DAS Slides: Data Governance and Data Architecture – Alignment and Synergies

Copyright Global Data Strategy, Ltd. 2020
Data Governance and Data Architecture –
Alignment and Synergies
Donna Burbank
Global Data Strategy, Ltd.
May 28th, 2020
Follow on Twitter @donnaburbank
Twitter Event hashtag: #DAStrategies

Global Data Strategy, Ltd. 2020
Donna Burbank
2
Donna is a recognised industry expert in
information management with over 20 years
of experience in data strategy, information
management, data modeling, metadata
management, and enterprise architecture.
Her background is multi-faceted across
consulting, product development, product
management, brand strategy, marketing,
and business leadership.
She is currently the Managing Director at
Global Data Strategy, Ltd., an international
information management consulting
company that specializes in the alignment of
business drivers with data-centric
technology. In past roles, she has served in
key brand strategy and product
management roles at CA Technologies and
Embarcadero Technologies for several of the
leading data management products in the
market.
As an active contributor to the data
management community, she is a long time
DAMA International member, Past President
and Advisor to the DAMA Rocky Mountain
chapter, and was awarded the Excellence in
Data Management Award from DAMA
International.
Donna is also an analyst at the Boulder BI
Train Trust (BBBT) where she provides advice
and gains insight on the latest BI and
Analytics software in the market. She was on
several review committees for the Object
Management Group’s for key information
management and process modeling
notations.
She has worked with dozens of Fortune 500
companies worldwide in the Americas,
Europe, Asia, and Africa and speaks regularly
at industry conferences. She has co-
authored two books: Data Modeling for the
Business and Data Modeling Made Simple
with ERwin Data Modeler and is a regular
contributor to industry publications. She can
be reached at
donna.burbank@globaldatastrategy.com
Donna is based in Boulder, Colorado, USA.
Follow on Twitter @donnaburbank
Twitter Event hashtag: #DAStrategies

DATAVERSITY Data Architecture Strategies
• January 23 Emerging Trends in Data Architecture – What’s the Next Big Thing?
• February 27 Building a Data Strategy - Practical Steps for Aligning with Business Goals
• March 26 Cloud-Based Data Warehousing – What's New and What Stays the Same
• April 23 Master Data Management – Aligning Data, Process, and Governance
• May 28 Data Governance and Data Architecture – Alignment and Synergies
• June 25 Enterprise Architecture vs. Data Architecture
• July 22 Best Practices in Metadata Management
• August 27 Data Quality Best Practices
• September 24 Data Virtualization – Separating Myth from Reality
• October 22 Data Architect vs. Data Engineer vs. Data Modeler
• December 1 Graph Databases: Practical Use Cases
3
This Year’s Lineup

What We’ll Cover Today
4
• Data Governance can have a varied definition, depending on the audience. To many, data
governance consists of committee meetings and stewardship roles.
• …To others, it focuses on technical data management and controls.
• Holistic data governance combines both of these aspects, and a robust data architecture and
associated diagrams can be the “glue” that binds business and IT governance together.
• This webinar will provide practical tips and hands-on exercises for aligning data architecture &
data governance for business and IT success.

Global Data Strategy, Ltd. 2020 5
A Successful Data Strategy links Business Goals with Technology Solutions
“Top-Down” alignment with
business priorities
“Bottom-Up” management &
inventory of data sources
Managing the people, process,
policies & culture around data
Coordinating & integrating
disparate data sources
Leveraging & managing data for
strategic advantage
Data Governance & Architecture are Part of a Wider Data Strategy
www.globaldatastrategy.com

Data Governance
Data Governance is critical in supporting the data-
driven business
• 76% have a current data governance initiative in
place or are planning one in the near future
• >50% identified improved collaboration
through using a defined data architecture
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Data Governance & Architecture improve collaboration and increase data accountability
* based on research from a 2019 DATAVERSITY survey on “Trends in Data Management” by Donna Burbank and Michelle McKnight

What is Data Governance?
The DAMA Data Management Body of Knowledge (DMBOK), defines data governance as the following:
• The exercise of authority, control and shared decision-making (planning, monitoring and
enforcement) over the management of data assets.
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DMBOK Definition
Exercise of authority, control Shared decision-making
Carrot
Yin
Yang

What is Data Architecture?
The DAMA Data Management Body of Knowledge (DMBOK), defines data architecture as the following:
• “Data Architecture is fundamental to data management. Because most organizations have more data
than individual people can comprehend, it is necessary to represent organizational data at different
levels of abstraction so that it can be understood and management can make decisions about it.
• … Data Architecture artifacts include specifications used to describe existing state, define data
requirements, guide data integration, and control data assets as put forth in data strategy. “
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DMBOK Definition

Global Data Strategy, Ltd. 2020 9
What my friends think I do
What I think I do
What my mom thinks I do
What my coworkers think I do
What society thinks do
DATA GOVERNANCE
What I actually do
Driving the
Success of
the Business

The Need for Data Governance
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Reducing Risk
(Defense)
Increasing Opportunity
(Offense)
Data Governance
Improving
Efficiency
Driving
Collaboration & Accountability
• Regulations require the need to protect & account
for customer data (e.g. GDPR, PCI)
• Actions & decisions driven by data require quality
& accountability of information.
• Data is a strategic asset and must be managed
accordingly for competitive advantage.
• Enabling a Data-driven organization through
quality, governed data.
• Well-governed data allows teams to spend less
time looking for, managing, and cleaning data,
and more time using it for strategic advantage.
• Governance helps reduce redundancy and
duplication of effort across systems and teams.
• Clear roles & responsibilities ensure that
individuals take active accountability for the
quality & protection of data.
• Teams work together to prioritize the best use of
data for the benefit of the organization and its
customers.

Data Governance as an Opportunity Driver
While many associate data governance with avoiding risk or complying with
regulation, data governance is also an opportunity driver.
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The “Carrot”
The “Stick”

“Offense” vs. “Defense”
• Focused on Creating Opportunity
• Improving Profitability
• Increasing Revenue
• Improving Customer Satisfaction
• Competitive Advantage
12
Which style of data governance fits your organization?
Offense Defense
• Focused on Reducing Risk
• Compliance & Regulation
• Avoiding Audits or Fines
• Fraud Detection
• Security & Privacy
On which end of the spectrum is your organization?

Find a Balance in Implementing Data Architecture
• Find the Right Balance
• Data Architecture projects can have the reputation for being overly “academic”, long, expensive, etc.
• No architecture at all can cause chaos.
• When done correctly, Data Architecture helps improve efficiency and better align with business priorities
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Focus on Business Value
Business Value
Too Academic, nothing
gets done
Too “Wild West”, nothing
gets done - chaos

The Diversity of Data Governance
• The Scope of Data Governance is diverse:
• From the “touchy feely” people side
• To the “detailed nerdy” technical side
• i.e. There is something for everyone….And everyone is likely to hate some part… ☺
14
Something for everyone…to love…or hate…or somewhere in between
I love working with
people!
I love creating
databases!

Applying a Structured Data Governance Framework
Organization &
People
Process &
Workflows
Data Management &
Measures
Culture &
Communication
Vision & Strategy
Tools & Technology
Business Goals &
Objectives
Data Issues &
Challenges

Building the Data Governance Framework
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Vision & Strategy
Organization &
People
Processes &
Workflows
Data Management &
Measures
Culture &
Communications
Tools & Technology
Is there a clear understanding
of the strategic goals of your
organization & the need for
enterprise data governance?
Who are the key data
stakeholders within and
outside your organization?
Do business process design
and operations management
take data needs into account?
Has key data been identified,
defined and analyzed?
Has the importance of data
been communicated across the
organization? Is there a data
communications plan?
Is there a coherent data
architecture in place to define
and guide how data is
captured, processed, stored
and used?
How does your organization
rely on data – now and in the
future?
Who are the primary data
producers, consumers &
modifiers?
Are there any specific data
management / improvement
processes in place?
Have data models been built –
conceptual / logical / physical?
Is the value of good data
management understood and
championed by senior
managers?
What primary IT systems and
platforms are used to store
and process key data?
What impact are data
problems currently having on
your organization?
Are individuals formally
accountable for data
ownership?
Are there issue and workflow
management processes to
address data problems?
Has the relationship between
business processes and data
been mapped?
Do all employees and third
parties receive data awareness
and improvement education
and training?
Do design gateways exist to
ensure data needs are taken
into account in new &
modified platforms?
Do you have a data governance
policy?
Are employees trained in good
data management practices?
Has there been any analysis of
the efficiency and
effectiveness of how data is
managed within operational
business processes?
Are data shortcomings known,
measured & recorded?
Are there communication
channels for communicating
best practice in data
management?
What specialist data
management tools are
currently in use?
What are the overall expected
benefits of better data
governance?
Are there any channels
through which data
shortcomings can be
highlighted and investigated?
How does the business and IT
interact to manage data
improvement?
Are there are formal standards
& rules specifying how data
should be managed and
improved?
Are there internal success
stories that could be used to
promote better data
management across the
organization?
What metadata is captured
and stored?

a number of respondents mentioned Data Governance in their comments as a way to align the various stakeholders around
common goals
Who is Driving Data Management in an Organization?
• While Technical Roles still lead Data
Management activities, Business
Stakeholders are playing a larger part.
• From those who listed “Other”, Data
Governance Lead was a common
response.
17
A number of respondents mentioned Data Governance as a way to align various stakeholders around common goals
* based on research from a 2019 DATAVERSITY survey on “Trends in Data Management” by Donna Burbank and Michelle McKnight

Data Stewardship
• Data Stewards are often found, not made.
• Often there are “hidden heroes” in the organization who are champions for data within their
organization.
• Many are pleased to finally have a “voice” and some official responsibility
18
Finding the right person for the job

Data Architect*
Data Governance Roles - Examples
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Executive Sponsor Business Data Owner Business Data Steward Technical Data Steward
Data Governance Lead*
• Promotes Data Driven Culture
• Champions Best Practices
• Advocate with ELT and Board
• Escalation Point for Key Issues
• Represents the data needs for a
particular functional area
• Defines key KPIs & data elements
• Defines key business rules
• Sets Data Quality Metrics &
Thresholds
Data Security Lead*
• Acts as a cross-functional lead for the data governance effort, working with both business and IT roles
• Chair of the Data Governance Steering Committee
* Typically a full-time role
• Responsible for the day-to-day
management and quality of data
• Subject Matter Expert (SME) for
a given business domain
• Aligns with the Data Owner to
support business rules and to
align with key KPIs
• Oversees the holistic data architecture for the organization, including data models, data standards, data integration, etc.
• Works with both business and technical stakeholders to ensure that systems implementations align with key business rules & needs
• Ensures that the organization adheres to the adequate security standards to support industry regulations and best practices
• Works with the Data Governance Lead and Data Architecture to ensure that data implementations support business needs in a secure way.
• Digital/IT expert for a given
business unit
• Subject matter expert for a given
system and its usage
• Aligns with Business Data
Stewards to ensure technical
needs are met
While there is no “one size fits all” approach to governance roles, the following are some examples:

Mapping Organizational Capability
Organizational Capability, Organizational Structure, and Roles are key to any Data Strategy
20
Aligning to Organizational Capabilities
e.g. From Plan to Production to Sales & Distribution
Designing Org Structures for Data-Centric Efforts
e.g. Aligning Data Governance to Individual Culture

Example: Federated, Global Manufacturing & Retail
21
Data
Innovation
Council
Supply
Chain
R&D
Etc
Etc
Operations
Consumer
Marketing
Data Architecture
Digital Transformation Team
“Digital Update”
Data Innovation Teams
Agile Project
Teams
Agile Project
Teams
Agile Development Cycles

Business Capability Models
• A business capability model outlines the core functional areas of the organization.
• Note: this is not the same as an organizational chart
• Capabilities can be overlaid with key data domains to create a “heat map” of cross-functional data usage.
22
Core Business
Shared Services
Artful Art’s Business Capabilities
Etc. – sample subset only
Product Development
R&D
Product
Management
Product
Manufacturing
Packaging
Marketing
Product
Messaging
Branding
Product
Launch
Campaign
Development
Lead
Generation
Pricing
Sales
Pipeline
Management
Customer
Relationship
Quotes &
Tenders
Research & Development Branding & Go-to-Market
Partner
Management
Sales & Distribution
Human Resources
Recruitment
Employee
Training
Performance
Management
Legal
Compliance
Contract
Management
Data Domains
Customer
Product
Account
Etc.

Implement “Just Enough” Data Governance
• Know what to manage closely and what to leave alone
• The more the data is shared across & beyond the organization, the more formal governance needs to be
23
Core Enterprise
Data
Functional & Operational
Data
Exploratory Data
Reference &
Master Data
Core Enterprise Data
• Common data elements used by multiple
stakeholders, departments, etc. (e.g. DW)
• Highly governed
• Highly published & shared
Functional & Operational Data
• Lightly modeled & prepared data for
limited sharing & reuse
• Collaboration-based governance
• May be future candidates for core data
Exploratory Data
• Raw or lightly prepped data for
exploratory analysis
• Mainly ad hoc, one-off analysis
• Light touch governance
Examples
• Operational Reporting
• Non-productionized analytical model data
• Ad hoc reporting & discovery
Examples
• Raw data sets for exploratory analytics
• External & Open data sources
Examples
• Common Financial Metrics: for Financial & Regulatory Reporting
• Common Attributes: Core attributes reused across multiple areas
(e.g. Customer name, Address, etc.)
Master & Reference Data
• Common data elements used by multiple stakeholders
across functional areas, applications, etc.
• Highly governed
• Highly published & shared
Examples
• Reference Data: Department Codes, Country Codes, etc.
• Master Data: Customer, Product, Student, Supplier, etc.
Exploratory analysis
uses core data sets
when applicable
Derived variables of
value can be fed into
Core Enterprise, or
even Master Data.
PublishPromote

Finding the Right Balance
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Human
Automated
Resolve at Source Resolve via Post-Processing
• Business Process Change
• Policies & Procedures
• Governance Steering Committees
• End User Training
• Industry Advisory Councils
• Data definition & glossary
• Data Quality Working Groups
• Application-Driven Data Entry & Workflow
• Application-level data validation
• Database-level data validation & integrity
(data models)
• Data Quality tool validation at source
• Data Cleansing Tools and/or SQL
• ETL (Data Warehouse)
• Data Stewardship
• “Conscious Disregard”
Proactive Business Management Reactive Business Management
Proactive Technical Management Reactive Technical Management
• Data Audit & Dashboards
• External Data Sources

Data Governance Tools
There is no “one size fits all” data governance tool. Pick the solutions that match your uses cases & audiences.
25

The Critical Role of Data Architecture & Governance
26
A high-level data architecture provides the roadmap for data strategy & associated governance.
Business data model
What data do we prioritize? Where is this data used?
Business process models
Where is this data stored?
Data architecture diagram
What rules apply to this data?
Business rules & policy
What is the quality of the data?
Data quality dashboard
Business Glossary
This architecture provides a guide for small, targeted projects for business value to add additional detail.
What data best
supports our Brokers?
What data best supports
our Customers?
What data can we use to
best Price our Policies?
Home Auto
Commercial
What external data can we use
for business advantage?

Metadata Makes Data Governance Actionable
• Metadata can help take the business rules & definitions defined in policies and make them
actionable in physical systems, maintaining a lineage & audit trail.
27
Policies & Procedures Technical Implementation Audit & Lineage

Metadata Management Tools
• The following are common architectural options for metadata management within & between organizations.
• There is no “one size fits all” approach.
• They can be used together within the same organization.
28
Central, Enterprise-wide
Metadata Catalogue / Repository
Metamodel(s)
Metadata Storage
(Database)
Population
Interfaces
Matching &
Reuse Logic
Publication & Sharing
Reports Web Portal Integration & Export
Tool or Purpose-Specific
Repository
Business Glossary
ETL Tool
Data Modeling Tool
BI ToolEtc
Data Dictionary
Database
Metadata Exchange &
Registry
Information Sharing & Standards

Data Models are a Key Part of Data Governance
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Data Models are critical to data governance at the conceptual, logical, and physical levels.

Process Models
• Process models are a helpful tool for describing core business processes.
• “Swimlanes” outline organizational considerations
• Data can be mapped to key business processes to understand creation & usage of information.
30
Identifying key data dependencies in core business processes

Customer Journey Map
• A customer journey map
outlines key phases of the
customer in their “journey”.
• They are similar to a process
model, but with a different
focus & perspective.
• Creating a data overlay is a
helpful way to see the key
data touched at each point
in the journey.
• Journey maps can be
created for other industries
as well, e.g. Student,
Patient, etc.
31

CRUD Matrix -- DRUC?
Product
Development
Supply Chain
Accounting
Marketing Finance
Product Assembly Instructions C R
Product Components C R
Product Price C U R
Product Name C U,D
Etc.
32
Create, Read, Update, Delete
• CRUD Matrices shows where data is Created, Read, Updated or Deleted across the
various areas of the organization
• This can be a helpful tool in data governance & data quality.

Data Governance through Data Architecture
• A major Retail Vendor wanted to become a data-driven company
• Enhancing the customer experience by mapping the Customer Journey to the Data Lifecycle
• Using IoT product data to improve product design & customer service
• Optimizing product supply chain & delivery
• Developing a Tactical Data Strategy determined that
• Master Data Management was needed to manage customer contact data throughout the Customer Journey
• Data Governance was required to manage data across organizational siloes: product development, marketing, sales, etc.
• Data Architecture was needed to understand the data ecosystem: data flow diagrams, data models, process models, etc.
33
Using Data to Build Customer Loyalty & Increase Sales
Customer SupportCustomer Discovery &
Purchase
Product Delivery &
Tracking
Product Usage Tracking Product Development

The Results
• This data governance and data architecture effort used a full range of data architecture artifacts
• Data Model
• Customer Journey Map (i.e. Process Model)
• CRUD Matrix
• Data Flow Diagram
• System Architecture
• The Data Governance Committee used these artifacts focused on a small piece of the organization for a “Quick Win”
• 4 week “sprint”
• Told a relatable “story” – the flow of a customer’s email address from discovery, to purchase, through tech support, etc.
• The results speak for themselves.
34
Aligning Business and Tech Teams though a “Quick Win”
I never thought I’d be using
the words “data flow
diagram”, but I love it!
- Chief Marketing Officer
Wait…shouldn’t I be
governing how my sales
team enters the data?
- Head of Sales

Summary
• Orchestrate the people, process, technology,
& culture required to support your data
architecture through a robust Data
Governance program.
• Pick the right tools for the right job – there is
no “one size fits all” solution.
• Build “quick wins” into your roadmap to
provide business value through every stage
of your architecture development

DATAVERSITY Data Architecture Strategies
• January 23 Emerging Trends in Data Architecture – What’s the Next Big Thing?
• February 27 Building a Data Strategy - Practical Steps for Aligning with Business Goals
• March 26 Cloud-Based Data Warehousing – What's New and What Stays the Same
• April 23 Master Data Management – Aligning Data, Process, and Governance
• May 28 Data Governance and Data Architecture – Alignment and Synergies
• June 25 Enterprise Architecture vs. Data Architecture
• July 22 Best Practices in Metadata Management
• August 27 Data Quality Best Practices
• September 24 Data Virtualization – Separating Myth from Reality
• October 22 Data Architect vs. Data Engineer vs. Data Modeler
• December 1 Graph Databases: Practical Use Cases
36
Join us next month

About Global Data Strategy™, Ltd
• Global Data Strategy™ is an international information management consulting company that
specializes in the alignment of business drivers with data-centric technology.
• Our passion is data, and helping organizations enrich their business opportunities through data and
information.
• Our core values center around providing solutions that are:
• Business-Driven: We put the needs of your business first, before we look at any technology solution.
• Clear & Relevant: We provide clear explanations using real-world examples.
• Customized & Right-Sized: Our implementations are based on the unique needs of your organization’s
size, corporate culture, and geography.
• High Quality & Technically Precise: We pride ourselves in excellence of execution, with years of
technical expertise in the industry.
37
Data-Driven Business Transformation
Business Strategy
Aligned With
Data Strategy
Visit www.globaldatastrategy.com for more information

Questions?
38
• Thoughts? Ideas?
www.globaldatastrategy.com

DAS Slides: Data Governance and Data Architecture – Alignment and Synergies

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DAS Slides: Data Governance and Data Architecture – Alignment and Synergies