Financial firms face complex data challenges due to fast changing regulations, complicated instruments, and data silos. Traditional methods of modeling data before consumption are expensive and lack agility. The document proposes that a semantic web approach combining structured and unstructured data can help firms increase insight, reduce costs, and improve agility by allowing non-technical users to access any data in familiar terms without complex queries. This solution extends big data practices to corporate structured data and introduces ontologies to integrate disparate sources. The approach involves free seminars and pilots before a priced proof of concept and final engagement.

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Big Data and the Semantic Web

2. 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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3. 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
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4. 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
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5. 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
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6. 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
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7. Traditional vs. Semantic
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8. Traditional Approach
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9. Semantic Approach
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10. Examples of Initial Investment Cost
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11. Examples of BAU Cost - 3 years
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12. Examples of 3 Years TCO Analysis
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