1. RApTOR Technical Presentation

2. TeleArc Capabilities
1) Binary Data
2) Mediate Data
3) Unified Format
4) Reconcile large Data
5) Analysis
6) Data Hosting – Applied
7) Reporting
8) Data Slice & Dice
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3. New Data Types
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BI &
Analytics
Business
Data
Web
Logs
Videos
Images
Sensor
Data
3rd Party
Apps

4. What is Big Data?
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What it IS
Unstructured
Petabytes+
Evolution of RDBMS
Many Platforms
Difficult for Analytics
Transactional
Simple or Easy
Structured DW
One Platform
Easy or Fast for Analytics

5. Characteristics of Big Data
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6. Big Data Technologies
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7. Five ways to leverage Big Data’s potential to create Value
1) Creating Transparency – Making Big Data more accessible
across department
2) Enabling Experimentation – Analyze, Identify and discover needs
or opportunities
3) Segmentation Population – Customize products and services to
meet customer needs
4) Replacing human decision making – Minimize decision risk and
improve decision making
5) Innovating new business model, products and services –
Enhance and create new products
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8. Advanced Analytics
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INFORM
ATION
MANAGE
MENT
Leveraging
historical data to
drive better insight
into decision-
making
for the future
FORECASTING
Mine transaction
databases for data of
spending patterns
that indicate a stolen
card..
DATA MINING
TEXT ANALYTICS
Finding treasures in
unstructured data
like social media or
survey tools
that could uncover
insights
about consumer
sentiment
Analyze massive
amounts of data in
order to accurately
identify areas likely to
produce the most
profitable results
OPTIMIZATION
STATISTICS

9. Big Data Analytics seen in following verticals
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10. Companies Spending on Big Data by Global Industry
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11. Business Functions Explored for Big Data Practices
Marketi
ng
Sales
Custom
er
Service
Manufa
cturing
R & D
Distribu
tion
Finance
Human
Resourc
e
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12. Potential benefit Big Data Generate for Marketing
Determining marketing campaign effectiveness
Determining marketing channel effectiveness.
Tailoring marketing campaigns and promotional offers
Determining value customer
Doing customer segmentation
Pricing
Predicting customer Behavior
Determining which product features are valued and not valued
Determining the optimal time to launch marketing campaigns
Monitoring customer and market perception of the company
Discerning customer needs for new product/services
Personalized search results on a company’s website
Monitoring and improving the customer experience on the web or mobile devices
Discerning customer needs for enhancement to existing product and services
Comparing pricing with competitors
Identify new geographic markets for existing products
Marketing to consumers based on their physical location
Understanding competitor’s move (beyond pricing)
Monitoring and improving the customer experience in offline channels
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13. Potential benefit Big Data Generate for Sales
Identifying customers with the most value/ potential value
Identifying cross selling opportunities
Determining optimal sales approaches and techniques
Determining optimal sales offers and messages
Creating more accurate sales forecasts
Sizing and structuring sales territories
Determining which customers to avoid
Analyzing behavior of visitors to company’s ecommerce site to see what they are most
and least interested in
Determining what other purchases customers may be interested in and making
recommendations to those customers
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14. Potential benefit Big Data Generate for Customer Service
Identifying customers who are at risk of dropping our products / services
Analyzing behavior of visitors to company’s website to see what they are most and
least useful
Identifying patterns in customer complaints (both internal in the call center and external)
Identifying trends in customer inquiries
Discerning trends in customer usage of the company’s products / services
Monitoring our products as customers use them to detect manufacturing or design
problems
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15. Product quality and defect tracking
Supply planning
Manufacturing process defect tracking
Supplier components defect tracking
Collecting supplier performance data to inform contract negotiations
Forecasting of manufacturing output
Increasing energy efficiency
Simulation and testing of new manufacturing processes
Enabling mass customization in manufacturing
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Potential benefit Big Data Generate for Manufacturing

16. Monitoring product quality
Identifying customer needs for new products and enhancements to existing products
Testing new product design
Getting continuous customer feedback on products already in market
Testing new products before market launch for viability with customers
Enabling concurrent engineering the simultaneous design, development and
engineering of a product
Conducting open innovation soliciting ideas for new products through the web
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Potential benefit Big Data Generate for R & D

17. Monitoring product shipment
Determining locations of inventory shrinkage
Identifying spikes in logistics costs, where and why they are occuring
Boosting energy efficiency
Identifying supply chain bottlenecks to speed the flow of goods, materials etc
Determining appropriate inventory levels
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Potential benefit Big Data Generate for Distribution

18. Budgeting/ Forecasting/ Planning
Measuring risk
Determining financial amounts for customers
Identifying bad credit risks
Identifying accounting irregularities
Identifying areas of external theft
Identifying areas of internal theft
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Potential benefit Big Data Generate for Finance

19. Improving employee retention by determining who is most likely to leave and trying to
discourage them
Identifying effectiveness of recruiting campaigns
Determining employees to promote and provide other rewards
Gauging employee morale/ engagement
Determining optimal job candidates for a certain job/ building more productive profiles
for hiring
Determining the most valuable employees
Identifying internal mentors who could coach other employees
Finding information in the company’s digital archives
Finding employee with the right knowledge to deal with a company issue
Identifying potential recruits who work outside the company
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Potential benefit Big Data Generate for Human Resource

20. Solutions to drive decisions within different verticals
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21. Enhancing Fraud Detection for Banks and Credit Card Company
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Scenario
Build up to date models from
transactional to feed real time risk
scoring systems for fraud detection
Requirements
Analyze volumes of data with
response time that are not possible
today
Apply analytic model to individual
client, not just client segment
Benefits
Detect transaction fraud in progress,
allow fraud model to be updated in
hours than weeks

22. Social Media Analysis for Products, Services and Brands
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Scenario
Monitoring data from various sources such
as blog, boards, news feeds, tweets and
social media for information pertinent to
brand and products, as well as competitors
Requirement
Extract and aggregate relevant topics,
relationship, discover patterns and reveal
up and coming topics and trends
Benefits
Brand Management for marketing
campaigns, Brand protection for ad
placement networks

23. Store Clustering Analysis in the Retail Industry
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Scenario
Retailer with large number of stores
needs to understand cluster patterns
of shoppers
Requirement
Using shopping patters for multiple
characteristics like location, income,
family size for better product
placement
Benefits
Store specific clustering of products,
clustering specific types of products by
locations

24. Healthcare and Energy Industry
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25. Utilities
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26. Thank You
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