Big Data and the Semantic Web

© Copyright 2014 Axis Technology, LLC
Big Data and the Semantic Web

Data Challenges
• Financial Firms Face Complex Data Challenges
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Fast Changing Regulations with strict deadlines
Complicated Financial Instruments
Data Silos
 High Data Redundancy
Reconciliations Between Front-Middle-Back Office
Expensive Infrastructure Cost
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• Traditional Methods Have Been Proven Expensive
 Based on the assumption that Data needs to be Modeled before being consumed
 Lack Of Agility
 Expensive to Modify
Created Proliferation of Data Marts and Data Warehouses with Large ongoing cost
Created the “Painting the Golden Gate Bridge” Syndrome
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10© Copyright 2012 Axis Technology, LLC

BIG Data
• Introduced to describe the management of terabites of data
 Introduced by Google and Yahoo, adopted by Facebook, Twitter, Amazon
 Hadoop, Lucene , MapReduce, MapR, NoSQL, Cassandra, Pig, HDFS, HBase
 Enable The Management of Unstructured or Semi-Structured Data
 Human readable and traditionally hard to be processed by machine
 Introduced the “Google Experience”
• Big Data applications could enable financial firms to:
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Increase Insight into Risk
Reduce processing Cost
Meet Strict Regulatory Deadline
Increase Agility in Data Management
• The benefits can generate billions of dollars in increased revenues and reduced costs

The Solution
• Extend the Big Data approach to structured data
• Create an Agile Data Management environment
• Extend the Google experience to Corporate Data
• Introduce Web 3.0 concept of Ontology and RDFs to Corporate Data
 new method for accessing, combining, using and sharing data from
disparate information sources, regardless of variations in underlying data
structures
• Partnership with vendors in the Semantic Web technology space
 Fit Solution to Client Requirements

The Business Value
• Combine Structured and Un-Structured Data
• Non Technical Users Can Access Any Data Any Time Regardless of Its Location
 After an Initial Investment of creating or mapping existing ontology (Ex: FIBO) to internal
Corporate Data
• Users Ask For What They Want In Familiar Terms
 No Complex SQL
• Better Time To Market And Development and Support Cost Savings
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Avoid the tedious and time consuming of examining all use cases to create a Data Model
2:1 Ratio Saving In Development Cost, even more for support on going cost
• Flexibility
 Can be incrementally deployed and extended, showing value as you go

The Approach
• Phase I: Introduction to a Client - Free Seminar
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2 hrs Presentation
½ hour Product Demo
• Phase II: Quick Pilot - 5 Days (Free )
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Gather Real Data from Client (static)
Create Customized Demo
• Phase III: POC - 8 weeks (Cost depends on # of people assigned)
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POC will be a limited version of the actual production system
Could be productionized
• Phase IV: Final Engagement (Fixed Cost TBD)
 Shared the implementation Risk

Traditional vs. Semantic

Traditional Approach

Semantic Approach

Examples of Initial Investment Cost

Examples of BAU Cost - 3 years

Examples of 3 Years TCO Analysis

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Big Data and the Semantic Web

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