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

Fast Changing Regulations with strict deadlines

Complicated Financial InstrumentsData Silos High Data Redundancy

Reconciliations Between Front-Middle-Back OfficeExpensive Infrastructure Cost

• 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 costCreated the “Painting the Golden Gate Bridge” Syndrome

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:

Increase Insight into Risk

Reduce processing CostMeet Strict Regulatory DeadlineIncrease 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 fromdisparate information sources, regardless of variations in underlying datastructures

• 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 internalCorporate Data

• Users Ask For What They Want In Familiar Terms

No Complex SQL

• Better Time To Market And Development and Support Cost Savings

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

2 hrs Presentation

½ hour Product Demo

• Phase II: Quick Pilot - 5 Days (Free )

Gather Real Data from Client (static)

Create Customized Demo

• Phase III: POC - 8 weeks (Cost depends on # of people assigned)

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

Technology

llc big data

axis technology

data warehouses

data model

data needs

big data approach

unstructured data

complex data challenges