dataversitydatagovernanceorgchangeapril2019-190429155809.pdf

Copyright Global Data Strategy, Ltd. 2019
Data Governance - Combining Data
Management with Organizational Change
Donna Burbank and Nigel Turner
Global Data Strategy, Ltd.
April 25th 2019
Follow on Twitter @donnaburbank
Twitter Event hashtag: #DAStrategies

Global Data Strategy, Ltd. 2019
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 recently awarded the
Excellence in Data Management Award from
DAMA International in 2016.
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

Nigel Turner
Nigel Turner has worked in Information
Management (IM) and related areas for
over 20 years. This experience has
embraced Data Governance,
Information Strategy, Data Quality, Data
Governance, Master Data Management,
& Business Intelligence.
He spent much of his career in British
Telecommunications Group (BT) where
he led a series of enterprise wide IM &
data governance initiatives.
After leaving BT in 2010 Nigel became
VP of Information Management Strategy
at Harte Hanks Trillium Software, a
leading global provider of Data Quality
& Data Governance tools and
consultancy. Here he engaged with over
150 customer organizations from all
parts of the globe.
Currently Principal Consultant for EMEA
at Global Data Strategy, Ltd, he has been
a principal consultant at such firms as
FromHereOn and IPL, where he has led
Data Governance engagement with
customers such as First Great Western.
Nigel is a well known thought leader in
Information Management and has
presented at many international
conferences. He also works part time at
Cardiff University, where he is setting up
a Student Software Enterprise company.
In addition he has also been a part time
Associate Lecturer at the UK Open
University where he taught Systems &
Management.
Nigel is very active in professional Data
Management organizations and is an
elected Data Management Association
(DAMA) UK Committee member. He
was the joint winner of DAMA
International’s 2015 Community Award
for the work he initiated and led in
setting up a mentoring scheme in the
UK where experienced DAMA
professionals coach and support newer
data management professionals.
Nigel is based in Cardiff, Wales, UK.
Follow on Twitter @NigelTurner8
Today’s hashtag: # DAStrategies

DATAVERSITY Data Architecture Strategies
• January 24 - on demand Emerging Trends in Data Architecture – What’s the Next Big Thing?
• February 18 - on demand Building a Data Strategy - Practical Steps for Aligning with Business Goals
• March 28 - on demand Data Modeling at the Environment Agency of England - Case Study
• April 25 Data Governance - Combining Data Management with Organizational Change (w/ guest Nigel Turner)
• May 23 Master Data Management - Aligning Data, Process, and Governance
• June 27 Enterprise Architecture vs. Data Architecture
• July 25 Metadata Management: from Technical Architecture & Business Techniques
• August 22 Data Quality Best Practices (w/ guest Nigel Turner)
• Sept 26 Self Service BI & Analytics: Architecting for Collaboration
• October 24 Data Modeling Best Practices: Business and Technical Approaches
• December 3 Building a Future-State Data Architecture Plan: Where to Begin?
4
This Year’s Lineup

Today’s Topic
Data Governance is both a technical and an organizational discipline, and getting Data Governance
right requires a combination of Data Management fundamentals aligned with organizational
change and stakeholder buy-in.
This webinar provides an architecture-based approach to aligning business motivation,
organizational change, Metadata Management, Data Architecture and more in a concrete, practical
way to achieve success in your organization.
5

Gartner’s View – 21st Century:
The Rise of the Data Driven, Digital Business
6
• To manage its people, a digital company
needs a HR function to manage
recruitment, skills & people retention
• To manage its IT, needs an IT function to
ensure its technology investment
supports its business aspirations
• To manage its processes, needs Process
management to drive the design and
deployment of its automated and manual
processes
• To manage its data, needs a Data
management function to develop &
improve its data assets to underpin its
People, Process & Technology functions

DAMA – Data Disciplines
Data Management Book of Knowledge (DMBOK) – Version 2 2017
11 DATA MANAGEMENT DISCIPLINES FUNCTIONAL AREAS
Data Architecture
Data Modelling &
Design
Data Storage &
Operations
Data Security
Data
Integration &
Interoperability
Documents &
Content
Reference & Master
Data
Data Warehousing &
Business Intelligence
Data Quality
Data
Governance
Meta-data
7

Aligning Business and Data Strategy
www.globaldatastrategy.com
A Successful Data Strategy links Business Goals with Technology Solutions
Level 1
“Top-Down” alignment with
business priorities
Level 5
“Bottom-Up” management &
inventory of data sources
Level 2
Managing the people, process,
policies & culture around data
Level 4
Coordinating & integrating
disparate data sources
Level 3
Leveraging data for strategic
advantage
Copyright 2019 Global Data Strategy, Ltd

Data Governance – a Simple Perspective
Data Governance is
a business led
continuous process
of improving data
for the benefit of all
data stakeholders
9
What is Data Governance – the Global Data Strategy Definition
Business Led Continuous Process
• Data is a business asset and so must be owned by
the business
• Data Governance must be a business as usual
activity; it’s not a project with an end date
Improving Data
• Governance must demonstrate business
improvement through better data
• Monitoring data without an improvement agenda
is pointless
Benefit
• The benefits of Data Governance must be real
and measurable
• All stakeholders (Business, IT, Customers,
Suppliers, Regulators etc.) should recognize the
benefits it brings to them

Lack of Governance & Accountability for Data: All Too Common
10
In many organisations, nobody
is formally responsible for data
and its governance…
…so bad data never gets
systematically fixed
“If we are all supposed to be responsible for data,
no one is responsible and nothing changes”
(Quote from senior GDS client – 2019)

How Can You Ensure Data Governance is Successful?
11
Main enablers of success
Have a clear vision
of what good looks
like
Focus on & prioritize the data
that really matters
Set realistic expectations
Use a recognised industry
framework
Align efforts with
business benefits
Equip people to succeed Learn from best practice Recognise that there is no
‘one size fits all’ solution

Data is Part of a Wider Enterprise Architecture
• Enterprise Architecture provides a high-level view of the people, processes, applications, and data
of an organization
• Putting data in business context, e.g.
• How does data link to the rest of my organization?
• If I change data, what business processes are affected?
• Customer Journey Maps • Customer Journey Maps w/ Data
Overlay

Sample Business Motivation Model
13
Corporate Mission Corporate Vision
Goals & Objectives
To provide a full service online retail experience
for art supplies and craft products.
To be the respected source of art products worldwide,
creating an online community of art enthusiasts.
Artful Art Supplies ArtfulArt
C
External Drivers
Digital Self-Service
Increasing
Regulation Pressures
Online Community &
Social Media
Customer Demand
for Instant Provision
Internal Drivers
Cost Reduction
Targeted Marketing
360 View of
Customer
Brand Reputation Community Building
Revenue Growth
C
Accountability
• Create a Data Governance
Framework
• Define clear roles &
responsibilities for both
business & IT staff
• Publish a corporate
information policy
• Document data standards
• Train all staff in data
accountability
C
Quality
• Define measures & KPIs for
key data items
• Report & monitor on data
quality improvements
• Develop repeatable
processes for data quality
improvement
• Implement data quality
checks as BAU business
activities
C
Culture
• Ensure that all roles
understand their
contribution to data quality
• Promote business benefits
of better data quality
• Engage in innovative ways
to leverage data for
strategic advantage
• Create data-centric
communities of interest
• Corporate-level Mission & Vision
• May already be created or may
need to create as part of project.
• Project-level, Data-Centric Drivers
• External Drivers are what you’re
facing in the industry
• Internal Drivers reflect internal
corporate initiatives.
• Project-level, Data-Centric Goals
& Objectives
• Clear direction for the project
• Use marketing-style headings
where possible

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.
14
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.

The Critical Role of Data Architecture & Governance
15
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?

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.
16
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.
17

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

The Benefits of Applying a Data Governance Framework
• Aligns Data Governance efforts with high priority business needs and key
data
• Ensures a holistic approach to Data Governance:
• People
• Process
• Technology
• Can help to identify areas where:
• No additional action is required
• Components are partly in place but need enhancement or further
development
• Little or nothing is in place and so new workstreams are required
• Provides a baseline and helps define targets for a Data Governance
program
• Structured approach, but recognises that every organization is unique
and so activities can be tailored to each organization
19

Building the Data Governance Framework
20
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 analysed?
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?

Building the Data Governance Framework
21
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 analysed?
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?

Example Data Governance Organizational Structure
22
Executive Level
• Executive support
• Data advocacy
• Vital to have ELT level sponsor and champion
Strategic Level
• Sets strategic direction for Data Governance
• Owns the Data Governance Roadmap
• Identification of working groups as needed
• Funding within budgeted amounts for data quality & governance
• Identification of data stewards for key data areas
• Arbiter in the case of conflicting needs, definitions, or priorities
around cross-functional use of data.
Tactical Level
Data Owners & Data Stewards
by Data Domain (e.g. Property,
Customer, Finance etc.)
Executive
Sponsor
Dept. Head 1 Dept. Head 3 Dept. Head 6
Dept. Head 4 Dept. Head 5
Dept. Head 2
Data Domain Working Groups
Data Steward
• Create Data Improvement Plans for Data Domains
• Propose and progress data improvement initiatives
• Report progress to Data Governance Steering Group
• Escalate cross-domain issues and barriers to DG Steering Group
Updates &
Escalation
Creation &
Direction
Technical Role
Business Role
Key
Updates &
Escalation
Executive Leadership Team
Data Governance Steering Group
Other SMEs (e.g.
DPO, HR, Legal)
Data Owner 1
Data Governance
Lead
IT
Representative
CIO
Business Data
Stakeholders
Technical Data
Stakeholders
Other SMEs
e.g. DPO, HR
Data Owner 2 Data Owner 3
Data Owner 5
Data Owner 4 Data Owner 6
Invited Data
Stewards
Lead Role
Data
Improvement
Plans & Activities
Leadership
Management
& Progression

Sample Organizational Structures
23
The Data Governance Organization is not the same for all
Large Global Telco Small Professional Development
Organization
Data Governance
Steering Group
IT Data Subject
Matter Experts
Business Data
Stewards
Data Governance
Working Group
Legal & Regulatory
Data Subject
Matter Experts
Large Consumer Energy Company
Data Steering
Group
Data Quality
Board
Data Steward 2
Data Steward 1
Data Steward
…15
UK Energy Company
Data Owners
Divisional Data
Stewardship
Groups
Data Stewards
Lead Data
Stewards
Data Governance
Steering Group

Federated, Global Manufacturing & Retail
24
Data
Innovation
Council
Supply
Chan
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

5 Models of Data Governance & Stewardship
Model Description
Process Centric
Process owner(s) become(s) the data owner for all data created, amended & deleted by the business
process for which he / she is responsible (e.g. Claims process, Billing process, etc.)
Systems Centric
System owner(s) become(s) the data owner for all data created, amended & deleted by the IT system
for which he / she is responsible (e.g. CRM, Billing System, etc.)
Data Domain
Centric
Business appointed full or part-time roles accountable for improvement of key data domains, created,
stored or used across an organization (e.g. Patient, Student, Product, Customer, etc.)
Organization
Centric
Business appointed FT or PT roles accountable for improvement of key data domains on the basis of
departmental boundaries (e.g. Finance, Marketing, Clinical, etc.) or geographical locations.
Blended
In large and complex organizations, an overall Data Governance program may consist of combinations
of some or all of the above models
25
• There are diverse ways to implement data stewardship, unique to each organization.

Which Data Governance & Stewardship Model to Apply?
Model Advantages Drawbacks
Process Centric
• Works well where E2E processes are clearly defined & owned
• Processes create & update data, so synergies self-evident
• Data is a measure of process efficiency
• Data improvement usually requires process change
• Helps engage business stakeholders
• Multiple data ownership where data is used / shared across
processes (e.g. Product, Customer)
• Process & data objectives may clash, e.g. speed versus quality
• Process-centric view of data may encourage multiple partial
improvements, so sub-optimizing opportunities
Systems Centric
• Effective where master / system of record data sources are in
place
• Aligns with IT view of the world, so helps secure IT support for
Data Governance initiatives
• Business systems owners (if they exist) often key budget
holders & potential data owners
• No E2E perspective of data problems, particularly upstream
and downstream sources and processes
• Tends to encourage IT centric, rather than business centric,
view of problems and solutions
• Ineffective if business ownership of systems not embedded in
current organizational culture
Data Domain
Centric
• Potential complete view & ownership of key, shared data
across the organization
• Easier to define and enforce data policies and business rules
• Simplest and most transparent Data Governance model
• Only practical in smaller, more centralized organisations
• Funding Data Governance can be problematic
• Danger that others in organization feel little or no
responsibility for the data
Organization
Centric
• Aligns Data Governance with existing organizational /
functional boundaries & authority
• Easier to answer the “What’s in it for me?” question from
stakeholders
• May be best in widely dispersed, global organizations
• Can reinforce existing siloed structures and thinking
• Harder to analyse and address E2E cross-business issues with
data, so again sub-optimizing opportunities
• May discourage cross-business collaboration and reinforce “My
data” attitudes
Blended
• Ensures that ownership and stewardship are unique to a
specific organization and best aligns with existing culture
• May best meet the needs of large, global, complex
organizations
• DG actors selected on basis of pragmatism, not current role
• Can create greater complexity of Data Governance roles and
responsibilities
• Requires strong Data Governance leadership from the centre
to work 26

Aligning Governance with Current Corporate Culture
• Governance structure should reflect the culture of the organization
• Formal ‘top’ down or more federated / ‘collaborative’
• Agile or Waterfall
• Meetings or Wikis
• Align with Business Environment
• Offense or Defense - Risk or Opportunity?
• Pace and Timing
• Complement existing Governance structures & roles
• Easier to gain business and IT stakeholder commitment
• Empowers data owners and data stewards if they already have existing authority for other types of
Governance (e.g. systems, processes etc.)
• Work with what works….easier path to success
• Language Matters
• ‘Minutes’ or ‘Action Log’
• Steering Committee, Council, Tribe, Collaborative, etc.
27
Key Learning Points

Data Governance Organization: Use Cases
28
ORGANIZATION DRIVERS DATA GOVERNANCE PRIORITIES
Professional Services (UK) • Endemic data quality (DQ) issues causing
process inefficiencies
• Unplanned IT & data architecture; heavy
reliance on spreadsheets
• Repeated manual data cleansing
• No formal channels for data collaboration
• Extend ToR of IT Governance Board to embrace DG
& Data Architecture
• Create Working Groups to tackle urgent DQ issues
• Link DG efforts to Customer Journey Maps
• Focus on Governance of new data architecture,
rather than current data estate
Insurance (Global) • Merger of two insurance companies
created need for better data integration
• Legacy companies had different data
cultures & priorities
• Lack of collaboration between legacy data
specialists
• Create cross-company Communities of Interest
(CoIs) themed around data management topics
(e.g. Data Analytics)
• Form Working Groups to tackle high priority cross-
business data problems
• Start with bottom up approach to lay the ground
for a future strategic DG program
Manufacturing (Global) • Highly competitive industry with growing
competitive threat
• Some master data sources in place, but
data quality issues
• Overreliance on spreadsheets to produce
reports
• Form a DG Steering Group to start and nurture a
cross-business data community & network
• Align with Business Process focused / engineering
culture
• Pilot DG concepts and practices via a Proof of
Concept pilot to win support for a DG program
• Run pilot to demonstrate DG benefits and support
a business case for global roll out

Summary
• Data Governance is part of a wider business strategy
• Business-centric architecture models can facilitate data governance
• Organizational structure and roles are critical to success
• Aligning with each unique corporate culture is key – one size does not fit all
29

DATAVERSITY Data Architecture Strategies
• January 24 Emerging Trends in Data Architecture – What’s the Next Big Thing?
• February 18 Building a Data Strategy - Practical Steps for Aligning with Business Goals
• March 28 Data Modeling at the Environment Agency of England - Case Study (w/ guest Becky Russell from the EA)
• April 25 Data Governance - Combining Data Management with Organizational Change (w/ guest Nigel Turner)
• May 23 Master Data Management - Aligning Data, Process, and Governance
• June 27 Enterprise Architecture vs. Data Architecture
• July 25 Metadata Management: from Technical Architecture & Business Techniques
• August 22 Data Quality Best Practices (w/ guest Nigel Turner)
• Sept 26 Self Service BI & Analytics: Architecting for Collaboration
• October 24 Data Modeling Best Practices: Business and Technical Approaches
• December 3 Building a Future-State Data Architecture Plan: Where to Begin?
30
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.
31
Data-Driven Business Transformation
Business Strategy
Aligned With
Data Strategy
Visit www.globaldatastrategy.com for more information

Questions?
32
• Thoughts? Ideas?

dataversitydatagovernanceorgchangeapril2019-190429155809.pdf

More Related Content

Similar to dataversitydatagovernanceorgchangeapril2019-190429155809.pdf

Recently uploaded

dataversitydatagovernanceorgchangeapril2019-190429155809.pdf