Data Architecture Strategies: Building an Enterprise Data Strategy – Where to Start?

Building an Enterprise Data Strategy –
Where to Start?
Donna Burbank, Managing Director
Global Data Strategy, Ltd.
February 22nd, 2018
Follow on Twitter @donnaburbank
Twitter Event hashtag: #DAStrategies

Global Data Strategy, Ltd. 2018
Donna Burbank
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.
2
Follow on Twitter @donnaburbank
Twitter Event hashtag: #DAStrategies

DATAVERSITY Data Architecture Strategies
• January - on demand Panel: Emerging Trends in Data Architecture – What’s the Next Big Thing?
• February Building an Enterprise Data Strategy – Where to Start?
• March Modern Metadata Strategies
• April The Rise of the Graph Database: Practical Use Cases & Approaches to Benefit your Business
• May Data Architecture Best Practices for Today’s Rapidly Changing Data Landscape
• June Artificial Intelligence: Real-World Applications for Your Organization
• July Panel: Data as a Profit Driver – Emerging Techniques to Monetize Data as a Strategic Asset
• August Data Lake Architecture – Modern Strategies & Approaches
• Sept Master Data Management: Practical Strategies for Integrating into Your Data Architecture
• October Business-Centric Data Modeling: Strategies for Maximizing Business Benefit
• December Panel: Self-Service Reporting and Data Prep – Benefits & Risks
3
This Year’s Line Up for 2018

A Word From Our Sponsor
4
Datawatch

Global Data Strategy, Ltd. 2018 5
Data Strategy Across The Organization
OFFENSEDEFENSE
OPERATIONAL EFFICIENCY ANALYTIC INSIGHT
HR
SALES
C-LEVEL
MARKETING
FINANCE
BI
IT

Bridge the Gap: Satisfy IT & Data Workers
Analysts need:
Collaboration
 A way to share their work
 A way to ensure their work is based on trusted data
 A means to showcase their contribution of models and analyses
 A way for other analysts and teams to contribute back to their creations
IT needs:
Centralization
 A solution that empowers all users to easily work with data
 Visibility and controls for governance and compliance
 Reduced burden - domain experts manage domain data
 Insight into which data is most valuable so they can focus their efforts
The organization
needs:
Socialized data
access
 Awareness of what data and assets are being created and by whom
 All users working and collaborating in the same place
 Agility and governance in one solution
 Analysis is conducted based on trusted and certified information
 Consistency in models across groups, teams, enterprise

Organizational Analytics Goals
PROMOTE COOPERATION
Bring teams together:
 Data scientists
 Analysts
 Data/ETL engineers
 IT
 QA/Governance
ENHANCE VISIBILITY
Emphasize:
 Communication
 Automation
 Collaboration
 Integration
 Measurement
IMPROVE PRODUCTIVITY
Help analytic teams:
 Rapidly produce insight
 Operationalize that insight
 Continuously improve analytic
operations & performance

What We’ll Cover Today
• The majority of successful organizations in today’s economy are data-driven, and innovative
companies are looking at new ways to leverage data and information for strategic advantage.
• While the opportunities are vast, and the value has clearly been shown across a number of
industries in using data to strategic advantage, the choices in technology can be overwhelming.
• From Big Data …
• … to Artificial Intelligence …
• … to Data Lakes and Warehouses
• … the industry is continually evolving to provide new and exciting technological solutions.
• This webinar will help make sense of the various data architectures & technologies available, and
how to leverage technology for business value and success.
• A practical framework will be provided to generate “quick wins” for your organization, while at
the same time building towards a longer-term sustainable architecture.
• Case studies will also be provided to show how successful organizations have successfully built
a data strategies to support their business goals.
8

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
Copyright 2018 Global Data Strategy, Ltd
Aligning Business Strategy and Data Strategy

How can we Transform our Business through Data?
Business Optimization
Becoming a Data-Driven Company
• Making the Business More Efficient
• Better Marketing Campaigns
• Higher quality customer data, 360 view
of customer, competitive info, etc.
• Better Products
• Data-Driven product development,
Customer usage monitoring, etc.
• Better Customer Support
• Linking customer data with support logs,
network outages, etc.
• Lower Costs
• More efficient supply chain
• Reduced redundancies & manual effort
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Business Transformation
Becoming a Data Company
• Changing the Business Model via Data – data
becomes the product.
• Monetization of Information: examples across
multiple industries including:
• Retail: Click-stream data, purchasing
patterns
• Social Media: social & family connections,
purchasing trends & recommendations, etc.
• Telecom: location information, usage &
search data, etc.
• Energy: Sensor data, consumer usage
patterns, smart metering, etc.
How do we do
what we do
better?
How do we do
something
different?

Basic Definitions
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Business & Data Strategies
A BUSINESS STRATEGY is a medium to long term business plan which
details the aims & objectives of a business and how it means to
achieve them.
A DATA STRATEGY is a medium to long term plan for the improvement,
management & exploitation of data across a business, and how it is
to be achieved.

Business & Data Strategy – the Interdependency
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Business Strategy Data Strategy
Sets Requirements for
Informs & Guides
Business Strategy

Getting it Wrong
13
What can cause Business and Data Strategies to become Misaligned?
Lack of a clearly articulated
business strategy
Absence of Business & IT
senior leadership in strategy
formulation & execution
Business fails to take
ownership of the data and
hence the data strategy
Data strategy is viewed as a
technology roadmap, led by
IT
Lack of cross-business / IT
collaboration &
communication
Complexity and lack of
priority, focus & deliverable
milestones
Not showing short-term
results & benefits
Lack of skills and expertise
to realize the strategy

Getting it Right
14
The Key Features of an Effective Data Strategy
ALIGNED Directly
Connected to
Business Drivers.
ACTIONABLE with
clear activities &
milestones
EVOLUTIONARY to
meet changing
business needs &
new technology
UNIQUE to the
specific organization

Consumer Energy Company
• For the consumer energy sector Big Data and Smart Meters are transforming the ways of doing business and interacting with
customers.
• Moving away from traditional data use cases of metering & billing.
• Smart meters allow customers to be in control of their energy usage.
• Control over energy usage with connected systems
• Custom Energy Reports & Usage
• Smart Billing based on usage times
• As energy usage declines, data is becoming the true business asset for this energy company.
• While the Big Data Opportunity is crucial, equally important are the traditional data sources
• Data Quality critical for operational and analytic data
• Data Governance critical for analyzing data in relation to business processes & roles
• With high volumes of data, critical data elements prioritized
Business Transformation through Data

Increasing Restaurant Revenue through Menu Data
• An international restaurant chain realized through its digital strategy that:
• While menus are the core product that drives their business…
• They had little control or visibility over their menu data
• Menu data was scattered across multiple systems in the organization from supply chain to kitchen prep to marketing,
restaurant operations, etc.
• Menu data was consolidated & managed in a central hub:
• Master Data Management created a “single view of menu” for business efficiency & quality control
• Data Governance created the workflow & policies around managing menu data
• Process Models & Data Mappings were critical
• Business Process diagrams to identify the flow of information
• CRUD Matrixes to understand usage, stewardship & ownership
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Managing the Data that Runs the Business
Product Creation &
Testing
Menu Display &
Marketing
Supply Chain Point of Sale &
Restaurant Operations

Optimizing Customer Experience through Data
• 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
• Data Architecture was needed to understand the data ecosystem: data flow diagrams, data models, process models, etc
• Data Governance was required to manage data across organizational siloes: product development, marketing, sales, etc.
• Master Data Management was needed to manage customer contact data throughout the Customer Journey.
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Using Data to Build Customer Loyalty & Increase Sales
Customer SupportCustomer Discovery &
Purchase
Product Delivery &
Tracking
Product Usage Tracking Product Development

UK Environment Agency
• The UK Environment agency worked with Global Data Strategy to develop Data Models & Data Standards
in order to support Open Data publication of key environmental measures.
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Supporting Digital Transformation & Open Data Publication
• Land boundaries
• Air & Water Quality
• Fish & Wildlife populations
• Etc.
• Becoming a Data-driven organisation included:
• Digital Transformation – all services online
• Open Data – promoting data sharing with public
• Common Data Models & Standards helped create a
common lingua franca across the organization:
“Establishing a standard is a really important step in bringing our
information together so we can be better joined up, better integrated
and work together more efficiently.”
- National River Basin Operations Manager, Environment Agency
• Saving time & money
• Supporting Regulation
• Enhancing public
reputation
• Improving data quality &
consistency
• Increasing collaboration
between teams

Managed Care Organization: Telling the Data “Story”
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Data Drives Everything We Do
Centered around Serving our Members
Member
Is Assisted by
ProviderStaff
Location
Credentials
Community Living
Visits an Emergency Room at
Would like to
transition to
Needs an
overnight stay in
Bed
Has Availability
for
Is Assisted by
Is Certified for
Practices at
Apply to
Support & Certify
Community
Train & Inform
Interacts
with

The Importance of Motivation
From Cruise Ship to Life Raft
With a common motivation, disparate skills, personalities and roles become an
asset, not an annoyance

Business Motivation Model
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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

The Role of the Data Professional
in the Data-Driven Business
• In the current environment of data-driven business, Data Professionals have an
opportunity to have a “seat at the table”
• Finding new opportunities to leverage data for business benefit
• Creating efficiencies & business process optimization
• Integrating data from disparate sources for new business insights
• Supporting organizational change
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Business Executive
• Results-Oriented
• Optimistic – Identifies opportunities
• “I’m busy.”
• “What’s the business opportunity?”
Data Architect
• Focused on architecture, data, technology
• Often seen as finding problems, not
solutions
• “Let me tell you about my data model!”
Data Advisor
• Focused on solutions, business, information
• Highlights issues & opportunities around
data
• “Less me show you how data can help your
business!”
The world is going to end if your
model is not in 3rd normal form!!
If you link your Customer data with
your Product usage stats, we can
increase sales.
What’s in it for me?
Be More “Data Advisor” and Less “Data Architect”

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

Data Management Maturity Assessment
Current State Future State
Strategy 2.8 4.3
We have a Data Strategy for maximizing the use of data within our organization 3 5
Our Data Strategy is aligned to our Business Strategy. 3 5
We have executive and/or senior-level business support and sponsorship for our strategy 2 4
We have published a plan to achieve our Data Strategy that includes organization support, process, and IT. 4 4
We have organizations and budgets in place to support our Data Strategy and Plan. 3 4
Our Data Strategy is published and well understood across lines of business and technology groups. 2 4
Etc…
Data Governance 3.0 5.0
We have a standard, auditable process for resolving data governance issues, such as change management, priority management, conflict resolution, etc. 3 5
We have data stewards who manage key data used across functional groups. They have clearly defined and well understood roles and responsibilities. 4 5
We have identified business-critical data elements and define & store them in a commonly-accessible format. 4 5
All of our people understand the importance of data to the organization and their personal responsibilities for managing it. 3 5
We have a data communications strategy to ensure data policies and standards are reinforced across our organization. 3 5
Data related objectives and responsibilities are formally included in job descriptions and/or personal objectives. 1 5
Our data governance process has a senior business and IT sponsorship and stakeholder buy-in across the functional leaders. 3 5
Etc…
Master Data Management 4 5
Etc….continue for each capability area 1 4
25
Ask a Detailed Set of Questions for Each Functional Area
Show Current State and desired Future State. You don’t have to be a “5” in everything!

Visualizing Current vs. Target Maturity
• A Radar Chart (“Spider Chart”) can be a helpful way to visualize the relative strengths & weaknesses
in various capability areas.
26
Determine Relative Strengths
Significant Gap in Data Governance
Metadata Management
meets Target Maturity
We’re “overdoing it” for
Data Architecture

Master Data
Management
Data Governance
Strategy
Mapping Business Drivers to Data Management Capabilities
27
Business-Driven Prioritization
Stakeholder Challenges
Lack of Business Alignment
• Data spend not aligned to Business Plans
• Business users not involved with data
1
360 View of Customer Needed
• Aligning data from many sources
• Geographic distribution across regions
2
Data Warehousing
Business Intelligence
Big Data Analytics
Data Quality
Data Architecture
& Modeling
Data Asset Planning &
Inventory
Data
Integration
Metadata
Mgt
Business Drivers
Digital Self Service
Increasing Regulation
Pressures
Online Community &
Social Media
Customer Demand for
Instant Provision
Internal Drivers
External Drivers
Targeted Marketing
360 View of Customer
Revenue Growth
Brand Reputation
Community Building
Cost Reduction
Integrating Data
• Siloed systems
• Time-to-Solution
• Historical data
3
Data Quality
• Bad customer info causing Brand damage
• Completeness & Accuracy Needed
4
Cost of Data Management
• Manual entry increases costs
• Data Quality rework
• Software License duplication
5
No Audit Trails
• No lineage of changes
• Fines had been levied in past for lack of
compliance
6
New Data Sources
• Exploiting Unstructured Data
• Access to External & Social Data
7
1 7
1 2 3 4 5 6
71 2 3 4 5 6
1 72 3
1 72 3
1 2 6
72 3
53 4
1 2 3 4
63 5
2 3 5 7
Shows “Heat
Map” of Priorities

Speak with a Wide Variety of Stakeholders
28
• 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.  )

Stakeholder Matrix
29
• Create a simple stakeholder matrix outlining the key stakeholders, their roles, involvement,
influence, impact, etc.
• Keeping track of “who’s who”: Create a simple stakeholder matrix outlining the key
stakeholders, their roles, involvement, influence, impact, etc.
RACI *:
R: Responsible
A: Accountable
C: Consulted
I: Informed

Data Source Inventory
• Document key data sources across the organization
• …as well as who is using them (i.e. key departments & stakeholders)
• Data models & other architecture tools can help document the technical structures & metadata
30
Data Sources Leadership Sales Finance Marketing Support R&D HR Legal Compliance
Relational Databases
MySQL X
Oracle X X X X X X X X
SQL Server X X
Sybase X
Etc.
BI Tools
Tableau X X X X X X
Qlik X X X
Etc.
Open Data
Data.gov – agricultural data X X X
Etc.

Industry Trends: Data Platforms are Currently in Use?
• A wide range of technologies are
currently in use:
• Relational databases most common
o Both Cloud & On-Premises
• Spreadsheets ubiquitous
31
“Which of the following data sources or platforms are you currently using?
[Select all that apply]
Relational Databases
are still clearly the
leader.
Spreadsheets are
ubiquitous
More Legacy
platforms (44.6%)
than Big Data (42.2%)
From Emerging Trends in Data Architecture, DATAVERSITY, by
Donna Burbank & Charles Roe, October 2017

Industry Trends: Emerging Technologies
32
“Which of the following do you plan to use in the future that you are not using
currently? [Select all that Apply]”
Many looking to Big
Data Platforms
Movement to the
Cloud is popular
Uncertainty is
common.
• For those looking at new
technologies, there is a wide range
of responses.
• Big Data Platforms a leader
• Move to Cloud RDMBS
• Graph Database
• Real-time Streaming
• Internet of Things (IoT)
• Many are still uncertain, indicating
the vast rate of change and wide
array of choices available.
From Emerging Trends in Data Architecture,
DATAVERSITY, by Donna Burbank & Charles Roe,
October 2017

Implement “Just Enough” Data Governance
• Each type of data has its own type of governance model & sharing paradigm
• As a general rule, the more the data is shared across & beyond the organization, the more formal governance needs to be
33
Core Enterprise
Data
Functional & Operational
Data
Exploratory Data
Reference &
Master Data
Core Enterprise Data
• Common data elements used by multiple
stakeholders across Bus, LOBs, functional areas,
applications, etc.
• 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, Account ID, Address)
Master & Reference Data
• Common data elements used by multiple stakeholders
across functional areas, applications, etc.
• Highly governed
• Highly published & shared
Examples
• Reference Data: Procedure codes, Country Codes, etc
• Master Data: Location, Customer, Product

Identify What Data Needs to Be Governed
34
And What to Leave Alone
Launch of New Product – Marketing Campaign
requires better customer information
Customer Product
Region
Vendor
Partner
Identify Key
Business Driver
Filter Data Elements
Aligned with Business
Driver
Focus Governance
Efforts on Key Data
What?Why? How?
Structured Warehouse for
Financial Reporting
Exploratory Analytics &
Discovery
Lightly governed
Social Media
Sentiment Analysis
Financial
Reporting
Highly governed

Crowdsourcing Governance & Metadata Definitions
• Many data governance projects (& vendors) are embracing the concept of “crowdsourcing”. i.e. The
Wikipedia vs. Encyclopedia approach
• Open editing
• Popularity & Usage Rankings
• Dynamically changing
35
Encyclopedia Wikipedia
• Created by a few, then published as read-only
• Single source of “vetted” truth
• Static
• Created by a by many, edited by many
• Eventual consistency with multiple inputs
• Dynamic
For Standardized, Enterprise Data Sets For Self-Service Data Prep & Analytics

Finding the Right Balance
36
• When implementing successful data governance in today’s rapidly-changing, self-service data
landscape, it is important to find a balance between:
Standards-based
Governance
The two methods work well together, using the right
approached depending on the data usage.
Collaboration-based
Governance
• Well-suited for enterprise-wide
data standards • Well-suited for self-service data
preparation & analytics

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
38
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?

Measuring Data Improvements
• KPIs & Measures aligned with concrete business drivers
• Helps prioritize efforts
• Assists with the “Why do I Care?” issue
• Basis for showing benefits and results
39
Align Data Quality Metrics to Business Improvements
KPI Current Target Status Business Benefits Type
Number of duplicate
customer records
2,000,000 1,000 • Correct # of customers for sales estimations
• Better single view of customer for integrated social
media campaign
• Reduce cost of physical mailing by $20K
• Cost savings
• Brand Reputation
• Marketing Innovation
Incorrect Salutation (Mr,
Ms, etc.)
5,000 1,000 •Customer satisfaction & Brand reputation harmed by
incorrect salutation.
•Targeted marketing campaigns by gender.
• Brand Reputation
• Campaign Effectiveness
Incorrect address/location 10,000 500 • Lower return rate on physical mailings
• Better targeted marketing by region.
• Cost Savings
• Campaign Effectiveness
Missing Sales Rep Assigned 500 100 • Ability for Sales to execute on customer leads
• Revenue growth
• Sales Effectiveness
Etc.
Business Driver: Improving Customer Data for Marketing Launch Campaign

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”
LoadEffort
Fulcrum
Identify areas where data can be the fulcrum.

Defining an Actionable Roadmap
• Develop a detailed roadmap that is both actionable and realistic
• Show quick-wins, while building to a longer-term goal
• Balance Business Priorities with Data Management Maturity
• Focus on projects that benefit multiple stakeholders
• Mix core architecture with “new shiny things”
41
Maximize the Benefit to the Organization
Initiatives H1 '17 H2 '17 H2 '18 H2 '18
Strategy Development
Governance Lineage for
Privacy Rules
Business Glossary
Population & Publication
Data Warehouse Metadata
Customer Analytics Pilot –
Social Media integration
Open Data Publication
IoT Integration
Ongoing Communication & Collaboration
Customer Product Location
Integrated
Customer View
Marketing
Sales
Customer Support
Executive Team

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.

Summary
• Aligning Data Strategy & Data Architecture with business drivers & goals is key to success
• Adapt your data architecture for both innovative & legacy technologies
• Orchestrate the people, process, technology, & culture required to support your data
architecture through a robust Data Governance program
• Design data quality and metadata into your data architecture from the outset, and how to
design core KPIs and metrics to track success
• Build “quick wins” into your roadmap to provide business value through every stage of your
architecture development
Where to Start? - With Business Drivers

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 technological solution.
• Clear & Relevant: We provide clear explanations using real-world examples, not technical jargon.
• 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, and we attract high-
quality professionals with years of technical expertise in the industry.
44
Data-Driven Business Transformation
Business Strategy
Aligned With
Data Strategy
www.globaldatastrategy.com

Related Article
• Related article on DATAVERSITY, Sept 2017:
• Data Management vs. Data Strategy: A
Framework for Business Success
45
To Read More

DATAVERSITY Data Architecture Strategies
• January Panel: Emerging Trends in Data Architecture – What’s the Next Big Thing?
• February Building an Enterprise Data Strategy – Where to Start?
• March Modern Metadata Strategies
• April The Rise of the Graph Database: Practical Use Cases & Approaches to Benefit your Business
• May Data Architecture Best Practices for Today’s Rapidly Changing Data Landscape
• June Artificial Intelligence: Real-World Applications for Your Organization
• July Panel: Data as a Profit Driver – Emerging Techniques to Monetize Data as a Strategic Asset
• August Data Lake Architecture – Modern Strategies & Approaches
• Sept Master Data Management: Practical Strategies for Integrating into Your Data Architecture
• October Business-Centric Data Modeling: Strategies for Maximizing Business Benefit
• December Panel: Self-Service Reporting and Data Prep – Benefits & Risks
46
This Year’s Line Up for 2018 – Join Us Next Month

Questions?
47
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Data Architecture Strategies: Building an Enterprise Data Strategy – Where to Start?

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Data Architecture Strategies: Building an Enterprise Data Strategy – Where to Start?