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Webinar: How to Drive Business Value in Financial Services with MongoDB
 

Webinar: How to Drive Business Value in Financial Services with MongoDB

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Huge upheaval in the finance industry has led to a major strain on existing IT infrastructure and systems. New finance industry regulation has meant increased volume, velocity and variability of data, ...

Huge upheaval in the finance industry has led to a major strain on existing IT infrastructure and systems. New finance industry regulation has meant increased volume, velocity and variability of data, so-called Big Data. This coupled with cost pressures from the business has led these institutions to seek alternatives. Top tier institutions like MetLife have turned to MongoDB because of the enormous business value it enables.

In this session, learn where and how you should use MongoDB to get the maximum value including specific case studies such as saving $40M in one project.

The use cases are specific to financial services but the patterns of usage - agility, scale, global distribution - will be applicable across many industries.

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  • Mention FS includes cap markets, banking, and insurance. Looking at attendees, cap markets most but also touch on insurance and banking
  • Reg- Cap markets – uncertainty plus changing regs with Dodd-Frank, Basel III, Volkor, etc. More regulatory reporting demands as GreatBanking – pressure from the fed for reportings, TBTFRisk mgmt – financial crisis showing failure of risk mgmt, so changing analytics, and the drive to be intraday, take more factors into accountCost pressure – esp. banking with low interest rates and fees
  • Bringing data together (regulatory, risk, trade repository, etc.) painful with RDBMS => polymorphicAgility to change systems generally is painful => agileRun risk analysis more often towards intraday => performanceStore many years of audit info cheaply but online => scaleData warehouse not timely or performant enough => performanceStuck in expensive contracts without leverage => cost
  • RDBMSs built before OOP, agile, cloud, and big dataData types – social networking and IM compliance; multiple desks, products, geographiesVolumes – store and analyze more data than ever for auditing and risk esp. and at low cost; data warehouse has some data but not how you want it and performant enough
  • 152 partners, growing ~20% monthlyCertification: Cloud, BI/ETL, Analytics, Auditing/SecurityOther partners in BI (e.g., Pentaho, Jaspersoft) with many more comingIBM: Standardizing on BSON, MongoDB query language, and MongoDB wire protocol; integration with Guardium security product; integration with WebSphereRed Hat: Collaborating on a secure architecture for MongoDBInformatica: Integration with ETLAmazon: Easily deploy MongoDB on Amazon EC2; we have worked together to develop reference architectures and to use MongoDB with Amazon’s latest technologies, such as SSD instances and Provisioned IOPS (PIOPS)Rackspace: Rackspace offers a purpose-build database-as-a-service offering for MongoDB (through acquisition of ObjectRocket)Microsoft Azure: We have collaborated on tools to make it easy to deploy MongoDB on Microsoft AzureIntel, EMC, NetApp: We’re certified to work with their hardware. More to come.
  • Good for regulatory reporting, e.g. KYC
  • Good for regulatory reporting, e.g. KYC
  • Then 10x better performance50% less dev time

Webinar: How to Drive Business Value in Financial Services with MongoDB Webinar: How to Drive Business Value in Financial Services with MongoDB Presentation Transcript

  • Driving Business Value in FS with MongoDB Matt Kalan, FS Solutions Architect Email: Matt.kalan@10gen.com Twitter: @matthewkalan
  • 2 • FS requirements today • Traditional approaches • MongoDB approach • Rapid adoption • Technical overview • Best fit usage patterns • Case studies Agenda
  • 3 Trends in FS Driving Change Mobile & Gamification (retail) New Opportunities Regulations Enhanced Risk Management CCP Central Clearing Cost pressures
  • 4 Requirements • Aggregate disparate data • Change apps quickly • Analyze data faster • Store more data • Increase productivity • Reduce TCO drastically Putting Urgency on These IT Requirements Like Never Before
  • 5 Unfortunately, RDBMSs Not Built for These Requirements Data Types & OOP • Unstructured data • Semi-structured data • Polymorphic data Volume of Data • Petabytes of data • Trillions of records • Tens of millions of queries per second Agile Development • Iterative • Short development cycles • New workloads New Architectures • Horizontal scaling • Commodity servers • Cloud computing
  • 6 • Customfield1…100 or separate tables • Caching & ORMs • Expensive hardware and storage • Schema migration • One schema across apps • Application-specific partitioning • Use files instead of databases • Schema change takes 6 months As a Result, Shoehorn Requirements Slow time-to-market Agility lost High cost Business frustrated
  • 7 Now There Is Help 2010 RDBMS Key-Value/ Column Store OLAP/BI Hadoop 2000 RDBMS OLAP/BI 1990 RDBMS Operational Data Datawarehouse Document DB NoSQL
  • 8 Database Landscape • No Automatic Joins • Document Transactions • Fast, Scalable Read/Writes
  • 9 Relational: All Data is Column/Row Customer ID First Name Last Name City 0 John Doe New York 1 Mark Smith San Francisco 2 Jay Black Newark 3 Meagan White London 4 Edward Daniels Boston Account Number Branch ID Account Type Customer ID 10 100 Checking 0 11 101 Savings 0 12 101 IRA 0 13 200 Checking 1 14 200 Savings 1 15 201 IRA 2
  • 10 Have to Manage Change in 3 Places Relational Database Object Relational Mapping Application Code XML Config DB Schema
  • 11 Instead Match the Data in your Application Relational MongoDB { customer_id : 1, first_name : "Mark", last_name : "Smith", city : "San Francisco", accounts : [ { account_number : 13, branch_ID : 200, account_type : "Checking" }, { account_number : 14, branch_ID : 200, account_type : ”IRA”, beneficiaries: […] } ] }
  • 12 Instead Put Data Model in One Place Application Code Relational Database Object Relational Mapping XML Config DB Schema Application Code Rich Queries Geospatial Text Search Map Reduce Aggregatio n
  • 13 No SQL But Still Flexible Querying MongoDB { customer_id : 1, first_name : "Mark", last_name : "Smith", city : "San Francisco", accounts : [ { account_number : 13, branch_ID : 200, account_type : "Checking" }, { account_number : 14, branch_ID : 200, account_type : ”IRA”, beneficiaries: […] } ] } Rich Queries • Find all Mark’s accounts • Find everybody who opened an account last month Geospatial • Find all customers that live within 10 miles of NYC Text Search • Find all tweets that mention the company within the last 2 days Aggregation • What’s the average value of Mark’s accounts Map Reduce • How many customers that have a checking account also have an IRA
  • 14 Operational Database Use Cases RDBMSs Key/Value or Column Stores MongoDB
  • 15 DB-Engines.com Ranks DB Popularity
  • 16 • MetLife Leapfrogs Insurance Industry with MongoDB-Powered Big Data Application – “innovative customer service application…in 90 days…from 70+ existing systems” • 10gen Establishes Financial Services Advisory Group – “ten leading global institutions…including Barclays, Goldman Sachs and MetLife.” • IBM and 10gen Collaborate to Bring Mobile to the Enterprise – “IBM will standardize on BSON, MongoDB wire protocol and query language” • Informatica and 10gen Partner to Expand Data Integration for MongoDB – “use PowerCenter Big Data Edition to access…data stored in the market's leading NoSQL database” Customers and Software Heavyweights Support MongoDB
  • 17 10gen Partners (150+) & Integration Software & Services Cloud & Channel Hardware
  • MongoDB Solution
  • 19 MongoDB Business Benefits Increased Developer Productivity Better Customer Experience Faster Time to Market Lower TCO
  • 20 MongoDB Technical Benefits Horizontally Scalable -Sharding Agile & Flexible High Performance -Indexes -RAM Application Highly Available -Replica Sets { name: “Mark Smith”, date: “2013-08-01”), address: “10 3rd St.”, phone: [ { home: 1234567890}, { mobile: 1234568138} ] } db.cust.insert({…}) db.cust.find({ name:”Mark Smith”})
  • Best Fit for MongoDB
  • 22 • New Application DB - normal real-time/OLTP application database for new application • Migrating Existing DB - migrated from RDBMS where scale, agility, and/or cost is an issue • Data Hub Above Core DBs/Apps – Layer above multiple systems for real-time/request-response access • Data Hub For Single Operational View - Single data store from many disparate apps • Data PaaS - Unlimited scalable data services as PaaS Most Common Usage Patterns of MongoDB
  • 23 New Application Operational DB MongoDB Cluster Application … MongoDB Driver/API Datawarehouse Environment
  • 24 Migrating Operational DB Application Pain from performance, agility, or cost? => Best candidate for MongoDB MongoDB Driver/API Data Access Object/ Layer ODBC
  • 25 Application Server Fast Access Layer Above Core Apps Application 1 MongoDB Cluster Application 2 Services Layer Application N … … Mainframe Core Systems RDMS Core/legacy Systems ETL or Pub/sub REST/WS/API
  • 26 Single View Across Disparate Systems Source database 1 Source database 2 … Source Database N • ETL • File export • Custom app • Pub/sub Document • per record in source system Application 1 Application 2 Application M OLTP/real-time access Queue to Update Source Systems …
  • 27 Database PaaS Application 1 MongoDB Cloud Application 2 Application N … …
  • 28 Common FS Use Cases Capital Markets 1. Reference Data Management 2. Risk Analysis & Reporting 3. Private DBaaS 4. Buy-Side Portal 5. Regulatory Reporting 6. Trade Repository 7. Tick Data Capture & Analysis 8. Order Capture Banking 1. Single View of Customer 2. Online Banking 3. Reference Data Management 4. Risk Analysis & Reporting 5. Product Catalog 6. Cybersecurity Threat Analysis Insurance 1. Single View of the Customer 2. Online Quoting 3. Customer Portal 4. Risk Analysis & Reporting 5. Reference Data Distribution 6. Policy Definition Catalog
  • 29 Common FS Use Cases Capital Markets 1. Reference Data Management 2. Risk Analysis & Reporting 3. Private DBaaS 4. Buy-Side Portal 5. Regulatory Reporting 6. Trade Repository 7. Tick Data Capture & Analysis 8. Order Capture Banking 1. Single View of Customer 2. Online Banking 3. Reference Data Management 4. Risk Analysis & Reporting 5. Product Catalog 6. Cybersecurity Threat Analysis Insurance 1. Single View of the Customer 2. Online Quoting 3. Customer Portal 4. Risk Analysis & Reporting 5. Reference Data Distribution 6. Policy Definition Catalog
  • 30 Distribute reference data globally in real-time for fast local accessing and querying Data Hub - Fast Access Case Study: Global investment bank Problem Why MongoDB Results • Delays up to 36 hours in distributing data by batch • Charged multiple times globally for same data • Incurring regulatory penalties from missing SLAs • Had to manage 20 distributed systems with same data • Dynamic schema: easy to load initially & over time • Auto-replication: data distributed in real-time, read locally • Both cache and database: cache always up-to-date • Simple data modeling & analysis: easy changes and understanding • Will save about $40,000,000 in costs and penalties over 5 years • Only charged once for data • Data in sync globally and read locally • Capacity to move to one global shared data service
  • 31 Previous Reference Data Management Architecture Feeds & Batch data • Pricing • Accounts • Securities Master • Corporate actions Source Master Data (RDBMS) Batch Batch Batch Batch Batch Batch Batch Destination Data (RDBMS) Each represents • People $ • Hardware $ • License $ • Reg penalty $ • & other downstream problems
  • 32 Solution with MongoDB Feeds & Batch data • Pricing • Accounts • Securities Master • Corporate actions Real-time Real-time Real-time Real-time Real-time Real-time Real-time Each represents • No people $ • Less hardware $ • Less license $ • No penalty $ • & many less problems MongoDB Secondaries MongoDB Primary
  • 33 Global 360 degree view of customers’ policy portfolio and interactions Single View of Customer Case Study: Tier 1 Global Insurance Provider Problem Why MongoDB Results • 70 systems and 20 screens to view customer policies • Many CSR calls taken just to reroute customer • Poor customer experience • Source systems are hard to change • Dynamic schema: can combine 70 systems easily • Performance: can handle all data in one DB • Replication: local reads and high availability • Sharding: can add data easily by scaling out • Delivered in 3 months with $4M – previous attempts failed with $25M • Unified customer view available to all channels • Shorter and less calls re- routed • Increased customer satisfaction
  • 34 Single View of Customer Case Study: Tier 1 Global Insurance Provider Source database 1 Source database 2 … Source Database 70 Custom app exports JSON Document • per product • per customer CSR Application Customer Application Agent/RM Application OLTP/real-time access Future phases Queue to Update Source Systems
  • 35 More timely and accurate market risk analysis Migrating Application DB Case Study: Global FS Provider Problem Why MongoDB Results • Merger brought many more users onto system • Fed requiring longer time window • Need for versioning for data lineage & auditing • Could not scale existing RDBMS • Performance: can handle more users and more data all at once • Dynamic schema: can store disparate data and make changes easily • Replication: local reads and high availability • Sharding: can add data easily by scaling out • Risk analysis performed every 15 minutes instead of daily • Have full audit trail of state of the world at any time • Can make application changes much faster • Trading desks can hedge more effectively and use more capital
  • 36 Aggregating Risk Reporting Reporting Trades in real-time
  • 37 Online Banking/Trading Portal Use case requirements: • Store portfolios, accounts, positions/balances, orders, market values, etc. • Ad hoc querying by account, security, date, trader, thresholds, etc. • Fast response times and iteration keep customer satisfaction high • Relationship manager wants real-time reporting and alerting on customer activity Why MongoDB? • Low latency & caching => fast response times for all data available • Dynamic schema => Can handle any portfolio structure, assets, or accounts • High scalability => Reporting requirements on often large customer data sets • Aggregation Framework => calculate metrics, aggregations, and analysis
  • 38 70%+ Lower TCO + New Capabilities Commercial RDBMS Compute – Scale-Up Servers Storage – SAN Dev. and Admin Compute – Commodity HW Storage – Local Storage Dev. and Admin $1,680K $517K
  • 39 • FS today requires agility, productivity, and low TCO • RDBMS not supporting requirements well • MongoDB addresses all these requirements as a general purpose operational DB • MongoDB has hit critical mass in adoption • The best fit usage patterns are everywhere • Many case studies demonstrating value in FS • Let us know if we can help you get started Summary
  • 40 Training Online and In-Person for Developers and Administrators MongoDB Monitoring Service Free, Cloud-Based Service for Monitoring and Alerts MongoDB Backup Service Cloud-Based Service for Backing Up and Restoring MongoDB 10gen Products and Services Subscriptions MongoDB Enterprise, Monitoring, Support, Commercial License Consulting Expert Resources for All Phases of MongoDB Implementations
  • 41 Resource Location MongoDB Downloads 10gen.com/download Free Online Training education.10gen.com Webinars and Events 10gen.com/events White Papers 10gen.com/white-papers Case Studies 10gen.com/customers Presentations 10gen.com/presentations Documentation docs.mongodb.org Additional Info info@10gen.com For More Information Resource Location