how to deliver a single view in financial services

How to deliver a Single View in Financial ServicesNoel Ady – Technology Strategist

Jim Duffy – Enterprise Architect22 March 2016

Agenda• Challenges

• Benefits of Single View

• Why MongoDB

• How to Single View

• Customer Success

• Start Today

Poor Data Quality

Many versions of the truth

Data consistency between providers

Data in various forms and structure

No inventory of what ‘complete’ is

Challenges

Department Siloes

Access to data is tightly controlled

Governance issues on data ownership

Unclear definition of what ‘single view’ is

Challenges

Pulling it all together

Data lineage between systems

Multiple Developer & Operational Teams

Various performance capabilities across systems

Challenges

• Tried with relational databases• $25M Investment without

progress

• Customers limited to historical data• Rigid relational schema prevented

change

Examples of not addressing these challenges

Finance

Single View

Customer ServiceMarketing

Sales

Customer Engagement

Service more customers (One stop to get full details of the customer) meaning you can

serve more customers in less time

Build customer confidence through having whole

information to hand with no long ‘on hold’ experience

Surface information to the customer / partners via

online portals and account applications.

Linking customers is a complex problem a large amount of linking can be

automated but there is always an element of manual confirmation and validation which sits naturally inside a

Single view solution/ application

A better understanding of the customer to help retain

and cross sell.

Reduced training costs for new staff . A single intuitive interface often requires little

or no training for new staff. Compared to multiple system training sessions for

backend end operational and financial systems.

Training

Reduce mistakes / missed conversations or important information due to a simple and easy to use system with a single approach

to reading and updating information

Support A Digital Strategy

Its not only customers that are becoming more demanding when it comes to

technology interaction. The millennials will be working in your organizations soon and

they have greater expectations .

Collaboration of team members and customers is becoming more important as business moves to digital. Customers expect to be in touch and staff

need to be able to share and work together , a single view solution provides an excellent platform for

these activities

Insights and Analytics

Depending on how you implement your single view, having data

gathered together is an excellent place to start reporting and

processing your information to gain further insights

Run complex algorithms to match and merge customer / entity

information

Having a broader view of an individual allows us to build more

robust and accurate recommendation engines.

Expressive Query Language& Secondary Indexes

Strong Consistency

Enterprise Management& Integrations

Relational

Scalability& Performance

Always On,Global Deployments

FlexibilityExpressive Query Language& Secondary Indexes

Strong Consistency


NoSQL

Nexus Architecture

Scalability& Performance

Always On,Global Deployments

FlexibilityExpressive Query Language& Secondary Indexes

Strong Consistency


In It’s Simplest Form

Logically this is a Consolidated source of data relating to one or more entities (often a

customer) across various systems , regions and

departments

Customer, Policy , Product …(Often a service layer)

Often complimented by an intuitive UI to allow users to search view and update

Often data source is multiple sources in multiple regions from multiple systems

Analytics and processing. Build intelligent models based on consolidated data and external data sources

Responsibilities for each component• Search for entities on a wide range of search

criteria• View full details of an entity• Execute actions to manipulate information

UI

SL• Query Interface• Get Interface• Trusted Security

LoB

• Application workflow between entities• User notification• Manual matching and match validation• Reporting

• Business Logic• Data provision• Local security

• Match / Merge• Aggregation• Insights

• Updates (Routing)• Authorization• Caching/ Data Store AN

Single View (Breadth and Depth)

Basic Customer information (Name + Address)System D

Invoicing

Web Analytics

Risk Evaluation

System E

System G

System H

System F

System CSystem BSystem A

Depth(Details about an entity)

Breadth(Ability surface a wide range of entities)

Consolidating Data (Options)On Request Batch Data Sync Real-Time Data

Sync

Single View Single View

Batch

Single View

Sink

Pros: Real-time data view, SimpleCons: Temporal couplingCanonical Models (lossy)Performance Impact

Pros: Less (manageable) Performance ImpactResponsive to end userNot lossy (when using mongo) documentsCons: Data LatencyAdditional complexity

Pros: Near real-time view of large and fast moving dataCons: ComplexReal-time ‘fill’ not always 100 % (although variance is slim)

A combination

Lambda. Real-Time + Batch

Single View

Mongo Store

Speed

Pros:Cons:

Batch

cache

Batch Layer Pre-Computes Views in

Store

Speed layer consumes real-time data (often using a distributed cache)

and aggregates

Service layer combines batch and

speed data on request

Service

Actor Cluster Fabric

High Throughput, Low Latency and Concurrency Requirements?

Service

Customer 145

Customer 776

Customer 998

Account 6665

Account 556

Event Sink

Mongo Store

Independently addressable

Ability to communicate

from actor to actor

Why Mongo ?

{id:’A88998’,name:’Mr John Doe’,address:’56 High Street, Lincoln, UK’accountBalance:’1768.98’,Currency:’GBP’}

{id:’A88998’,firstName:’Harry’,lastName:’’Smith’,addressLine1:’77 Baker Street’postcode:’L88 7TH’account:{balance:99.00, accountNo:’7789’}}

Store data as close to original data as possible. Keeping as much as you can about the data. Mongo’s dynamic schema allows us to build additive data models which may grow over time as systems collect more information.

Think immutable. Store new versions of the data as it changes and timestamp it. This helps in many areas:• Analytics and insights• Entity id matching • Point in time data • AuditabilityMongo can cope with this amount of data.

{customerId: 889998, address:’55 Smith Street’}

{customerId: 889998, address:878 High Street’}

{customerId: 889998, address:’77 Church Street’}

{customerId: 889998, address:’34 Langdon Lane’}

{customerId: 889998, address:’556 Highfield Road’}

12-09-199003-08-199806-07-200307-09-200808-09-2011

Search Data Entity Data

Pre Compute

Single View Service

Search Retrieve

Generate separate collections for the purpose of search , pre aggregate values , simplify and flatten data structures in a way that makes search easy and efficient and as a pointer to the (latest) entity data for full retrieval. Mongo allows us to do this work on the fly. Building new logic based on existing data using the aggregate framework.

Why Mongo - Automated MatchingTurning multiple versions of an entity to one accurate reflection of them all

Jon SmithNULL88 Headington CloseNULL

Jon Smith16th Jan 197789 Main [email protected]

Problem • Complex (requires fuzzy logic, training sets,

soundex , block linking techniques)• Time consuming

Solution• Utilizing richer information through immutability • Ability to process at scale with the spark adapter

Having the capability to link customers with accuracy is key to the success of the platform, especially as the application landscape grows.

John SmithNULL88 Headington [email protected]

Jon Smith15th Jan 197788 Headington [email protected]# JC8 89 767 55

mailto:[email protected]

Why Mongo …?

BI Connector/ODBC

Mongo Community

Manual Matching-------------------------------------------------------------------------

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• Highlight potential links when viewing an entity

• Search Results to show potential links and probabilities

• Pre written scripts to help the user interact with a customer to link with confidence without exposing private information

• Avoid the “Potential Match List”

Writing Back

SerachData EntityData

Pre Compute

Single View Service

Search Retrieve

System A System B System A

Ingestion Write Back Channel

Commands

Execute Update Commands(Avoid crud)

Eventual Consistency

Single View Approach Build a breadth view across two / three key systems as a pilot / PoC. Ideally building a search view which reflects the users most common search criteria.

Increase breadth and begin building out the depth for more detailed information about individual entities.

Introduce manual matching within application.

Introduce a write mechanism . As discussed this often means implement an asynchronous pattern based on

Win “Buy-in” :Clickable prototype, presentations andmarketing videos to help sell the vision and build support cross your business units. Often the business is very excited to have a view of how a cross system application will work.

on eventual consistence model

Build out your analytics , matching capabilities and

Deeper insights into your data.

Customer Success

Single View of CustomerInsurance leader generates coveted single view of customers in 90 days – “The Wall”

Problem Why MongoDB ResultsProblem Solution Results

No single view of customer, leading to poor customer experience and churn

145 years of policy data, 70+ systems, 24 800 numbers, 15+ front-end apps that are not integrated

Spent 2 years, $25M trying build single view with Oracle – failed

Built “The Wall,” pulling in disparate data and serving single view to customer service reps in real time

Flexible data model to aggregate disparate data into single data store

Expressive query language and secondary indexes to serve any field in real time

Prototyped in 2 weeks

Deployed to production in 90 days

Decreased churn and improved ability to upsell/cross-sell

Mobile HR AppOne of largest HCP solution providers builds app for single view of HR, serving 1M+ users globally


One of top HCM solution providers in the world; serves 1 in 6 paychecks in the US

Wanted to give customers access to critical info on smartphones to live up to modern consumer expectations – mobile app for single view of HR (benefits, payroll, etc.)

Needed to pull in customer data from multiple legacy source systems built on Oracle

Built ADP Mobile Solutions on MongoDB, leveraging flexible data model to pull in disparate HR data

High performance, mobile-friendly user experience via auto-sharding

Native, cross-DC replication and automated failover for high availability

Able to serve 1M users and 41K companies across 17 countries

99.999% uptime

Top iOS Business App in iTunes App Store


Rigid relational data model inhibits agility of application development

Inability to scale as new price comparison services were launched

Systems down for 30 minutes during failures or maintenance

Flexible data model allows company to quickly build, launch and evolve new applications

Scale-out across hybrid on-prem & cloud platform, with tunable consistency to optimize performance

Self-healing replica sets and multi-data center aware architecture for service availability

2x faster time to market after migrating from Microsoft SQL Server to MongoDB.

Enabled continuous delivery: now pushing new features every day

Greatly improved customers service by eliminating 30 minute service downtime events

UK’s Leading Price Comparison SiteOut-pacing Internet search giants with continuous delivery pipeline powered by microservices & Docker containers running MongoDB and Hadoop in the cloud


Proprietary solution with rigid data model slowed rate of new service introductions, impacting competitiveness

Unable to scale as subscriber and service portfolio expanded

High TCO incurred from proprietary hardware and software

Built new customer data management platform on MongoDB

Flexible data model enables dynamic schema modification to support new service introductions

Automatic sharding to scale database as the business grows

MongoDB platform scales to serve 12M customers, with 50% reduced cost per subscriber

Streamlined and simplified systems allowing faster innovation and higher agility

Migration to MongoDB completed in just 6 months

Customer Data Mgt.Telco leader unifies customer experience, driving 50% lower cost and reduced churn

PersonalizationBuilt personalization engine in 25% the time with 50% the team


Needed personalization server that acts as the master storage for customer data. Originally built on Oracle (over 14 months) but it performed below expectations, did not scale, and cost too much

New requirements made Oracle unusable – 40% more data, must reload entire data warehouse (22M customers) daily in small window – could not be met with Oracle

Implemented on MongoDB, using flexible data model to easily bring in data from disparate customer data source systems

Expressive query language made it possible to access customer records using any field

Consulting and support significantly reduced upfront development and deployment costs

New version of personalization engine was built on MongoDB in 25% the time with 50% the team

Led to performance boosts of more than a magnitude

Storage requirements decreased by 66%, lowering infrastructure costs


AXA Banque needed to differentiate themselves in the the retail banking space by offering a 100% mobile experience

Reaching the “digital native” demographic required new tools and approaches

The solution must be able to absorb large peak loads and true 24/7/365 availability

“We were quickly convinced that MongoDB's open-source NoSQL model was the best choice” Pascal Lozovoy CIO AXA Banque

Mobile application is capable of absorbing large amounts of unstructured data with ease

Seamless integration with existing banking platform on AS400 mainframe is achieved using Java and web services

SooN successful launch Jan 2014 now has thousands of users

New functionality is quickly developed and released to the public

Zero interruption of service despite not limiting what users can upload

Mobile Banking PlatformAXA Banque uses MongoDB to power “SooN” their innovative mobile banking platform

MongoDB Use Cases

Single View Internet of Things Mobile Real-Time Analytics

Catalog Personalization Content Management

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