Building a Data Strategy – Practical Steps for Aligning with Business Goals

Copyright Global Data Strategy, Ltd. 2022
Building a Data Strategy
Practical Steps for Aligning with Business Goals
Donna Burbank
Global Data Strategy, Ltd.
February 24th, 2022

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Global Data Strategy, Ltd. 2022 www.globaldatastrategy.com
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.
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
several books 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
@GlobalDataStrat
Twitter Event hashtag: #DAStrategies

DATAVERSITY Data Architecture Strategies
• January Emerging Trends in Data Architecture – What’s the Next Big Thing?
• February Building a Data Strategy - Practical Steps for Aligning with Business Goals
• March Master Data Management – Aligning Data, Process, and Governance
• April Data Governance & Data Architecture: Alignment & Synergies
• May Improving Data Literacy Around Data Architecture
• June Business Intelligence & Data Analytics: An Architected Approach
• July Best Practices in Metadata Management
• August Data Quality Best Practices
• September Business-centric Data Modeling
• October Graph Databases: Benefits & Risks
• December Enterprise Architecture vs. Data Architecture
3
This Year’s Lineup

What We’ll Cover Today
• Developing a Data Strategy for your organization can seem
like a daunting task – but it’s worth the effort.
• Getting your Data Strategy right can provide significant
value, as data drives many of the key initiatives in today’s
marketplace, from digital transformation to marketing,
customer centricity, population health, and more.
• This webinar will help demystify Data Strategy and its
relationship to Data Architecture and will provide concrete,
practical ways to get started..
4

The Rise of the Data-Driven Business
Data, more than ever, is seen as a key business asset and strategic differentiator.
5

Data-Driven Business
Data-Driven Business is an impetus
for data management
• 70% of respondents feel that their organization
sees data as a strategic asset*.
• 68% are looking to save costs and increase
efficiency**
• 64% see digital transformation as a key driver
for data management (increase of 11% from 2019)**
6
Data Management is the foundation of the Data-Driven Business
* based on research from a 2019 DATAVERSITY survey on “Trends in Data Management” by Donna Burbank and Michelle Knight
** based on research from a 2020 DATAVERSITY survey on “Trends in Data Management” by Donna Burbank and Michelle Knight

What is a Data Strategy?
7
Strategy:
1. the art of devising or employing plans
or stratagems toward a goal
2. an adaptation or complex of adaptations (as
of behavior, metabolism, or structure) that
serves or appears to serve an important
function in achieving evolutionary success
3. the science and art of military command
exercised to meet the enemy in combat under
advantageous conditions
Strategy vs. Management
- Source Merriam Webster
Management:
1. judicious use of means to accomplish an end
2. the act or art of managing : the conducting or
supervising of something (such as a business)
- Source Merriam Webster

But What is “It”, Really?
• Many people are overwhelmed with the concept of building a Data Strategy – it can seem like a
massive and overarching task.
• On a very tactical level, many wonder what format it should be in – Word Document, PowerPoint
presentation, Interpretive Dance? 
• While many formats can be effective, a visual presentation often has the most impact. Key
sections of the strategy should include:
• Business Alignment – case for change and value proposition
• Current State Analysis
• Future State Recommendations
• Roadmap and Next Steps
• Projected ROI and Benefit
8

Business Optimization vs. Business Transformation
9
Digital Transformation is transforming business
Business Optimization
Becoming a Data-Driven Company
• Improving Efficiency
• Reduce Redundancy
• Eliminate Manual Effort
• Growing Revenue
• Improved Marketing Campaigns
• Data-driven Product Development
• Etc.
Business Transformation
Becoming a Data Company
• New Business Models
• Data is the product
• Monetization of information
• Digital Transformation
• New Business Models
• Data is the Business
• Etc.
How do we do what we do
better?
How do we do something
different?

Business & Data Strategy – the Interdependency
10
Data
Strategy
Informs & Guides
Informs & Guides
Business
Strategy

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
Aligning Business Strategy with Data Strategy

Look for Business Value “Levers”
• Identify areas that will derive the highest business value by
addressing.
• Is this supporting the new marketing campaign for a high visibility
product launch?
• Or are you “re-arranging the deck chairs on the Titanic” – i.e.
focusing valuable time and effort no low-value activities
• As with any areas of the business that have value, it is helpful to
build a model or architectural design around the key areas of
business value.
Identify “Quick Wins”
Load
Effort
Fulcrum
Identify areas where data can be the fulcrum.

Making the Business Case
13
While Business Cases and ROI Calculations can be complex, they generally fall into 4 categories:
• Wasted Labor costs due to
manual efforts
(Data cleansing, manual integration,
etc.)
• Inefficient business processes
for data management
(Product Master Data process)
• Data quality cost avoidance
(Wasted mailings sent to wrong
address)
Decreasing Costs
• Price Optimization through
Analytics
• Improved Marketing Campaigns
through Quality Customer Data
• Data-Driven Recommendation
Engines to enhance the sales
cycle.
• Better Grant writing through
data-driven needs analysis
• Industry regulations
(GDPR, HIPAA, BCBS 239, Spice,
HIPAA, etc.)
• Product Traceability
(Food lineage from farm/catch)
• Litigation due to Data Breach
• Health and Safety Audit
• Customer Satisfaction
• Brand Trust
• Social Media Voice of Consumer
• Loyalty & ‘Stickiness’
Increasing Revenue Reducing Risk Protecting Reputation

Include the Risk of Doing Nothing
14
• There is significant cost and risk in the status
quo
• Doing nothing often has a higher cost than
investing in data management.
• Make sure to include the “do nothing”
option in your analysis.

Speak with a Wide Variety of Stakeholders
15
• It’s important to speak with a wide range
of roles across the organization.
• Business & IT
• Cross-functional teams (Marketing,
Finance, Analytics, etc, etc.)
• Understand key opportunities &
challenges.
• Recruit allies & volunteers (and identify
those you still need to convince.  )
From Data Modeling for the Business by Hoberman, Burbank, Bradley, Technics Publications, 2009

Stakeholder Analysis
16
• Stakeholders are key to the success or failure of your data program. Like data assets, they should be
analyzed and managed.
• They are the “Who” in the Zachman Framework
• A number of tools and techniques exist to help manage stakeholders.
• Stakeholder Map: Listing of key stakeholders with their roles, contact information, location, etc.
• Interest/Influence matrix: Rank stakeholders by level of interest vs. amount of influence they hold.
• Interest matrix: Identify key interest areas and map their importance to each stakeholders or stakeholder group.
• Interview Schedule & Key Questions: Plan the interview schedule to respect stakeholders’ time. Identify key
questions ahead of the meeting.
• Preferred Communication Styles: Identify the Styles of communication preferred by stakeholders & their
communication styles (email, face to face meeting, coffee, introvert/extrovert, etc.)
• Communication Plan: Develop a phased communication plan including feedback, reporting, metrics, etc.

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
• 86% consider data security a priority
• >50% identified improved collaboration
through using a defined data architecture
17
Data Governance improves collaboration and increases data accountability & protection
* based on research from a 2019 DATAVERSITY survey on “Trends in Data Management” by Donna Burbank and Michelle Knight

Tools &
Platforms
Applying a Structured Data Governance Framework
Vision & Strategy
Organization &
People
Processes &
Workflows
Data Management
& Measures
Culture & Communication
Copyright Global Data Strategy, Ltd. 2022
A structured Data Governance Framework helps align business and IT for long-term success.

Mapping Organizational Capability
• Organizational Capability, Organizational Structure, and Roles are key to any Data Strategy
19
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

Data Strategy Relies on Data Architecture & Governance
20
A successful Data Strategy
relies heavily on Data
Architecture and Data
Governance
Based on research from a 2020 DATAVERSITY survey on “Trends in Data Management” by Donna Burbank and Michelle Knight

Top 10 Data Platforms & Sources – 2022-23
21
0.00% 10.00% 20.00% 30.00% 40.00% 50.00% 60.00%
Graph database
Real-time/streaming database
Big Data platforms (e.g. Hadoop Ecosystem)
JSON
Don’t know
Packaged applications (e.g. ERP, CRM, etc.)
Spreadsheets
Non-relational cloud-based database
Relational on-premises database
Relational cloud-based database
Which Data Platforms & Sources Do You Plan to Use in the Next 1-2 Years? Top 10
(Select all that apply)
** based on research from a 2021 DATAVERSITY survey on “Trends in Data Management” by Donna Burbank and Michelle Knight

Building Trusted Data Sets
• Successfully building trusted data sets
requires a full range of data management
disciplines
What does it take to build Trusted Data Sets?
Trusted
Data
Data Architecture
Data Quality
Privacy &
Security
Metadata
Management
Data
Governance
Trusted Data

The Critical Role of Data Architecture & Governance
23
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?

Global Data Strategy, Ltd. 2022 www.globaldatastrategy.com 24
Key Steps to Creating a Data Program
• The following steps should be included when creating a data program. The order is less important
than ensuring that they are completed.
Steps to Success
Secure Senior Executive
Support
• Identify a Data Champion among
senior leadership.
Define Vision, Drivers &
Motivations
• Define business-driven vision for
the program.
Build the Business Case
• Outline key benefits of data
program & risks of not doing so
Deliver “Quick” Wins
• Short, iterative, business-driven
projects deliver short-term value,
building towards long-term gain.
Identify Business-Critical Data
• Focus on the data that has the
highest impact on the business.
Identify & Interview
Stakeholders
• Elicit feedback from key stakeholders
– listen & communicate.
Create Organization
• Define an organizational structure
that aligns with your way of
working.
Communicate
• Build a communication plan from
initial feedback phase throughout
all phases of the program.
Assess IT Maturity
• Assess the maturity of the IT
organization across all aspects of
data management.
Map Business Priorities to IT
Capabilities
• Create a realistic “heat map”
aligning business goals with data
management capabilities.
Show Continued ROI
• Show continued value and
benefits throughout the program
Integrate with Change Mgt
• Integrate into larger Change
Management and Culture Change
activities

Implementation Roadmap
• Define your roadmap with key activities timed to align with key business initiatives.
• Align with necessary staffing and training.
25
Define Roadmap & Staffing for Executing the Strategy
Implementation Roadmap for Key Activities by Functional Area
Staffing & Training Recommendations

Why: Aligning with the Organizational Vision
• Key to any Data Strategy and Roadmap is aligning with the
organization’s vision.
• Particularly for planning a roadmap, it is important to
understand the company’s plans & trajectory
• Is the organization cautious or aggressive in its future state?
• Are there events that you should align your strategy
around? e.g. Product Launch, Corporate Audit, School
Semester start, etc.

Organizational Change Management
27
• There are several types of organizational change management methodologies (Prosci, Kotter, McKinsey, etc.).
• While they each have their unique benefits and approaches, below are some common phases & themes.
Awareness
Interest /
Excitement
Knowledge
Reinforcement /
Understanding
Communication, Communication, Communication!
• Alignment with
Business Value
• Vision & Roadmap
• Etc.
• WIIFM
• Testimonials
• Swag, slogans, etc.
• Etc.
• Launch Awareness
• Trainings
• Certifications
• Etc.
• Ongoing Communication Plan
• ROI & Value over time
• Success Stories
• Etc.

Organizational Change Management
28
Awareness
Interest /
Excitement
Knowledge
Reinforcement /
Understanding
Communication, Communication, Communication!
• Alignment with
Business Value
• Vision & Roadmap
• Etc.
• WIIFM
• Testimonials
• Swag, slogans, etc.
• Etc.
• Launch Awareness
• Trainings
• Certifications
• Etc.
• Ongoing Communication Plan
• ROI & Value over time
• Success Stories
• Etc.
Emotions play a key role underpinning each phase.
This is new!
This is new! Should I be
concerned?
This is new!
Other people are excited
about it – maybe it can
help me, too. This is new!
This is interesting – I’m
starting to understand
how it can help me.
This is new!
I’m seeing the value of
this over time – how did I
ever work without this?

Summary
• Aligning Data Strategy with business drivers &
goals is key to success
• Orchestrate the people, process, technology, &
culture required through a robust Data
Governance program.
• Pick the right tools for the right job – there is no
“one size fits all” solution.
• Your roadmap should tell a story and show an
evolution from the current state.
• Define “quick wins” as well as long-term value
• Culture building and organizational change are
critical to success

DATAVERSITY Data Architecture Strategies
• January Emerging Trends in Data Architecture – What’s the Next Big Thing?
• February Building a Data Strategy - Practical Steps for Aligning with Business Goals
• March Master Data Management – Aligning Data, Process, and Governance
• April Data Governance & Data Architecture: Alignment & Synergies
• May Improving Data Literacy Around Data Architecture
• June Business Intelligence & Data Analytics: An Architected Approach
• July Best Practices in Metadata Management
• August Data Quality Best Practices
• September Business-centric Data Modeling
• October Graph Databases: Benefits & Risks
• December Enterprise Architecture vs. Data Architecture
30
This Year’s Lineup

Global Data Strategy, Ltd. 2022
Who We Are: Business-Focused Data Strategy
Maximize the Organizational Value of Your Data Investment
In today’s business environment, showing rapid time to value for
any technical investment is critical.
But technology and data can be complex. At Global Data Strategy,
we help demystify technical complexity to help you:
• Demonstrate the ROI and business value of data to your
management
• Build a data strategy at your pace to match your unique culture
and organizational style.
• Create an actionable roadmap for “quick wins”, which building
towards a long-term scalable architecture.
Global Data Strategy’s shares experience from some of the largest
international organizations scaled to the pace of your unique team.
www.globaldatastrategy.com
Global Data Strategy has worked with organizations globally in the
following industries:
Finance · Retail · Social Services · Health Care · Education · Manufacturing
· Government · Public Utilities · Construction · Media & Entertainment ·
Insurance …. and more

Questions?
Thoughts? Ideas?
32

Building a Data Strategy – Practical Steps for Aligning with Business Goals

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