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

Copyright Global Data Strategy, Ltd. 2020
Building a Data Strategy – Practical Steps for
Aligning with Business Goals
Donna Burbank
Global Data Strategy, Ltd.
February 27th, 2020
Follow on Twitter @donnaburbank
Twitter Event hashtag: #DAStrategies

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

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

What We’ll Cover Today
• 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 from digital transformation, to marketing, to customer
centricity, population health, and more.
• This webinar will help de-mystify data strategy and 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

Business and Technical/Data Roles are Merging
6
As Data & Digital Transformation grow, there is a blurring between business and tech roles

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
7

What is a Data Strategy?
8
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
9

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
• 53% see digital transformation as a key driver
for data management
10
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 McKnight

Business Optimization vs. Business Transformation
11
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
12
Business Strategy Data Strategy
Informs & Guides
Informs & Guides
Business Strategy

Global Data Strategy, Ltd. 2020 13
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 2019 Global Data Strategy, Ltd
Aligning Business Strategy and Data 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

Business Motivation Model
15
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

Master Data
Management
Data Governance
Strategy
Mapping Business Drivers to Data Management Capabilities
16
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

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

Find a Balance in Implementing Data Management
• Find the Right Balance
• Data Management projects can have the reputation for being overly “academic”, long, expensive, etc.
• No architecture at all can cause chaos.
• When done correctly, Data Management helps improve efficiency and better align with business priorities
18
Focus on Business Value
Business Value
Too Academic, nothing
gets done
Too “Wild West”, nothing
gets done - chaos

a number of respondents mentioned Data Governance in their comments as a way to align the various stakeholders around
common goals
Current Platform Adoption
• Relational Database still dominate the data
management landscape
• Majority is on-premises
• Some Cloud Adoption
• Spreadsheets still ubiquitous, partly due to
the large interest from business users.
19
Relational database still dominate the market, both on premises and Cloud-based

a number of respondents mentioned Data Governance in their comments as a way to align the various stakeholders around
common goals
Future Platform Adoption
• Future Plans still include a high percentage
of relational databases, with a higher
percentage of Cloud-based systems.
• A wider distribution of platform usage
indicates the variety of options and fit-for-
purpose solution – one size doesn’t fit all.
20
Future plans still feature relational databases, with a higher focus on Cloud Adoption, and a wider mix of technologies.

Integrating the Data Lake & Traditional Data Sources
• The Data Lake has a different architecture & purpose than traditional data sources
such as data warehouses.
• But the two environments can co-exist to share relevant information.
21
Data Analysis & Discovery – Data Lake Enterprise Systems of Record
Data Governance & Collaboration
Master &
Reference Data
Data Warehouse
Data MartsOperational Data
Security & Privacy
Sandbox
Lightly Modeled
Data
Data
Exploration
Reporting & Analytics
Advanced
Analytics
Self-Service BI
Standard BI
Reports

The Importance of KPIs
• Most businesses set strategic goals they desire to achieve, and measure these goals against Key
Performance Indicators (KPIs).
• These KPIs provide a concrete, objective way to measure progress towards these goals
• To use Finance as a comparison, they have a number of KPIs they use
to manage financial assets.
• Revenue Projections
• Budget Goals & Limits
• Expense Ration, etc.
• We need to do the same with data assets.
• % complete
• % accuracy
• Timeliness
• ROI
• Cost Savings
22
“You Can’t Manage What You Can’t Measure”

Measuring Data Improvements
• Data Quality KPIs & Measures can aligned with concrete business drivers
• Helps prioritize efforts
• Basis for showing benefits and results
• For example, if we ensure that email address are 100% complete and 90% accurate, our marketing campaign
effectiveness can increase by 20%.
23
Aligning Data Quality Metrics with 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
Business Driver: Improving Customer Data for Marketing Launch Campaign

Over $800M from Data Quality Improvement
24
• BT Group plc - a British multinational telecommunications
holding company which had the following challenges:
• Business Agility to deliver new products and services
• Regulatory Compliance to meet industry demands in a
heavily-regulated industry
• Customer Satisfaction through positive customer
interaction.
• Unfortunately, these efforts were being hampered by poor
data quality:
• Poor Supplier & Customer data hampered self-service
interactions
• Inaccurate inventory led to increased capital
expenditure
• Billing errors caused customer dissatisfaction
• To improve Data Quality, BT embarked on an enterprise-wide
data improvement journey which included both technology
and culture change.
The Challenge
• As a result, BT achieved in aggregate more than $800
million in quantified benefits including:
• Capital Cost Avoidance: Through improving accuracy of
inventory data, BT optimized equipment inventory and
reduced inventory costs.
• Improved Revenue Assurance: By improving Billing Data
accuracy, revenue loss decreased from more than 15% to
less than 1%.
• Productivity Gains in B2B Processes: By resolving data
quality issues, BT was able to automate customer and
supplier interactions, and reduce cycle times and manual
effort.
The Result
A Focus on Data Quality Yields Big Benefits for BT 1
1 Gartner, Publication Number G00138085, 24 March 2006

“Offense” vs. “Defense”
• Focused on Creating Opportunity
• Improving Profitability
• Increasing Revenue
• Improving Customer Satisfaction
• Competitive Advantage
25
Which style of data strategy & governance fits your organization?
Offense Defense
• Focused on Reducing Risk
• Compliance & Regulation
• Avoiding Audits or Fines
• Fraud Detection
• Security & Privacy
On which end of the spectrum is your organization?

Making the Business Case
• Minimizing Costs – this is often the easiest to show
• Reduction in Wasted Labor Costs: How much human capital is wasted due to poor data quality, lack of data
access, redundant data efforts, etc.?
• E.g. Mary spends 10 hours per week cleaning data before each marketing campaign.
• Calculate Mary’s hourly rate x # hours per week x # weeks -> $50/hr x 10 x 46 = $23,000 per year
• Improving Inefficient Business Processes: Can Supply Chain be made more efficient with a standardized set
of material master data?
• E.g. Efficiency gains of 2% can be achieved resulting in $X of savings.
• Cost Avoidance: Would improving address data quality reduce the number of returned mailings?
• E.g. Cost of each mailing x # returned x # times per year. -> $1 per mailing x 500 returned per week x
50 weeks of mailing = $25,000 per year
26
Minimizing Costs
Minimizing Costs

• Optimizing Revenue – How can better data improve business efficiency and profitability?
• Improved Business Performance: But more interesting is the ways data can be used to improve business effectiveness:
• Marketing Campaigns: Improved contact data (email, address, etc.) can improve marketing, but what new data sources
can be used? Social Media? Weather Data? Other?
• Advanced Analytics: How can analytics be used to improve the business, e.g. Price Optimization, Customer
Segmentation, etc.
• New Data-Driven Applications: How can data and new data-driven technologies (e.g. AI) be used to improve the business:
• Chat Bots: To streamline customer service
• Recommendation Engines: To enhance the sales cycle.
• New Revenue Streams: Can data be used for new revenue streams:
• Grant Funding: Data-driven analysis is often a key aspect of grant writing.
• Leveraging IP: Is there data that your company owns that can be monetized for research, marketing, etc.?
27
Optimizing Revenue
Optimizing Revenue

• Reducing Risk – Risk avoidance is a key aspect of any business. How can data and governance be used to minimize risk?
• Regulation: Industry regulations drive many data governance efforts including GDPR, HIPAA, BCBS 239, Spice, HIPAA, etc, etc.
• Product Traceability: Many food producers offer data-driven lineage showing the source of their food materials (e.g. fish catch).
• Health and Safety: Ensuring the health and safety of both customers and employees is critical.
• Employee: Data driven applications can help monitor employee health and safety activities (e.g. speeding).
• Customer: Is nutritional or allergen information correctly shown on menus? Does this information link to the actual food
source in the supply chain?
• Audit & Fines: Which industry regulations result in audit or fines? Has the organization been fined in the past? Can this be
quantified?
• Litigation: Has litigation occurred in the past due to data errors? What were the monetary sums?
28
Reducing Risk
Reducing Risk

Improvements to Core Business
• It’s often beneficial to turn your “back of the
envelope” calculations into a formal financial
projection.
• Benefits
• One time: e.g. sale of a data set, research
grant, etc.
• Recurring: e.g. staff productivity, ongoing
revenue, reduction in ongoing costs, etc.
• Costs:
• One time: e.g. software or hardware
purchase, initial training costs, etc.
• Recurring: e.g. Software subscription or
maintenance, staff salary, etc.
• Consider Capital Expenditure (Capex) vs.
Operational Expenditure (Opex)
• E.g. Cloud subscriptions can generally be
considered Opex.
• Speak with Finance to see what is beneficial
to them – they often have unique priorities
per industry.
29
Part of your job is Finance!

Calculate a Realistic “Break Even” Point
• Often, there is a period of “ramp up”
time where initial costs are greater than
the overall benefits.
• Software costs
• Training or Consulting Costs
• Initial loss of productivity as:
• Business processes are optimized
• Technical teams get up to speed
• It’s helpful to calculate a “break even”
point where the initiative begins to show
value.
30
Net-positive benefits may not happen immediately
Net positive value
Initial costs “Break even” point

Mapping Organizational Capability
• Organizational Capability, Organizational Structure, and Roles are key to any Data Strategy
31
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
Part of your job is
Organizational Change!

Cockroach, Unicorn, or Dinasour?
32
What project or programs do you align with?

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.
Part of your job is
Marketing!

Find Advocates Across the Organization
• It’s key to find champions for your data strategy effort across the business
• Both “business” and “IT”
• From Senior level executives as champions to Field staff as supporters
• In making the case for funding or buy-in, it’s helpful to have someone else tell your story
• Can marketing advocate the case for change to improve campaigns
• … Or sales discuss the potential increases in revenue
• …. Or Engineering point out the increases in efficiency and quality
34
While we may want to take the
“credit” for the data strategy, it is often
most successful when other people
have embraced it as their own.

Global Data Strategy, Ltd. 2020 35
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.

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”
36
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

Building Blocks to an Effective Roadmap
37
Why? Who?
How? What?
When?
What are the key business drivers?
Think both “Offense” & “Defense”
Who are the key stakeholders who will benefit?
Who are the Data Stewards who can be
“discovered” in the organization?
How will you organize the Data Governance team(s)?
Will this be Hierarchical, Federated?
Collaborative or Standards-Driven?
What data needs to be governed?
Is this structured or unstructured?
Real-time or batch?
In which platforms or systems?
When will you roll this out?
What is the timing and cadence or actions and
deliverables?
Are there other key initiatives it’s important to align
with?

Summary
38
• Aligning Data Strategy & Data Architecture with business drivers & goals is key to success
• The blurring of “Data” and “Business” will continue
• Adapt your data architecture for both innovative & legacy technologies
• Consider whether your Data Strategy focuses Primarily on “Offense” or “Defense”
• Design core KPIs and metrics to track business value & success
• Develop a practical roadmap that balances “quick wins” with long-term foundational
architecture

White Paper: Trends in Data Management
• Download from www.globaldatastrategy.com
• Under ‘Whitepapers’
• Also available on Dataversity.net
39
Free Download

DATAVERSITY Data Architecture Strategies
• January 23 Emerging Trends in Data Architecture – What’s the Next Big Thing?
• February 27 Building a Data Strategy - Practical Steps for Aligning with Business Goals
• March 26 Cloud-Based Data Warehousing – What's New and What Stays the Same
• April 23 Master Data Management – Aligning Data, Process, and Governance
• May 28 Data Governance and Data Architecture – Alignment and Synergies
• June 25 Enterprise Architecture vs. Data Architecture
• July 22 Best Practices in Metadata Management
• August 27 Data Quality Best Practices
• September 24 Data Virtualization – Separating Myth from Reality
• October 22 Data Architect vs. Data Engineer vs. Data Modeler
• December 1 Graph Databases: Practical Use Cases
40
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.
41
Data-Driven Business Transformation
Business Strategy
Aligned With
Data Strategy
Visit www.globaldatastrategy.com for more information

Questions?
42
• Thoughts? Ideas?
www.globaldatastrategy.com

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

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