Align Business Goals with Data Strategy

Copyright Global Data Strategy, Ltd. 2019
Building a Data Strategy - Practical Steps for
Aligning with Business Goals
Donna Burbank, Managing Director
Global Data Strategy, Ltd.
February 28th, 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

DATAVERSITY Data Architecture Strategies
• January 24 - on demand 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?
3
This Year’s Lineup

Today’s Topic
Developing a Data Strategy for your organization can seem like a daunting task.
The opportunity in getting it right can be significant, however, as data drives many
of the key initiatives in today’s marketplace: digital transformation, marketing,
customer centricity, and more.
This webinar will help de-mystify Data Strategy and Data Architecture and will
provide concrete, practical ways to get started.
4

What is a Data Strategy?
5
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

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
Aligning Business and Data Strategy
Global Data Strategy, Ltd. 2019 www.globaldatastrategy.com

Business & Data Strategy – the Interdependency
7
Business Strategy Data Strategy
Informs & Guides
Informs & Guides
Business Strategy

Current State AssessmentBusiness Goals & Strategy Implementation RoadmapProposed Future State
Where to Begin? Data Strategy Assessment & Roadmap
Understanding Current
Maturity & Environment
Identifying Business Goals & Objectives
Aligned to Data
Prioritizing Efforts &
Identifying “Quick Wins”
Propose Future State Capabilities,
Processes & Organizational Structure
Communication & Evangelism
Business Motivation Model Data Management Maturity Assessment
3-12-24 Month Roadmap
KPIs and
Metrics
Organizational Structure & Framework
Architecture & Technology Recommendations
Technology Landscape Overview
Roles, Skills &
ResponsibilitiesProcesses &
Procedures
Summary & Recommendations
www.globaldatastrategy.comGlobal Data Strategy, Ltd. 2019
Business Drivers Mapped to
Strategic Data Initiatives

Global Data Strategy, Ltd. 2019 9
Level 1: Aligning Business Strategy with Data Strategy

Business Motivation Model
10
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
11

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.

Mapping Business Drivers to Data Objectives
13
Challenges
Aligning Business & IT
• Policies not actionable at technology level
• Clarity needed around core business definitions
• Better taxonomies & classifications needed
Information Silos
• Key definitions stored in single use tools
• Reporting & Metrics differ across groups
• Database structures not easily accessible
• Documentation often project-focused & not
widely shared.
Limited Information Context
• Lack sufficient context to manage & consume
data effectively
• Lack of data lineage to view key relationships
• Time spent searching for data & definitions, not
new business uses for data.
• No single ‘roadmap’ of information assets
Process Inefficiencies
• Policy interpretations are done as snapshots
• Duplication of effort across reporting teams (e.g.
different queries for same use case)
Single View of Customer
• Single View of Customer across systems
• Map data elements to Customer Journey
• Automate PII classification for Privacy & Security
• “How Can Data Better Support Our Customers?
Establish Integration & Lineage
• Automate data lineage between key data sources
• Source to target mapping for reporting
• Change impact analysis
• Policy Audit
• “Understand Critical Connections Between Data”
Data-Centric Objectives
Publish Common Data Catalogue
• Provide common Business Glossary
• Publish physical data structures
• Share common queries for reuse
• “Reuse & Efficiency via Information Sharing.”
Support Collaboration & Discovery
• Build a collaboration platform for sharing of ideas
• Provide a common map of data to highlight new
ways to leverage & integrate information
• Assign data stewards for key data areas
• “Innovation through Collaboration”
Customer Satisfaction &
Brand Integrity
Business Effectiveness
Collaboration & Insights
Operational Efficiency
Risk Reduction
Business Drivers

Level 2: People, Process, and Culture

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. ☺ )

Data Governance – A Basic Framework
Organization &
People
Process &
Workflows
Data Management &
Measures
Culture &
Communication
Vision & Strategy
Tools & Technology
Business Goals &
Objectives
Data Issues &
Challenges
Managing the Complexity Interactions between Technology, Organizations, and People

Building the Data Governance Framework
17
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?

Mapping Organizational Capability
• Organizational Capability, Organizational Structure, and Roles are key to any Data Strategy
18
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

5 Basic 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
19
• There are diverse ways to implement data stewardship, unique to each organization.

Level 3: Leveraging Data for Strategic Advantage

Visualizing Current vs. Target Maturity
• It’s important to take a realistic look at your organization’s current state maturity
• Where you are
• Where you want to be
21
Determine Relative Strengths
Significant Gap in Data Governance
Metadata Management
meets Target Maturity
We’re “overdoing it” for
Data Architecture

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
22
Focus on Business Value
Business Value
Too Academic, nothing
gets done
Too “Wild West”, nothing
gets done - chaos

Developers
Managers
Enterprise Data Management
Part of a Data Strategy is Defining Fit for Purpose Solutions
Operational Data Reporting & Analytics Master & Reference Data Metadata
CRM
Customer X orders
Product Y at 2pm on
Oct 24, 2017 Sales
CRM
ERP
Customer
Care
IoT
Customer X calls
Support at 1pm on
Nov 1, 2017
Inventory consists of x
number of Product X
components on Oct
24, 2017
Supply
Chain
Customer turns on
foot warmer at 11pm
on Oct 30, 2017
Product
Team
Customer
CRM
& other
systems
DW
What were total
sales for Product X
in 2016 by region?
Lake
Operational Reporting
Enterprise Historical Reporting
Analytics & Discovery
What variables
most influence
customer repeat
purchases?
Limited Personal Use
Limited ad hoc analysis
for small data sets.
Not recommended
for enterprise data
management.
UCM
UPM
“Golden Record” for Customer,
Product, etc.
Mary Smith lives on 101 Main ST,
Detroit, MI and has been a
customer since 2011
Product 720 has a product code
of SS720 & a suggested retail
price of $11,000 USD.
Business & Technical Context &
Descriptions
ELT
How many support
calls are currently
open?
Analytics
Team
Managers
Reference Data
Hierarchies
The Sales management reporting
hierarchy is structured as follows.
Valid Return Codes are “X, Y, & Z”
State Codes include MA, MD MI …
Applications
DW Etc
DW Etc
DW Etc
Business
Glossary How is Total Sales
calculated?
What is a Qualified
Lead?
Business
Users
Data
Models
How do we uniquely
identify a customer?
Can a customer have
more than 1 email?
Data
Dictionary
What is this DW table
used for?
The standard length for
customer ID is CHAR(12)
Developers
Data
Lineage
How was this field
calculated?
What will break
downstream if I make a
change?
Developers
Developers
Business
Users
Access

Master Data Management & Data Integration
• Master Data Management (MDM) is the practice of identifying, cleansing, storing & governance core data
assets of the organization (e.g. customer, product, etc.)
• There are many architectural approaches to MDM, and data integration overall. Two are the following:
24
Centralized -- Commonly Relational Virtualized/Registry – Commonly Graph
MDM
Virtualization Layer
• Core data stored in
a common schema
in a centralized
“hub”.
• Used as a common
reference for
operational systems,
DW, etc.
• Data remains in
source systems.
• Referenced through
a common
virtualization layer.
BOTH require the same core foundation of data quality, parsing & matching, semantic meaning,
data governance, etc. in order to be successful… and that’s usually the hardest part.

Master Data
Management
Data
Architecture
Data
Governance &
Stewardship
Business
Process
Alignment
• Accountability & stewardship
• Business rule validation
• Conflict resolution
• Business Prioritization
• Business process models
• Data mapping to process
• CRUD and usage matrices
• Optimizing business process
for data improvement
• System Architecture & data flow
• Data models & hierarchies
• Match/merge and survivorship rules
• Data integration & design
Successful MDM Combines Data, Process, & Accountability

Level 4: Coordinating & Integrating Disparate Data Sources

Both Business & Technical Drivers Require Data Integration
27
A Data Model is a Common Reference Hub for Business & Technical Rules
Business Drivers
Technology Drivers
Enterprise Knowledge
Inventory
Mergers &
Acquisitions
Innovation &
Collaboration
Efficiency &
Agility
Etc…
Data ModelData Warehousing
Master Data
Management (MDM)
Data Lake
APIs & Application
Integration
Etc…
A Data Model can
be a Common
Reference

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 Catalogues - Harnessing “Tribal Knowledge”
29
Usage Ranking
• Which:
• Definitions are most
complete & helpful?
• Algorithms offer a helpful
starting point?
• Queries offer great logic
to share?
• Etc.
Helpfulness Ranking
• Which:
• Queries are others using?
• Tables are accessed the
most?
• Glossary terms are most
often searched?
• Etc.
Collaboration & Crowdsourcing
Term: Part Number
Alternate Names: Component Number
Definition:
A part number is an 8 digit alphanumeric field that uniquely
identifies a machine part used in the manufacturing process.
Is this truly the same as the old Component
Number? That was a 10 digit numeric field. It
didn’t have letters.
Yes, it is. I had the same problem for the
finance app, and I wrote a quick program to
convert the numbers. We just strip off the first
two chars now. Click here to find it.

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
30
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

Level 5: “Bottom-Up” Management & Inventory of Data Sources

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
32
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.

Data Modeling Creates an “Active Inventory” of Data Assets
• Know what data you have: Create a visual inventory of database systems
• Know what your data means: Communicate key business requirements between business and IT
stakeholders
• Support data consistency: Build consistent database structures & support data governance initiatives
Sybase
MySQL
Oracle
Data Models
Teradata
Sybase
SQL
Server
DB2
Teradata
SQL
Server DB2
MySQLSQL
Azure
SQL
Azure
Oracle

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
34
“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
35
“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

Building a Roadmap: Putting it All Together

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”
37
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

Evangelism & Outreach
• Key to long-term success is continued evangelism & outreach
• Communicate, communicate, communicate!
• Training & education
• Newsletters
• Webinars
• “Branding” & Collateral
• 1:1 Briefings
• “Lunch & Learns”
• Conference presentations
• Service Catalogues
• Etc.

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.

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?
40
Join Us Next Month

Related Article
• Related article on DATAVERSITY, Sept 2017:
• Data Management vs. Data Strategy: A
Framework for Business Success
41
To Read More

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.
42
Data-Driven Business Transformation
Business Strategy
Aligned With
Data Strategy
Visit www.globaldatastrategy.com for more information

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43
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Align Business Goals with Data Strategy

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