June 4 th 2015 Big Data’s Big Picture Industry Perspectives Prasad Chitta All the views expressed in this presentation are purely of Author. They do not represent any official view or product direction of the Employer or Clients author works with.
Jul 26, 2015
1
June 4th 2015
Big Data’s Big PictureIndustry Perspectives
Prasad Chitta
All the views expressed in this presentation are purely of Author. They do not represent any official view or product direction of the Employer or Clients
author works with.
2#BigdataIndustryPerspectives
Data
I n f o r m a ti o n T e c h n o l o g y
Presentation
Process
Data
Systems of Recor
ds
Systems of Engagement
Structured
Semi-structured
Unstructured
Batch/Real-time
Response time
Through put
Quality
Audit
Security
Responsive
Context sensitive
User centric
3#BigdataIndustryPerspectives
Context setting – Big Data
Business Verticals
Core TechnologyIT Services
Cloud Computing
Big Data
4#BigdataIndustryPerspectives
Three Perspectives
A•Business – Traditional, New
B•Core Technology Products
C• IT Services
5#BigdataIndustryPerspectives
Business Perspective – Insurance Industry
Focus Areas for Insurance Analytics
Marketing Analysis•Customer Lead Management•Campaign Management
•Channel Profitability Analysis•Social Media Analytics
Customer Management •Customer Segmentation•Customer Churn Analysis •Lifetime Value Analytics•Cross-sell & Up Sell Analytics
Claims Management•Fraud Analytics & Models•Subrogation Models•Claims Analysis
Sample KPI and Business Drivers
• Lead conversion rate• Channel ROI or Effective ness• Market share for each channel• Customer Satisfaction Index
• Profiling of customers • Customer Attrition/Retention Rate• % of Repeat Business from customer• Customer Net worth and Life time value
• Loss due to Fraudulent claims • Loss ratios• Claims Process Cycle ratios• Claims reserves and Provisions
Underwriting / Risk Management• Risk Assessment and Evaluation• Automated Underwritings•Re Insurance Retention Analysis
• Underwriting Margins / Profit Margins• Capacity required for Underwriters• Improve the retentions and profit margins
Insurance Business Analytics for effective decision making by analysing the historic data
6#BigdataIndustryPerspectives
Digital Engagement based (new) business
facebook, uber, airbnb, Netflix etc., Shared Economy
Customer Centric Enterprise Social by default Digital crypto currencies
@
https://www.linkedin.com/pulse/battle-customer-interface-tom-goodwin-5985813315086008320
7#BigdataIndustryPerspectives
Three Perspectives
A•Business - Traditional, New
B•Core Technology Products
C• IT Services
8#BigdataIndustryPerspectives
Core Technology - The data processing lifecycle
Sensing
Acquiring, Validating
Storing Transactional Update
Operational Reporting,
Dashboards
ETL, Warehousing OLAP reporting
Analytics
Archiving, Purging
9#BigdataIndustryPerspectives
Analytical services landscape
Analytical Processing of DataOperational Reporting /
MI
OLAP / BI / ETL
Analytics
Content (Unstructured)
Structured
AnalyticsDescriptive (Uni
or bivariate)
Diagnostic or Inquisitive
Discovery
Predictive
Predictive Statistical Techniques Machine Learning
11#BigdataIndustryPerspectives
Three Perspectives
A•Business - Traditional, New
B•Core Technology Products
C• IT Services
12#BigdataIndustryPerspectives
Survey Infographics
http://www.tcs.com/big-data-study/Pages/default.a
spx
13#BigdataIndustryPerspectives
Analytics Value Chain
Business Value - Analytics Matrix
OLAP ReportingDrill-thru
Drill-Across
Insights/Limited What-ifActionable insights
Descriptive ModelingDescribe historical event
Predictive ModelingBaseline Demand
Impact of Causal Factors
Busi
ness
Val
ue
OptimizationLinear/Non-linear
programming & Simulations
Standard ReportingSales, Inventory, Business
Performance
Data ManagementInternal, Syndicated,
Decision Support Decision Guidance Advanced analytics
Why something happened?
What will happen?
What is the best that can happen?
What happened?
Analytics
RTBI
DSS
DSS – Decision Support Systems, RTBI – Real Time Business Intelligence
13
14#BigdataIndustryPerspectives
Data Scientist, Data Artist, Data Philosopher?
Knowledge of
statisticsMathematics
Story telling
Ability to influence without authority
Artistic skills
Business understanding
Operations
knowledge
Architectural
understanding
Solution develop
ment
Tools Enabler
Storage Processing
Business KPI
Optimization
Excellence
Data Scientists
The challenge