The document discusses the need for organizations to actively drive user adoption of big data platforms in order to succeed. It states that migrating from traditional data warehouses to big data platforms is a multi-phased journey that requires holistic adoption campaigns. A core adoption program is recommended to bridge the gap between end users and platform teams by providing user onboarding, tools/data support, communications, and prioritizing the platform roadmap based on user feedback. Successful adoption programs can decrease legacy users, increase active users and data quality/trust, and reduce infrastructure costs.

1. ACCELERATE
TO THE NEW
ACCELERATING BIG DATA ADOPTION

2. Copyright © 2018 Accenture. All rights reserved. 2
YOU MUST ACTIVELY
DRIVE USER
ADOPTION TO
SUCCEED WITH YOUR
BIG DATA PLATFORM
IT’S NO LONGER ENOUGH TO BUILD IT AND RELY ON
THE PROMISE OF BIG DATA FOR USERS TO COME

3. THE BIG DATA JOURNEY TO THE NEW
IS MULTI-PHASED
Copyright © 2018 Accenture. All rights reserved. 3
The journey begins by migrating from the classical data warehouse and requires a
holistic adoption campaign to fully transition to the “new”
Legacy data warehouses drive all
functions across production use
cases, analytics, reporting,
regulatory, finance, etc.
Migration to Big Data begins with a
data lake supporting production
use cases and analytics with legacy
data warehouses filling key
regulatory and reporting functions.
Hybrid architectures leverage on-
premise data lakes as the system of
record and cloud computing power
to support large scale Artificial
Intelligence and Machine Learning.
1 2 3
1
BIG DATA
LAKE
NEXT GENERATION
LAKE & CLOUD
CLASSICAL DATA
WAREHOUSE

4. A SUCCESSFUL JOURNEY CAN DRAMATICALLY CHANGE
THE WAY YOU OPERATE
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The flexibility gained from adopting the “new” will translate to significant benefits for
your organization
REDUCED INFRASTRUCTURE COSTS
• OPTIMIZE STORAGE COSTS AND VALUE OF YOUR EDW
• CAPEX TO OPEX SHIFT
NEW SOURCES OF REVENUE
• REACH NEW/ADDITIONAL CUSTOMERS
• NEW TRENDS AND MONETIZATION CAPABILITIES
INCREASED AGILITY & PRODUCTIVITY
• FASTER DELIVERY OF SERVICES
• PATH TO NEW CAPABILITIES

5. BUT NEW CHALLENGES ALONG THE JOURNEY IMPACT
ADOPTION ACROSS THE ENTERPRISE
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Big Data Platforms present unique challenges that slow efforts to move away from legacy
and into the new
MORE SOPHISTICATED AI/ML
TECHNIQUES
Advanced ML/AI require tuning and
user-defined parameters
MORE COMPLEX DATA
GOVERNANCE
Changing regulations make
accessing the right data harder
LARGER DATA ECOSYSTEMS
New ‘data lakes’ expose users to huge
amounts of datac
STEEPER LEARNING CURVE
Less mature and inherently more complex
tools require more advanced training
LESS ENTERPRISE SUPPORT
Open source adoption has left
user support to internal teams PLATFORM
ADOPTION

6. ADOPTION REQUIRES A MULTI-PRONG PROGRAM
THAT INCLUDES USERS AND PLATFORM TEAMS
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Embedding a Core Adoption Program bridges the gap between end users and the
platform teams to actively drive user adoption and satisfaction
PLATFORM TEAM
END USERS
USER ACCESS &
ONBOARDING
TOOLS & DATA
SUPPORT
COMMUNICATIONS &
CHANGE MANAGEMENT
DATA QUALITY &
TRUST
PLATFORM ROADMAP &
FEATURE PRIORITIZATION
CORE
ADOPTION
PROGRAM
User feedback
Best practices
KEY SUCCESS FACTORS
1 2 3 4 5

7. MIGRATING FROM THE CLASSICAL DATA WAREHOUSE
IS THE FIRST BIG HURDLE
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1 2 3
1
CLASSICAL DATA
WAREHOUSE
CURRENT BIG DATA
LAKE
NEXT GENERATION
LAKE & CLOUD
Migration from a classical data warehouse to Big Data lake requires in-depth user
support as this phase often faces the most resistance
USER ACCESS &
ONBOARDING
TOOLS & DATA
SUPPORT
COMMUNICATIONS &
CHANGE MANAGEMENT
DATA QUALITY &
TRUST
PLATFORM ROADMAP &
FEATURE PRIORITIZATION
Develop end-to-end
onboarding process
Clarity & support for end
users on analytics toolset and
available data
Deliver consistent and
informative communications
to create awareness and gain
buy-in
Ensure only high quality,
trustworthy data is made
available to end users
Prioritize feature development
based on user feedback and co-
create roadmap with users
1 2 3 4 5

8. EVOLVING FROM CURRENT BIG DATA LAKES TO THE
NEW, WHERE IT MAKES SENSE, IS STEP TWO
Copyright © 2018 Accenture. All rights reserved. 8
1 2 3
1
CLASSICAL DATA
WAREHOUSE
CURRENT BIG DATA
LAKE
NEXT GENERATION
LAKE & CLOUD
Transformation to the new requires a fit-for-purpose strategy to take advantage of scale
and speed offered by the cloud
USER ACCESS &
ONBOARDING
TOOLS & DATA
SUPPORT
COMMUNICATIONS &
CHANGE MANAGEMENT
DATA QUALITY &
TRUST
PLATFORM ROADMAP &
FEATURE PRIORITIZATION
Analyze end-user usage
patterns to determine who to
migrate to the cloud
Develop a migration strategy
for data to the cloud and
tailored tools support
Deliver consistent and
informative communications
to create awareness and gain
buy-in
Holistic data trust approach
working with end-users along
the way to data in the cloud
Understand business unit
priorities and long-term vision
to create a path for future
cloud-native capabilities
1 2 3 4 5

9. EXAMPLE COMPONENTS OF AN ADOPTION PROGRAM
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Stage
2

Stage
1
USER ACCESS &
ONBOARDING
TOOLS & DATA
SUPPORT
COMMUNICATIONS &
CHANGE MANAGEMENT
DATA QUALITY &
TRUST
PLATFORM ROADMAP &
FEATURE PRIORITIZATION
• User consumption
requirements analysis
• User access provisioning
• Cloud set-up
• Training content
development
• Training delivery & logistics
• Historical data migration
strategy & approach
• Data classification
• Baseline query execution
• Schema conversion &
optimization
• Communications strategy
• Leadership alignment
• Executive scorecard
• User feedback channels
• User satisfaction surveys &
dashboard
• Data filtering
• Migrated data alignment
check
• Operational and data
comparison reports
• Data reconciliation
• Intelligent data quality
enrichment
• Product development
support
• Security operations
• Cloud optimization services
• User proficiency profiles
• User workflow analysis
• Data readiness assessment
• Platform access support
• Team prioritization
• Training content
development
• Training delivery & logistics
• Legacy data mapping
• Legacy code conversion &
migration
• Dedicated virtual migration
support
• In-person 1:1 office hours
• Reference guides and job
aids
• Digital code library
• Communications strategy
• Leadership alignment
• Executive scorecard
• User feedback channels
• User satisfaction surveys &
dashboard
• De-provision tracking
• Multi-factor data quality
assessment
• Data trust root cause
analysis
• Data quality escalation
• Data quality SWAT team
• E-2-E data lineage mapping
• Data ontology
• Data standardization and
clean-up
• Test mapping with test data
• Product development
support
• Platform advocacy group
• Design-led roadmap creation
Stage
3

Stage
2

10. WHAT DOES A SUCCESSFUL ADOPTION PROGRAM
LOOK LIKE
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0
2
4
6
0.5 1 1.5 2 2.5 3 3.5 4 4.5 5 5.5
x% 3x%
DECREASED # OF
LEGACY USERS
INCREASED # OF
ACTIVE USERS
INCREASED NET PROMOTER SCORE (NPS) FOR
THE NEW PLATFORM
REDUCED INFRASTRUCTURE
COSTS
Integrity
Accuracy
Timeliness
Conformity
Completeness
Consistency
INCREASED DATA QUALITY
& TRUST

11. About Accenture
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Key Contacts
Ramesh Nair
Managing Director, Financial Services
Leader, Applied Intelligence
ramesh.a.nair@accenture.com
Huendy Espinal
Manager, Financial Services
Applied Intelligence
huendy.espinal@accenture.com