in compliance with all standards and regulations
Timely analysis and decisions
• No wasted time collecting, integrating, manipulating data
• No wasted time researching which report is correct
• Need for sustained and sanctioned data decision making is growing swiftly
Effective Business Actions
• Improve operating performance
• Increase competitive advantage
Optimized business results
• Companies with effective governance processes are said to generate over 40%
higher ROI on their IT investments than their competitors*
Optimized
Business Results
Data Availability
and Accessibility
Trusted
Data/Information
Timely Analysis
and Decisions
Effective
Business Actions

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Alignment of Data Architecture and
Data Governance

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Common Obstacles
§ Competing priorities and lack of resources
§ Data Ownership and other territorial issues
§ Lack of cross-business unit coordination
§ Lack of understanding
§ Resistance to change or transformation
§ Lack of executive sponsorship and buy-in
§ Resistance to accountability
§ Lack of business justification
§ Inexperience with cross-functional initiatives
§ Change of personnel
Effort
Control

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Aligning data architecture and data governance
DATA GOVERNANCE
§ Organizing framework for
establishment and
enforcement (rules of the
road) of the strategy,
objectives, policies,
procedures, and standards
to effectively manage data …
§ Supports enterprise
standards
DATA ARCHITECTURE
§ Applies rules of the road to
effectively drive data creation
through an organization’s
solution development life
cycle(s) to deliver quality data
products to functional business
units
§ Identifies new governance
opportunities and requirements

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Why Data Architecture and Data Governance alignment matters
§ Ensure doing the right things and doing them right
- Right projects and right priorities
- Right data
§ Reduce time to value (market) – affect bottom line
§ Accelerate Data Architecture maturity
§ Reduce costs (reduced headcount multiple roles
trained to perform DG tasks)
§ Increase data architects’ morale!

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Shared governance
What are some of the key
components or artifacts that
comprise what can be
governed?
§ Guiding Principles
§ Policies
§ Standards
§ Procedures (aka processes)
§ Data Governance provides direction over
how Data Architecture is implemented …
thus …
§ Data Governance and Data Architecture
should share the same set of governance
artifacts
• Data Architecture should inherit Data Governance artifacts
• Data Architecture will most likely require additional
governance artifacts for operationalizing DA processes
and creation and management of DA deliverables; e.g.,
Data Model Management (version control)

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Alignment – shared governance (not shared)
Guiding Principle
§ There will be common (enterprise) definition
standards for all data, information, and
content that will be shared appropriately
across the enterprise
Policy
§ There will be authorized data sources and
single versions of the truth for specific data
subject areas (data domains) – all users
will use authorized sources only and should
not accept alternative sources as the
accurate version of the truth for that specific
data subject area
Guiding Principle
§ Each business area and/or application
team may develop their own definition
content to meet their local needs
Policy
§ Application teams should try to copy data
and definitions from like versions of data to
try and keep them the same, as they create
new versions of same data for their unique
applications – any copies, though, can be
changed to meet local needs
Sample Data Governance Sample Data Architecture Governance

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Alignment – shared governance (not shared)
Guiding Principle
§ There will be common (enterprise) definition
content that will be shared appropriately
across the enterprise
Policy
§ There will be authorized data sources and
single versions of the truth for specific data
subject areas (data domains) – all users
will use authorized sources only and should
not accept alternative sources as the
accurate version of the truth for that specific
data subject area
Guiding Principle
§ See data governance guiding principles
Policy
§ See data governance policies
§ Data model management – All data models
will be developed using approved
enterprise standard data modeling tool(s)
Sample Data Governance Sample Data Architecture Governance

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Alignment – shared governance
DATA GOVERNANCE DATA ARCHITECTURE
Should be a win-win
for Company AND you
… we’ll drive with you
on the road to
success!
Thanks for the
directions! Can’t wait
to get in the driver’s
seat!
Guiding Principles
Policies
Standards
Procedures

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Alignment: Operating Models
§ A centralized Data Governance Office (DGO) is accountable for development
and delivery of the Data Governance function
Data Architecture
Alignment Opportunities

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IMSC
CG Data Governance Council (DGC)
CRM ERP EPP
Enterprise
Department – Local Data Governance (LDG)
Example
Projects /
Applications
EPMO
Architecture
Review
Board
Projects and
Programs
Existing Structures
Metadata
1% at the IMSC
Escalation
resolution
19% at the DGC
80% at the LDG
Alignment: Council Engagement Data Architecture
Alignment Opportunities

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Technology Intersections

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Alignment – Technologies
What are some common relevant technologies?
§ Business Glossary
§ Metadata Repository
§ Risk/Issue Management
§ Data Profiling
§ Master Data Management
§ Reference Data Management
§ Data and Process Modeling
§ Data Movement/Integration
§ EA Modeling

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Technology alignment
Biggest alignment opportunity is collaboration and
joint discovery and decision-making
§ Example: Identifying need for and developing
business case for relevant technology
§ Example: Partnership developing and prioritizing
requirements for relevant technology evaluation and
selection
§ Example: Partnership in development of education
and training for application of technology (to apply
DG and DA processes)

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Project Engagement

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Systems Development Life Cycle (SDLC) vs Data-centric Projects
Requirements
Analysis
Design
Development
Quality Assurance
Production
Post-Production
1. SDLC presumes there is a process to be automated
In a data-centric project the starting point is
existing production dataBUT
2. Business Analysts expect users to tell them all their requirements
Users never understand the data at the
outsetBUT
3. The SDLC is a waterfall (even if done as agile)
On a data-centric project there are true cycles of
iteration as understanding of the source data evolvesBUT
4. The SDLC QA phase only tests functionality, not data
On a data-centric project data quality needs to be testedBUT

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Introducing the Data-centric
Development Life Cycle (DCLC)
§ It recognizes the specific activities
needed for a data-centric project
instead of abstracting them into over-
generalizations like “analysis.”
§ It provides for real iterations that lead
to refinement of information
requirements, instead of a single
requirements activity.
§ It understands that some activities
can be carried out in parallel, instead
of the linear flow envisaged by both
the SDLC and Agile.
INFORMATION
REQUIREMENTS
DATA
DISCOVERY
DATA
PROFILING
QUALITY
ASSURANCE
PRODUCTION
POST-
PRODUCTION
TARGET DESIGN
DEVELOPMENT
SOURCE
TARGET
MAPPING

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Metadata governance sub-track within data track
* CRUD
• Create
• Read/Report/Query
• Update
• Delete
• Focus on research
• Impact analysis
• Lineage
• Data owners/SMEs
• Data transformation
• Source:target maps
• DBMS software
• ETL software
Requirements
and Analysis
Design and
Construction
Testing and
Verification
Implementation
Prod Support
Maint/Enhance
Ideation/Initiation
Solution Development Life Cycle
• Focus on research
and creation
• Impact analysis
• Lineage
• Definitions/rules
• Data quality
• Focus on update and
reporting
• Data definitions
• DBMS technical doc
• Focus on update and
reporting
• Business rules and
validation
• Focus on reporting
• Impact analysis
• Lineage
• DBMS tech doc
• Focus on reporting
• Impact analysis
• Lineage
• DBMS technical doc
Metadata
Repository
Business
Metadata
Technical
Metadata
CRUD* CRUD*
• Application Developer
• Application Technical Analyst
• Data Custodian
• DBA
• Project Manager
• DG Analyst
• Data Steward
• Metadata Analyst
• Data Architect/Analyst
• Data Quality Analyst

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Some elements of DCLC are needed for all projects
§ The full DCLC is appropriate for projects that are heavily data-centric
§ However, even projects that are overwhelmingly process-centric can benefit from some elements of the DCLC
§ This is because process-centric projects will be creating data that may be used in the future in some analytics
environment (that may not even exist yet)
100%
Process
-centric
100%
Data-
centric
Requirements
Analysis
Design
Development
Quality Assurance
Production
Post-Production

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Critical success factor – practical execution
§ Nature of roll out can vary from a narrow focus to immediate enterprise-wide change
§ At all times the context is ENTERPRISE
Focus on DG/DM
practices at local or
project level
Narrow Focus Broad Focus
Compliance-driven
DG and Data
Management
Domain
specific DG for
MDM
Reference
data to
support
analytics

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Critical success factor – practical execution
§ Nature of roll out can vary from a narrow focus to immediate
enterprise-wide change
§ At all times the context is ENTERPRISE
Focus on DG/DM
practices at local or
project level
Narrow Focus Broad Focus
Compliance-driven
DG and Data
Management
Domain
specific DG for
MDM
Reference
data to
support
analytics
“Minimally invasive”
can work
Need to balance
“invasive”
aspects with
business needs
DG and DA begin
to become
perceived as
necessarily
invasive
DG and DA become
invasive – “it’s just the
way we do things”

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Questions?
kelle@firstsanfranciscopartners.com
Or visit firstsanfranciscopartners.com
Thanks for joining today!

Geek Sync | Data Architecture and Data Governance: A Powerful Data Management Duo

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Geek Sync | Data Architecture and Data Governance: A Powerful Data Management Duo