DAS Slides: Building a Future-State Data Architecture Plan - Where to Begin?

Copyright Global Data Strategy, Ltd. 2019
Designing a Future State Data Architecture:
Where to Begin?
Donna Burbank
Global Data Strategy, Ltd.
December 3rd 2019
Follow on Twitter @donnaburbank
Twitter Event hashtag: #DAStrategies

DataStax + Apache
Cassandra™
Building a Foundation for Modern Data Architecture
Louise Westoby
Senior Director, Product Marketing
@lwestoby

Data Architecture Has Evolved
3
Today
CLOUD
1990s1970s
CLIENT-SERVERMAINFRAME

Modern Apps
Require
Modern Data
Management
4
What’s needed is a
modern database
ALWAYS-ON
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REAL TIME
CAN YOUR LEGACY DATA
SYSTEMS KEEP UP?

Database Architecture Matters
5
High Availability
No more scheduled outages for
upgrades. Always available.
Key Design Principle: Design with the understanding that system/hardware failures can and will occur
Flexible Deployment
Options
Can run in any data center – on
premises, hybrid cloud, multi cloud.
Data Sovereignty and
Security
Globally distribute data without
compromising security.
No Single Point of Failure
Out of box data replication for fault
tolerance and global distribution.
Scalability
Continuously available at all
time zones, at all times.
Cost of Deployment
and Management
Control rising and
unpredictability of costs.

6
Modern Database Foundation – Apache Cassandra
#1 DATABASE
for scale, uptime,
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ONLY MASTERLESS
ARCHITECTURE
among leading
DBMS vendors
#1 Contributor to Apache Cassandra
Develop and contribute all open source Cassandra drivers
Developed and updated from the open source Cassandra
project
Best distribution of Cassandra for production

DataStax is Uniquely Suited for Modern Apps
DataStax Masterless Architecture
No single
point of failure for
100% uptime
Predictable
performance with linear
scalability and low
latency data access
Single toolset
to manage cloud and on
premise developments
Automatic global data
distribution for hybrid and
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DataStax customer
deployments are in
the public cloud
>60%
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All of Your Workloads Seamlessly Handled by One
Database
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MULTI-WORKLOAD DATABASE
SUPPORT
Native graph database capabilities allow you to unlock the value behind your data and all the
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which is a requirement for most modern applications today.
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for geospatial, full-text, and advanced search operations.
In-memory engine delivers the fastest possible response times for data that is constantly accessed.

9
Macy’s needed to invest in a positive and engaging customer experience
across all channels in an effort to attract and retain customers
online and in-store for customer retention and business revenue.
STRATEGY
Adopt a multi-cloud
strategy with IBM with
the option to add
other CSPs
Leverage DataStax
for omnichannel
catalog service
OUTCOME
Service scales up to millions of universal
product codes and million requests per
second, with a 100 ms response time.
A seamless online and mobile customer
experience across multi-cloud infrastructure
Easily adding new CSPs to existing cloud
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Macy’s needed a flawless data
management platform to power its
omnichannel catalog – especially
during the busy holiday season.
The Challenge

Learn About
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10

Global Data Strategy, Ltd. 2019
Donna Burbank
12
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 - on demand Building a Data Strategy - Practical Steps for Aligning with Business Goals
• March 28 - on demand Data Modeling at the Environment Agency of England - Case Study
• April 25 - on demand Data Governance - Combining Data Management with Organizational Change
• May 23 - on demand Master Data Management - Aligning Data, Process, and Governance
• June 27 - on demand Enterprise Architecture vs. Data Architecture
• July 25 - on demand Metadata Management: Technical Architecture & Business Techniques
• August 22 - on demand Data Quality Best Practices (w/ guest Nigel Turner)
• Sept 26 - on demand Data Catalogues: Architecting for Collaboration & Self-Service
• October 24 - on demand Data Modeling Best Practices: Business and Technical Approaches
• December 3 Building a Future-State Data Architecture Plan: Where to Begin?
13
This Year’s Lineup

• 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
14
Join us in 2020

What We’ll Cover Today
• With technology changing at an ever more rapid pace and
business requirements ever-evolving to meet the needs of the
market, building a future-state Data Architecture plan can be a
challenge.
• This webinar focuses on practical ways to balance technology and
business needs as you develop your future-state architecture for
the coming years.
• How do we define Data Management in today’s data
ecosystem?
• Which are the hot technologies to adopt?
• What might be a fad or passing trend? Which are on their
way out?
• How can a data architecture support my business goals?
• Content is based on research from a 2019 DATAVERSITY survey on
“Trends in Data Management”.
15

What is Data Management?
The DAMA Data Management Body of Knowledge (DMBOK), defines data architecture as the following:
“Data Management is the development, execution, and supervision of plans, policies, programs, and
practices that deliver, control, protect, and enhance the value of data and information assets throughout
their lifecycles.”
16
DMBOK Definition

What is Data Management?
Survey respondents also provided a range of relevant definitions including:
“Data Management describes people, process, and technology to optimize, protect, and
leverage data as an asset.”
“Data Management is an organization capability supported by tools, processes, standards,
and people.”
“Data Management makes enterprise data effective and efficient by supporting business
activities.”
17
Survey Respondents Provided a Range of Views

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

Implementation Now & In the Future
• The Top Data Management components currently
implemented are :
• Business Intelligence and Reporting: 87.02%
• Data Warehouse: 86.55%
• Data Security: 85.95%
• Data Integration: 70.37%
• Document Management: 70.33%
• Data Governance: 61.11%
• Data Quality: 61.29%
• Those planned in the next 1-2 years include:
• Semantic Web Technologies: 76.00%
• Data Virtualization: 63.24%
• Data Science (Including AI or Machine Learning):
54.74%
• Big Data Ecosystems: 53.42%
• Self-service Analytics: 52.63%
• Metadata Management: 52.43%
• Data Governance: 38.89%
19

a number of respondents mentioned Data Governance in their comments as a way to align the various stakeholders around
common goals
Business Goals & Drivers
• Analytics and Reporting continue to lead the
business drivers for data management.
• Top drivers include:
• Gaining insights through reporting and analytics: 79.70%
• Saving cost and increasing efficiency: 68.42%
• Reducing risk: 66.92%
• Improving customer satisfaction: 58.65%
• Driving revenue and growth: 57.14%
• Supporting digital transformations: 53.38%
20
Gaining Business Insight through Analytics and Reporting continues to be a main business driver for today’s organizations.

Data is an Asset, but Communication & Quality Remain an Issue
• While the majority of organizations see
data as an essential asset, and manage
security and compliance:
• All stakeholders across the organizations do
not take part in data management
• Communication is an issue
• Data Quality continues to be a challenge
• Formal data management metrics are not
tracked
21

common goals
Who is Driving Data Management in an Organization?
• While Technical Roles still lead Data
Management activities, Business
Stakeholders are playing a larger part.
• From those who listed “Other”, Data
Governance Lead was a common
response.
22
Notably, a number of respondents mentioned Data Governance as a way to align various stakeholders around common goals

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.
23
a

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.
24
Relational Databases still dominate the landscape, with a higher focus on Cloud Adoption.

Moving to the Cloud: Pros and Cons
25
While organizations are moving to the Cloud for better scalability, concerns regarding security & privacy remain.

Models & Architecture Help Manage Disparate Data Platforms
26
What Types of Models/Diagrams do you use in your Data/Enterprise Architecture?

Prioritizing Efforts for 2020
27
What does this mean for your Data
Architecture Plans for 2020 and
Beyond?

Global Data Strategy, Ltd. 2019 28
Key Steps to Creating a Data Management Program
• The following steps should be included when creating a data management 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 Data Maturity
Assess the data maturity of the
organization across all aspects of
data management.
Map Priorities to 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”
29
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
30
Why? Who?
How? What?
When?
• What are the key business drivers?
• Think both “Offense” & “Defense”
• What KPIs can be monitored to show ROI?
• Who are the key stakeholders who will benefit?
• Who is an executive champion?
• Who are the Data Owners & Stewards helping
to support data governance?
• How will you organize the Data Governance
team(s)?
• How are business capabilities aligned with data
management priorities?
• What data needs to be managed & prioritized?
• 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?
• What are some potential “quick wins”?

Summary
• A Robust Data Architecture supports managing Data as a Strategic Asset in order to gain business
insights
• Reporting and Analytics are key business initiatives
• With growing interest from business users, more roles than ever are involved in Data Architecture
decisions, driving the need for collaboration.
• Organizations are faced with the challenge of making sense of a diverse data technology landscape
• Relational Databases are by far the leader in use by most enterprises
• The move to the Cloud lends to both Cost Savings & Scalability as well as Security & Privacy concerns.
• With a wide variety of options available, fit-for-purpose solutions are key
• Models and metadata are more important than ever in gaining an understanding of both business
requirements & technical implementation.
• A successful data management program and architecture requires a balanced mix of people, process,
technology, and governance

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.
32
Data-Driven Business Transformation
Business Strategy
Aligned With
Data Strategy
Visit www.globaldatastrategy.com for more information

• 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
33
Join us in 2020

White Paper: Trends in Data Management
• Download from www.globaldatastrategy.com
• Under ‘Whitepapers’
• Also available on Dataversity.net
34
Free Download

Questions?
35
• Thoughts? Ideas?
www.globaldatastrategy.com

DAS Slides: Building a Future-State Data Architecture Plan - Where to Begin?

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DAS Slides: Building a Future-State Data Architecture Plan - Where to Begin?