Introduction to Big Data & Analytics

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August 22, 2014
Big Data & Analytics - Introduction
Faculty Development Program
@BIET, Davangere
Prasad Chitta

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#FDPBigDataAnalytics
Discussion Topics
∞
•Data & Processing – small and BIG
∞
•Big Data, Data Science and Art
∞
•Analytics and Optimization

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Data – A historic perspective

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The data processing lifecycle
Sensing
Acquiring,
Validating
Storing
Transactional
Update
Operational
Reporting,
Dashboards
ETL,
Warehousing
OLAP reporting
Analytics
Archiving &
Purging

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Aspects of ‘Data’
Data
Meta data
Master Data
Reference
Data
Integration,
Migration
Quality
Visualization

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Data Scenarios…
• New product design
• Simulation
• Knowledge
representation
No Data
• From normalized
OLTP systems
• Variables , mostly
numbers
Structured
Data • Unstructured
• Quickly varying
• Mostly alpha-
numeric
BIG data

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Processing of data
Serial, bring
data to process,
traditional
Parallel, take
process to data,
modern

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The Data Explosion
http://pennystocks.la/internet-in-real-time/

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Landing &
Staging
Integration
Store
Semantic
(Logical &
Physical)
In-Memory
Databases
Visualization
Tools &
Framework
System of
Records
ETL / ELT
Big Data – Ingestion to insights

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The Big Data Landscape

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Analytical Processing of Data
Operational
Reporting /
MI
OLAP / BI / ETL
Analytics
Content
(Unstructured)
Structured
Analytics
Descriptive (Uni
or bivariate)
Diagnostic or
Inquisitive
Discovery
Predictive
Predictive
Statistical Techniques Machine Learning

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Analytics Landscape Overview
SQL Analytics Descriptive Analytics Data Mining Predictive Analytics Simulation Optimization
Count Univariate Distribution Association Rules Classification Montecarlo Linear Optimization
Mean Central Tendency Clustering Regression Agent based modeling Non-linear Optimization
OLAP Dispersion Feature Extraction Forecasting Discrete Event modeling
Spatial
Machine Learning
Text Analytics
BI Advanced Analytics

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Business Value - Analytics Matrix
OLAP Reporting
Drill-thru
Drill-Across
Insights/Limited What-if
Actionable insights
Descriptive Modeling
Describe historical event
Predictive Modeling
Baseline Demand
Impact of Causal Factors
BusinessValue
Optimization
Linear/Non-linear
programming & Simulations
Standard Reporting
Sales, Inventory, Business
Performance
Data Management
Internal, Syndicated,
Decision Support Decision Guidance  Advanced analytics
Why something happened?
What will happen?
What is the best that can happen?
What happened?
A
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a
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y
t
i
c
s
R
T
B
I
DSS
DSS – Decision Support Systems,
RTBI – Real Time Business Intelligence
Analytics Value Chain

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Focus Areas for Insurance Analytics
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

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Traditional Analytics Process
Extracting and
consolidating data
from various
sources and
databases
Generating
Random samples
to create
Development &
Validation
Samples
Understanding the
data & nature of the
variables
 Distribution
 Relationships
 Differences
Cleansing &
Preparing the data
for Modeling:
 Outlier, Missing
Treatment
 Variable
Transformation,
Derivation
Model
Building
DB2DB1
Final
Modeling
Universe
Dev
70%
Val
30%
Data Consolidation SamplingDiagnostics Data Prep Model Building

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Data Scientist - Skills needed
Business and Domain knowledge
Planning & Architecting Data Science Solutions
Statistical Modeling
Technology Stack – R, Hadoop
Text Mining, Social Network Analysis and Natural Language Processing
Methods and Algorithms in Machine Learning
Optimization and Decision Analysis
Story telling and Visualization
Privacy, Security and Ethical Concerns

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