Navigating Financial Big Data Challenges with Semantic Solutions

1. The Road to Big Data Management

2. In a time of drastic change, it is the learners who inherit the future.The learned usually find themselves equipped to live in a world that no longer exists.- Eric Hoffer

3. Data Challenges • Financial Firms Face Complex Data Challenges. • Fast Changing Regulations with strict deadlines • Complicated Financial Instruments • Data Silos • High Data Redundancy • Reconciliations Between Front-Middle-Back Office • Expensive 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 cost • Created the “Painting the Golden Gate Bridge” Syndrome

4. BIG Data • Introduced To Describe The Management Of >Terabytes 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 Cost • Meet Strict Regulatory Deadline • Increase Agility in Data Management • The Benefits Can Generate Billions of Dollars in Increased Revenues and Reduced Costs

5. 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.

6. 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. • 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 • Canbeincrementallydeployedandextended,showingvalueasyougo

7. Who We Are • Strada’s team is unique and specialized . • Members of the Strada team are experts in the Data Management and are specialized in the new field of semantic web technology. • Mr. Mittiga has over 25 years experience developing and implementing corporate-wide, long-term strategic and operational solutions in the Financial Industry. • XXXIncis a leader in providing a full spectrum of Management Services including Information Technology, Business Consulting Solutions, Staff Augmentation, and Project Management in addition to specializing in Enterprise Strategies.

8. Our 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

9. Traditional vs. SemanticExample

10. Traditional Approach.

11. Semantic Approach.

12. Initial Investment Cost.

13. BAU Cost - 3 years.

14. 3 Years TCO Analysis.

15. Thanks.

16. The Road to Big Data Management