Leverage analytics-driven capabilities to differentiate in a highly commoditized market.

1. Operationalize
analytics through a
complete modern
data strategy
Kamal Kishore
Director, Analytics
& Big Data

2. There is a drive towards innovation fueled by customers,competitors and regulators.
Innovation Imperatives Opportunities for Differentiation Technology Enablers
Customer
Centricity
Risk &
Compliance
Operations
Customer Experience – Faster Turnaround New wave of Competitors Risk and ComplianceTechnical Debt and Limited Collaboration
Time to
Market
Risk &
Compliance
Products
and
Services
Tailored
Experience
Challenges
Cyber
Security
Big Data &
Analytics
Tech
Modernization
Mobile,
Social,
& Cloud

3. Customer
• Market sizing & segmentation
• Acquisition strategy
• Cross-sell and Upsell
• Marketing mix optimization
• Campaign effectiveness
• Sales effectiveness
• Social media & Digital
• Customer segmentation and profiling
Risk
• Credit risk analytics
• Fraud prediction & mitigation
• AR management
• Default management
• Collection analytics
• Trade cancelation and settlement
• Social media reputation management.
• Information privacy and security
Operations
• IT operations optimization
• Call centre optimization
• Workforce optimization
Companies need to leverage analytics-driven capabilities to differentiate in a highly
commoditized market..
Acquire, Grow, Retain and Satisfy
Customers
Manage Risk, Fraud & Regulatory
Compliance
Improve Operational Effectiveness
and Efficiency

4. A single, consistent, and authoritative view of an organiztation’s data
A roadmap for incremental and agile delivery of data improvements
A well-positioned and authoritative data delivery environment
Scalable solutions for business needs
Support for operational, tactical, and strategic data requirements
Trusted, relevant, available, secure, and compliant data
Low cost of data ownership
And act fast to build a
technology infrastructure
that consists of:

5. Companies face a number of challenges with existing technology systems
• The capacity of traditional data storage
and processing systems acts as a hurdle
in leveraging data efficiently.
• Volume, variety, velocity, veracity of data
that companies manage today needs
re-evaluation and modernization of
underlying data strategy and architecture.
Capacity
Modern data architecture comes to the rescue.
• Historical methods for structuring,
organizing, storing and accessing data
were designed for structured data.
• The growing number of types and formats
of source data necessitates modernization
of data architectures and their underlying
data standards.
Unstructured Data
• Rapidly changing regulatory environment
calls for business agility.
• Meeting the needs of changing
regulatory/legal environments require
an agile and responsive data
architecture.
Regulatory Changes
Regulations around data privacy, access,
usage, and storage are increasing. The
potential fines and legal ramifications
associated with data breaches and
inappropriate use pose a high business
risk.
On an average, 70 to 80 percent of an
organization’s data is unstructured.
The percentage of organizational
data that is expected to be used for
analysis will grow significantly.
Today 2020
22% 37%
70% 80%
(Baseline Magazine, 2015)

6. Modern data architecture is designed around 3 key pillars
People
• Business first, team effort
• Manage accessibility of data
• Create & support data architecture
artifacts & activities
• Explore, iterate, share & collaborate
• Application of discoveries &
operational insights
• Consider analytics as omnipresent
and ubiquitous
Process
• Embrace agile, fail fast
• Keep individuals & interactions above
processes & tools
• Choose customer collaboration over
contract negotiation
• Respond to change instead of following
the plan
• Collect, create, maintain a scalable data
strategy
• Plan for security and data governance
for enterprise data model, related
architectures, and data delivery
Technology
• Scalable for new
structured/unstructured data
sources, complex analytics, new
machine learning models, real-time
ingestion and consumption over
time
• End-to-end data lineage maintenance
• Working software over comprehensive
documentation
• Design that supports iterative
development

7. A complete modern data
architecture combines 8
key components.
1
2
3
4
5
6
7
8
Identification of the most valuable types of data
Real-time foundation
Data governance
Master data management strategy
Security
Data positioning as a service
Build systems to change, not to last
Self servicing environments

8. Operationalize the vision with the right framework,process,and people.
Technology Framework Agile Analytics Development T–Skilled Resources
Business Context
Iterative
Learning Cycles
Business
Use Case
Model
Testing
Model
Training
Model Validation
Technology
Enablement
Hypothesis
Model
Refinement
Data Scientist,
Business Analyst
Analytics Modelling
Data Processing
Data Ingestion
Data Storage Big Data
Insight Consumption
Customer,
Facility Manager
Data Science
Technology
Relational
DB’s
Files Social Networks
Enterprise
DW
Web Logs,
Click Stream

9. Reference framework to support enterprise data strategy
Deliver performance gain by bundling process-aware platforms and intelligent services
Business Solutions User Experience Analytics
Analytics Environment
Predictive Analytics Self-Service BI Canned ReportsData Visualization
Collaborative BI Personalized views Mobile BISegmentation
Cognitive Computing
Raw Data Island
Unstructured Data Processing
Relational DB’s Enterprise DW Web logs, click stream Files Social networks
Message & Web Services
Ingestion Toolkit
Unstructured Data Toolkit Relational DB Toolkit Social Media Toolkit
Structured Data Processing
Processed Data Island Historical Data IslandCollaborative & Exploratory
Data Island
NLP/Text Analytics Machine Learning Forecasting/Simulation
Simulations Trends
Prescriptive ROI
Visualization Environment
DataGovernance
DataQuality
AdaptorToolkit
InfrastructureSecurityMonitoringMetadata
AdministrationConsole
Risk & Compliance Analytics Operations Analytics
Data Consumption
Analytical Engine
Data Storage
and Provisioning
Data Processing
Data Ingestion
Data Sources

10. About Nagarro
Nagarro provides technology services for digital disruption to both industry leaders and challengers. When our clients want to move fast and make things, they
turn to us. We combine design, digital and data to help them outperform the competition. We distinguish ourselves by our agility, imagination and absolute
commitment to our clients’ business success.
Some of our clients include Siemens, GE, Lufthansa, Viacom, Estēe Lauder, ASSA ABLOY, Ericsson, DHL, Mitsubishi, BMW, the City of New York, Erste Bank, T-Systems,
SAP and Infor. Working with these clients, we continually push at the boundaries of what is possible to do through technology, and in what time frame.
Where to go from here? Companies must define their immediate as well as long-term business goals, identify and prioritize business problems. Next, map
the current state and readiness of data, technology, people, infrastructure and communication. And finally, start small and build on tangible successes.
Explore our capabilities and offerings in big data and analytics.
Align your business strategy to a value-driven
analytics framework
Explore how you can process and leverage
large amounts of data with Nagarro.
Engage customers with a triple-alignment
strategy for banking
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Today, we are more than 4000 experts across 15 countries. Together we form Nagarro, the global services division of Munich-based Allgeier SE.