SlideShare a Scribd company logo
Single customer view 
The drivers, challenges, lessons learned and a success story 
By Andy Ross
Agenda 
• About MongoDB 
– The Company 
– The Database (MongoDB) 
• Single View of Customer 
2
MongoDB 
NoSQL database 
3 
Document 
Data Model 
Open- 
Source 
General 
Purpose 
{ ! 
name: “John Smith”,! 
date: “2013-08-01”,! 
address: “10 3rd St.”,! 
phone: {! 
!home: 1234567890,! 
!mobile: 1234568138 }! 
}!
MongoDB Overview 
4 
400+ employees 900+ customers 
Over $231 million in funding 
(More than other NoSQL vendors combined) 
Offices in NY & Palo Alto and 
across EMEA, and APAC
Leading Organizations Rely on MongoDB 
5
Agenda 
• About MongoDB 
– The Company 
– The Database (MongoDB) 
• Single View of Customer 
6
MongoDB. 
NoSQL Document based database. 
Designed to build todays applications. 
• Fast to build. 
• Quick to adapt. 
• Easy to scale 
• Lessons learned from 40 years of RDBMS. 
7
Relational Model 
EmpID Name Dept Title Manage Payband 
9950 Dunham, 
PlanID BenFK Plan 
100 1 PPO Plus 
200 2 Standard 
8 
Justin 
500 1500 6531 C 
EmpBenPlanID EmpFK PlanFK 
1 9950 100 
2 9950 200 
BenID Benefit 
1 Health 
2 Dental 
DeptID Department 
500 Marketing 
TitleID Title 
1500 Product Manager
Document Model 
EmpID Name Dept Title Manage Payband Benefits 
9950 Dunham, 
9 
Justin 
Marketing Product 
Manager 
6531 C 
EmpBenPlanID EmpFK PlanFK 
1 9950 100 
2 9950 200 
Health PPO Plus 
Dental Standard 
PlanID BenFK Plan 
100 Health PPO Plus 
200 Dental Standard
Document Model 
EmpID Name Dept Title Manage Payband Benefits 
9950 Dunham, 
10 
Justin 
Marketing Product 
Manager 
6531 C Health PPO Plus 
Dental Standard 
{ 
_id : 9950, 
employee_name: "Dunham, Justin", 
department : "Marketing", 
title : "Product Manager, Web", 
report_up: "Neray, Graham", 
pay_band: “C", 
benefits : [ 
{ type : "Health", 
plan : "PPO Plus" }, 
{ type : "Dental", 
plan : "Standard" } 
] 
}
MongoDB - Agility 
Dynamic Schemas 
EmpID Name Dept Title Manager Payband Benefits 
9950 Dunham, 
Health PPO Plus 
Dental Standard 
EmpID Name Title Payband Bonus 
9952 Joe White CEO E 20,000 
EmpID Name Dept Title Manager Payband Shares 
9531 Nearey, 
11 
Justin 
Marketing Product 
Manager 
6531 C 
V 1.0 V 1.1 V 2.0 
Graham 
Marketing Director 9952 D 5000
MongoDB - Usability 
Drivers 
Drivers for most popular 
programming languages and 
frameworks 
Shell 
Command-line shell for 
interacting directly with 
database 
12 
Java 
Python 
> db.collection.insert({product:“MongoDB”, 
type:“Document Database”}) 
> 
> db.collection.findOne() 
{ 
“_id” : ObjectId(“5106c1c2fc629bfe52792e86”), 
“product” : “MongoDB” 
“type” : “Document Database” 
} 
Perl 
Ruby 
Haskell 
JavaScript
“No SQL”, But Fully Featured 
13 
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 : "Savings"! 
!} ]! 
}! 
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 bank 
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
MongoDB - Scalability 
• High Availability 
• Auto Sharding 
• Enterprise Monitoring 
• Grid file storage 
14
Common FS Use Cases 
15 
Capital Markets 
1. Reference Data 
Management 
2. Risk Analysis & 
Reporting 
3. Tick Data Capture 
& Analysis 
4. Customer Portal 
5. Portfolio and P&L 
Reporting 
6. Trade Repository 
7. 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
Agenda 
• About MongoDB 
– The Company 
– The Database (MongoDB) 
• Single View of Customer 
16
Single View of a Customer - 
Requirements 
• Extract customer info from many source systems 
as often as desired 
• Load into one database and application 
• Link data together for each customer 
• Query for all customer and associated product 
info at once 
• Enable CSRs, RMs/Agents, customers, etc. to 
know all customer and product information at 
once 
17
How most companies approach it 
ETL 
Transformation & Access 
Source Layer BI Abstraction & 
18 
Reporting Layer 
Acquisition Layer 
Extraction & 
Staging 
Cleansing 
Atomic Layer 
MDM 
Web Services 
Dashboards & 
Web Reports 
Ad-hoc reports & 
Analytics 
Corporate Data Warehouse 
Data 
within 
range 
Data Lineage and Metadata 
Layer 
Transformation & 
Calculation 
Performance & 
Access 
Change Data 
Data not 
null 
! 
Data in 
right 
format 
Reject Data 
Normalisation 
& Storage
How most companies approach it 
ETL 
Transformation & Access 
Source Layer BI Abstraction & 
19 
Reporting Layer 
Acquisition Layer 
Extraction & 
Staging 
Cleansing 
Atomic Layer 
MDM 
Web Services 
Dashboards & 
Web Reports 
Ad-hoc reports & 
Analytics 
Corporate Data Warehouse 
Data 
within 
range 
Data Lineage and Metadata 
Layer 
Transformation & 
Calculation 
Performance & 
Access 
Change Data 
Data not 
null 
! 
Data in 
right 
format 
Reject Data 
Normalisation 
& Storage 
New Business 
Requirement
How most companies approach it 
ETL 
Transformation & Access 
Source Layer BI Abstraction & 
20 
Reporting Layer 
Acquisition Layer 
Extraction & 
Staging 
Cleansing 
Atomic Layer 
MDM 
Web Services 
Dashboards & 
Web Reports 
Ad-hoc reports & 
Analytics 
Corporate Data Warehouse 
Data 
within 
range 
Data Lineage and Metadata 
Layer 
Transformation & 
Calculation 
Performance & 
Access 
Change Data 
Data not 
null 
! 
Data in 
right 
format 
Reject Data 
Normalisation 
& Storage 
New Business 
Requirement 
Understand 
schema 
Change & 
Modify 
Change & 
Modify 
Change & 
Modify
Architecture with MongoDB 
Source 
database 1 
Source 
database 2 
21 
… Source 
Database 40 
Any schema to 
JSON 
Document 
• per product 
• per customer 
App 1 
App 2 
App 3 
OLTP/real-time 
access
Single View of a Customer – 
Why MongoDB? 
• Dynamic schema => can handle vastly different data together and 
22 
can keep improving and fixing issues over time easily 
• High scale/performance => directly impacts customer experience or 
CSR MTTR so every second counts 
• Auto-sharding => can automatically add processing power as 
customers and products are added 
• Rich querying => supporting ends users directly requires multiple 
ways of access and key/value is not sufficient 
• Aggregation framework => database-supported roll-ups for analysis 
on data hub for customer information by marketing, sales, etc. 
• Map-reduce capability (Native MR or Hadoop Connector) => batch 
analysis looking for patterns and opportunities in data hub
And We’ve Done it Before 
• MetLife Leapfrogs Insurance Industry with 
MongoDB-Powered Big Data Application 
• New York—May 7, 2013—10gen, the MongoDB company, today 
23 
announced that MetLife, Inc. selected MongoDB as the data engine 
for “The Wall”, an innovative customer service application that went 
live last month. … 
• http://www.10gen.com/press/metlife-leapfrogs-insurance-industry-mongodb-powered- 
big-data-application
Case Study: Tier 1 Global Insurance 
Provider 
Source 
database 1 
Source 
database 2 
24 
… Source 
Database 40 
Custom app 
exports JSON 
Document 
• per product 
• per customer 
CSR Application 
Customer 
Application 
Agent/RM 
Application 
OLTP/real-time 
access 
Future phases
Customer Records in Source 
Systems, e.g. banking 
25 
Personal Bank Accounts 
• Account ID 
• Open date 
• First name 
• Last name 
• Joint First Name 
• Joint Last Name 
• Joint SSN 
• Address 
• City 
• State 
• Zip 
• Address 2 
• Home phone 
• Work phone 
• APR 
• Account type 
• Branch ID 
• Region ID 
• …. 
Credit Cards 
• CC number 
• SSN 1 
• Full name 1 
• Address 1 
• City 1 
• State 1 
• Zip 1 
• SSN 2 
• Full Name 2 
• Address 2 
• City 2 
• State 2 
• Zip 2 
• Primary phone 1 
• Mobile phone 2 
• Issue date 
• Reward type 
• …. 
Mortgages 
• Mortgage ID 
• Borrower name 
• Borrower SSN 
• Borrower address 
• Borrower city 
• Borrower state 
• Borrower zip 
• Co-borrower SSN 
• Co-borrower name 
• Co-borrower address 
• Co-borrower city 
• Co-borrower state 
• Co-borrower Zzp 
• Mobile phone 
• Effective date 
• Term 
• Interest 
• Money down 
• Principal loan 
• Total loan 
• ….
Joint Bank Account Document 
{ 
26 
_id : ObjectId("4e2e3f92268cdda473b628f6"), 
accountID: 9874983789, 
accountType: “Checking”, 
ownershipType: “Joint” 
accountOwners: [ 
{ firstName : ”John", 
lastName: “Smith”, 
address: “52 Vanderbilt Ave.”, …}, 
{ firstName : ”Anne", 
lastName: “Smith”, 
address: “52 Vanderbilt Ave.”, …} ] 
openDate: ISODate("2013-02-15 10:00”) 
… 
} 
> db.accts.find( { accountOwners: {$elemMatch: { 
lastName: “Smith”, 
address: “52 Vanderbilt Ave.”}}} )
General document per customer per 
account 
{ 
27 
_id : ObjectId("4e2e3f92268cdda473b628f6"), 
sourceIDs: { 
ABCSystemIDPart1: 8397897, 
ABCSystemIDPart2: 2937430, 
ABCSystemIDPart3: 932018 } 
accountType: “Checking”, 
accountOwners: [ 
{ firstName : ”John", 
lastName: “Smith”, 
contactMethods: [ 
{ type: “phone”, subtype: “mobile”, number: 8743927394}, 
{ type: “mail”, address: “58 3rd St.”, city: …} ] 
possibleMatchCriteria: { 
govtID: 2938932432, fullName: “johnsmith”, dob: … } }, 
{ firstName : ”Anne", 
maidenName: “Collins”, 
lastName: “Smith”, …} ], 
openDate: ISODate("2013-02-15 10:00”), 
accountFeatures { Overdraft: true, APR: 20, … } 
} 
OR creditCardNumber: 8392384938391293 
OR mortgageID: 2374389 
OR policyID: 18374923
Infusion. 
The Way Forward 
Infusion unites insight, creativity, and 
technology to accelerate and transform 
business for leading companies around 
the world.
A global presence 
Toronto New York Krakow 
Dallas Raleigh 
Houston 
Boston 
London 
Wroclaw 
Malta 
Singapore
40 
Our people
A history of success with the largest customers
2014 PARTNER OF THE YEAR 
Application Development 
Winner 
TOP 5 COOLEST INSURANCE APPS 
MetLife, Infinity 
Winner 
2014 PARTNER OF THE YEAR 
Winner 
2014 WINDOWS MOBILITY 
PARTNER OF THE YEAR 
Winner 
2014 BEST DIGITAL COMMUNITY 
National September 11 Memorial & 
Museum 
HonorableMention 
2014 Awards
Drivers and difficulties 
Single customer view is a goal that many large firms are striving to 
achieve, but it has its challenges
Why have a Single Customer View 
Source: Experian April 2012 
Enhance the customer 
experience 
Improve operational 
efficiency 
Increase cross-selling 
opportunities 
Improved marketing and 
product development 
Regulatory requirement
It is difficult 
Consequences 
of M&A 
Numerous 
systems and 
inconsistent 
formats 
Customer 
centric versus 
product centric 
Weary of large 
programmes 
The challenge: how does a big company act like a start-up?
How to be successful 
Lessons learned from the many single customer projects that we are 
involved with
How to be successful? 
Behave like a start-up 
Strong champion 
Constrain 
Modern technology Sell the idea
How to leapfrog? 
Have a vision Make it beautiful Make it real 
You can do things differently!
The MetLife story
One day we got an email from Gary… 
Business context 
New MetLife CIO with a (30/60/90) plan 
Get the company moving fast 
Introduce new technology 
Solve a problem that would improve the customer experience
Business challenge 
Asked if we could help build an application that 
would produce a 360° view of his new customers 
using mongoDB noSQL technology 
and have a Facebook style interface? 
70 
different systems 
140yrs 
customer data 
45million 
policies 
100million 
transactions 
One day we got an email from Gary…
2 
weeks 
90 
days 
The approach 
Prototype 
Delivered the application
The Wall unmasked
The benefits – improved customer experience
Further information 
MetLife press release: http://www.metlifegto.com/news/Built-in-record-time-- 
the-MetL 
Interview with John Bungert, Senior Architect at MetLife: 
http://www.mongodb.com/customers/metlife-interview 
Presentation by Jason Lombardo, AVP, Software Engineer, MetLife: 
http://www.mongodb.com/presentations/business-track-metlife-leapfrogs-insurance- 
industry-mongodb-powered-big-data 
Lots of interviews and articles. Just Search ‘MetLife The Wall’
Q&A
Single View of the Customer
Single View of the Customer

More Related Content

PDF
Knowledge Graph Embeddings for Recommender Systems
PDF
A Universe of Knowledge Graphs
PPTX
Oracle Management Cloud, OMC architecture
PDF
A Reference Architecture for ETL 2.0
PDF
Apache Iceberg: An Architectural Look Under the Covers
PPTX
Snowflake Datawarehouse Architecturing
PDF
How Will Knowledge Graphs Improve Clinical Reporting Workflows
PPTX
Graph Databases at Netflix
Knowledge Graph Embeddings for Recommender Systems
A Universe of Knowledge Graphs
Oracle Management Cloud, OMC architecture
A Reference Architecture for ETL 2.0
Apache Iceberg: An Architectural Look Under the Covers
Snowflake Datawarehouse Architecturing
How Will Knowledge Graphs Improve Clinical Reporting Workflows
Graph Databases at Netflix

What's hot (20)

PPTX
Snowflake Overview
PPTX
[DSC Europe 22] Overview of the Databricks Platform - Petar Zecevic
PPTX
Build a car with Graphs, Fabien Batejat, Volvo Cars
PDF
Data Quality in the Banking Industry: Turning Regulatory Compliance into Busi...
PDF
Using an employee knowledge graph for employee engagement and career mobility
PDF
A Primer on Entity Resolution
PDF
Modeling data and best practices for the Azure Cosmos DB.
PDF
Denodo Data Virtualization Platform: Overview (session 1 from Architect to Ar...
PDF
Modern Data Challenges require Modern Graph Technology
PDF
Trino: A Ludicrously Fast Query Engine - Pulsar Summit NA 2021
PPTX
Advantages of metadata
PPTX
Real-time Analytics with Trino and Apache Pinot
PDF
PDF
Big Data Storage Challenges and Solutions
PDF
Getting Started with Knowledge Graphs
PDF
Journey to Creating a 360 View of the Customer: Implementing Big Data Strateg...
PDF
Pourquoi Leroy Merlin a besoin d'un Knowledge Graph ?
PDF
Simplify CDC Pipeline with Spark Streaming SQL and Delta Lake
PPTX
How Expedia’s Entity Graph Powers Global Travel
PDF
Vault 101
Snowflake Overview
[DSC Europe 22] Overview of the Databricks Platform - Petar Zecevic
Build a car with Graphs, Fabien Batejat, Volvo Cars
Data Quality in the Banking Industry: Turning Regulatory Compliance into Busi...
Using an employee knowledge graph for employee engagement and career mobility
A Primer on Entity Resolution
Modeling data and best practices for the Azure Cosmos DB.
Denodo Data Virtualization Platform: Overview (session 1 from Architect to Ar...
Modern Data Challenges require Modern Graph Technology
Trino: A Ludicrously Fast Query Engine - Pulsar Summit NA 2021
Advantages of metadata
Real-time Analytics with Trino and Apache Pinot
Big Data Storage Challenges and Solutions
Getting Started with Knowledge Graphs
Journey to Creating a 360 View of the Customer: Implementing Big Data Strateg...
Pourquoi Leroy Merlin a besoin d'un Knowledge Graph ?
Simplify CDC Pipeline with Spark Streaming SQL and Delta Lake
How Expedia’s Entity Graph Powers Global Travel
Vault 101

Similar to Single View of the Customer (20)

PPT
Webinar: Making A Single View of the Customer Real with MongoDB
PPT
How Retail Banks Use MongoDB
PPTX
Webinar: How Financial Firms Create a Single Customer View with MongoDB
PDF
How Financial Services Organizations Use MongoDB
PPTX
Webinar: How Financial Services Organizations Use MongoDB
PPTX
How Insurance Companies Use MongoDB
PPTX
Webinar: How to Drive Business Value in Financial Services with MongoDB
PPTX
Enterprise architectsview 2015-apr
PPTX
Enterprise Reporting with MongoDB and JasperSoft
PPTX
Webinar: How to Drive Business Value in Financial Services with MongoDB
PPTX
L’architettura di Classe Enterprise di Nuova Generazione
PPTX
L’architettura di classe enterprise di nuova generazione
PPTX
MongoDB in a Mainframe World
PPTX
An Enterprise Architect's View of MongoDB
PPTX
MongoDB + Spring
PPTX
MongoDB and Spring - Two leaves of a same tree
PDF
MongoDB .local Toronto 2019: MongoDB – Powering the new age data demands
PDF
10-Step Methodology to Building a Single View with MongoDB
PDF
MongoDB Breakfast Milan - Mainframe Offloading Strategies
PDF
MongoDB .local Chicago 2019: MongoDB – Powering the new age data demands
Webinar: Making A Single View of the Customer Real with MongoDB
How Retail Banks Use MongoDB
Webinar: How Financial Firms Create a Single Customer View with MongoDB
How Financial Services Organizations Use MongoDB
Webinar: How Financial Services Organizations Use MongoDB
How Insurance Companies Use MongoDB
Webinar: How to Drive Business Value in Financial Services with MongoDB
Enterprise architectsview 2015-apr
Enterprise Reporting with MongoDB and JasperSoft
Webinar: How to Drive Business Value in Financial Services with MongoDB
L’architettura di Classe Enterprise di Nuova Generazione
L’architettura di classe enterprise di nuova generazione
MongoDB in a Mainframe World
An Enterprise Architect's View of MongoDB
MongoDB + Spring
MongoDB and Spring - Two leaves of a same tree
MongoDB .local Toronto 2019: MongoDB – Powering the new age data demands
10-Step Methodology to Building a Single View with MongoDB
MongoDB Breakfast Milan - Mainframe Offloading Strategies
MongoDB .local Chicago 2019: MongoDB – Powering the new age data demands

More from MongoDB (20)

PDF
MongoDB SoCal 2020: Migrate Anything* to MongoDB Atlas
PDF
MongoDB SoCal 2020: Go on a Data Safari with MongoDB Charts!
PDF
MongoDB SoCal 2020: Using MongoDB Services in Kubernetes: Any Platform, Devel...
PDF
MongoDB SoCal 2020: A Complete Methodology of Data Modeling for MongoDB
PDF
MongoDB SoCal 2020: From Pharmacist to Analyst: Leveraging MongoDB for Real-T...
PDF
MongoDB SoCal 2020: Best Practices for Working with IoT and Time-series Data
PDF
MongoDB SoCal 2020: MongoDB Atlas Jump Start
PDF
MongoDB .local San Francisco 2020: Powering the new age data demands [Infosys]
PDF
MongoDB .local San Francisco 2020: Using Client Side Encryption in MongoDB 4.2
PDF
MongoDB .local San Francisco 2020: Using MongoDB Services in Kubernetes: any ...
PDF
MongoDB .local San Francisco 2020: Go on a Data Safari with MongoDB Charts!
PDF
MongoDB .local San Francisco 2020: From SQL to NoSQL -- Changing Your Mindset
PDF
MongoDB .local San Francisco 2020: MongoDB Atlas Jumpstart
PDF
MongoDB .local San Francisco 2020: Tips and Tricks++ for Querying and Indexin...
PDF
MongoDB .local San Francisco 2020: Aggregation Pipeline Power++
PDF
MongoDB .local San Francisco 2020: A Complete Methodology of Data Modeling fo...
PDF
MongoDB .local San Francisco 2020: MongoDB Atlas Data Lake Technical Deep Dive
PDF
MongoDB .local San Francisco 2020: Developing Alexa Skills with MongoDB & Golang
PDF
MongoDB .local Paris 2020: Realm : l'ingrédient secret pour de meilleures app...
PDF
MongoDB .local Paris 2020: Upply @MongoDB : Upply : Quand le Machine Learning...
MongoDB SoCal 2020: Migrate Anything* to MongoDB Atlas
MongoDB SoCal 2020: Go on a Data Safari with MongoDB Charts!
MongoDB SoCal 2020: Using MongoDB Services in Kubernetes: Any Platform, Devel...
MongoDB SoCal 2020: A Complete Methodology of Data Modeling for MongoDB
MongoDB SoCal 2020: From Pharmacist to Analyst: Leveraging MongoDB for Real-T...
MongoDB SoCal 2020: Best Practices for Working with IoT and Time-series Data
MongoDB SoCal 2020: MongoDB Atlas Jump Start
MongoDB .local San Francisco 2020: Powering the new age data demands [Infosys]
MongoDB .local San Francisco 2020: Using Client Side Encryption in MongoDB 4.2
MongoDB .local San Francisco 2020: Using MongoDB Services in Kubernetes: any ...
MongoDB .local San Francisco 2020: Go on a Data Safari with MongoDB Charts!
MongoDB .local San Francisco 2020: From SQL to NoSQL -- Changing Your Mindset
MongoDB .local San Francisco 2020: MongoDB Atlas Jumpstart
MongoDB .local San Francisco 2020: Tips and Tricks++ for Querying and Indexin...
MongoDB .local San Francisco 2020: Aggregation Pipeline Power++
MongoDB .local San Francisco 2020: A Complete Methodology of Data Modeling fo...
MongoDB .local San Francisco 2020: MongoDB Atlas Data Lake Technical Deep Dive
MongoDB .local San Francisco 2020: Developing Alexa Skills with MongoDB & Golang
MongoDB .local Paris 2020: Realm : l'ingrédient secret pour de meilleures app...
MongoDB .local Paris 2020: Upply @MongoDB : Upply : Quand le Machine Learning...

Recently uploaded (20)

PPT
Teaching material agriculture food technology
PPTX
Tartificialntelligence_presentation.pptx
PDF
Advanced methodologies resolving dimensionality complications for autism neur...
PDF
A comparative study of natural language inference in Swahili using monolingua...
PDF
Encapsulation_ Review paper, used for researhc scholars
PDF
Build a system with the filesystem maintained by OSTree @ COSCUP 2025
PDF
Approach and Philosophy of On baking technology
PDF
MIND Revenue Release Quarter 2 2025 Press Release
PDF
Encapsulation theory and applications.pdf
PDF
August Patch Tuesday
PDF
Getting Started with Data Integration: FME Form 101
PDF
7 ChatGPT Prompts to Help You Define Your Ideal Customer Profile.pdf
PPTX
1. Introduction to Computer Programming.pptx
PDF
Accuracy of neural networks in brain wave diagnosis of schizophrenia
PPTX
Group 1 Presentation -Planning and Decision Making .pptx
PDF
Building Integrated photovoltaic BIPV_UPV.pdf
PPTX
Digital-Transformation-Roadmap-for-Companies.pptx
PDF
Machine learning based COVID-19 study performance prediction
PDF
Per capita expenditure prediction using model stacking based on satellite ima...
PDF
Unlocking AI with Model Context Protocol (MCP)
Teaching material agriculture food technology
Tartificialntelligence_presentation.pptx
Advanced methodologies resolving dimensionality complications for autism neur...
A comparative study of natural language inference in Swahili using monolingua...
Encapsulation_ Review paper, used for researhc scholars
Build a system with the filesystem maintained by OSTree @ COSCUP 2025
Approach and Philosophy of On baking technology
MIND Revenue Release Quarter 2 2025 Press Release
Encapsulation theory and applications.pdf
August Patch Tuesday
Getting Started with Data Integration: FME Form 101
7 ChatGPT Prompts to Help You Define Your Ideal Customer Profile.pdf
1. Introduction to Computer Programming.pptx
Accuracy of neural networks in brain wave diagnosis of schizophrenia
Group 1 Presentation -Planning and Decision Making .pptx
Building Integrated photovoltaic BIPV_UPV.pdf
Digital-Transformation-Roadmap-for-Companies.pptx
Machine learning based COVID-19 study performance prediction
Per capita expenditure prediction using model stacking based on satellite ima...
Unlocking AI with Model Context Protocol (MCP)

Single View of the Customer

  • 1. Single customer view The drivers, challenges, lessons learned and a success story By Andy Ross
  • 2. Agenda • About MongoDB – The Company – The Database (MongoDB) • Single View of Customer 2
  • 3. MongoDB NoSQL database 3 Document Data Model Open- Source General Purpose { ! name: “John Smith”,! date: “2013-08-01”,! address: “10 3rd St.”,! phone: {! !home: 1234567890,! !mobile: 1234568138 }! }!
  • 4. MongoDB Overview 4 400+ employees 900+ customers Over $231 million in funding (More than other NoSQL vendors combined) Offices in NY & Palo Alto and across EMEA, and APAC
  • 6. Agenda • About MongoDB – The Company – The Database (MongoDB) • Single View of Customer 6
  • 7. MongoDB. NoSQL Document based database. Designed to build todays applications. • Fast to build. • Quick to adapt. • Easy to scale • Lessons learned from 40 years of RDBMS. 7
  • 8. Relational Model EmpID Name Dept Title Manage Payband 9950 Dunham, PlanID BenFK Plan 100 1 PPO Plus 200 2 Standard 8 Justin 500 1500 6531 C EmpBenPlanID EmpFK PlanFK 1 9950 100 2 9950 200 BenID Benefit 1 Health 2 Dental DeptID Department 500 Marketing TitleID Title 1500 Product Manager
  • 9. Document Model EmpID Name Dept Title Manage Payband Benefits 9950 Dunham, 9 Justin Marketing Product Manager 6531 C EmpBenPlanID EmpFK PlanFK 1 9950 100 2 9950 200 Health PPO Plus Dental Standard PlanID BenFK Plan 100 Health PPO Plus 200 Dental Standard
  • 10. Document Model EmpID Name Dept Title Manage Payband Benefits 9950 Dunham, 10 Justin Marketing Product Manager 6531 C Health PPO Plus Dental Standard { _id : 9950, employee_name: "Dunham, Justin", department : "Marketing", title : "Product Manager, Web", report_up: "Neray, Graham", pay_band: “C", benefits : [ { type : "Health", plan : "PPO Plus" }, { type : "Dental", plan : "Standard" } ] }
  • 11. MongoDB - Agility Dynamic Schemas EmpID Name Dept Title Manager Payband Benefits 9950 Dunham, Health PPO Plus Dental Standard EmpID Name Title Payband Bonus 9952 Joe White CEO E 20,000 EmpID Name Dept Title Manager Payband Shares 9531 Nearey, 11 Justin Marketing Product Manager 6531 C V 1.0 V 1.1 V 2.0 Graham Marketing Director 9952 D 5000
  • 12. MongoDB - Usability Drivers Drivers for most popular programming languages and frameworks Shell Command-line shell for interacting directly with database 12 Java Python > db.collection.insert({product:“MongoDB”, type:“Document Database”}) > > db.collection.findOne() { “_id” : ObjectId(“5106c1c2fc629bfe52792e86”), “product” : “MongoDB” “type” : “Document Database” } Perl Ruby Haskell JavaScript
  • 13. “No SQL”, But Fully Featured 13 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 : "Savings"! !} ]! }! 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 bank 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. MongoDB - Scalability • High Availability • Auto Sharding • Enterprise Monitoring • Grid file storage 14
  • 15. Common FS Use Cases 15 Capital Markets 1. Reference Data Management 2. Risk Analysis & Reporting 3. Tick Data Capture & Analysis 4. Customer Portal 5. Portfolio and P&L Reporting 6. Trade Repository 7. 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
  • 16. Agenda • About MongoDB – The Company – The Database (MongoDB) • Single View of Customer 16
  • 17. Single View of a Customer - Requirements • Extract customer info from many source systems as often as desired • Load into one database and application • Link data together for each customer • Query for all customer and associated product info at once • Enable CSRs, RMs/Agents, customers, etc. to know all customer and product information at once 17
  • 18. How most companies approach it ETL Transformation & Access Source Layer BI Abstraction & 18 Reporting Layer Acquisition Layer Extraction & Staging Cleansing Atomic Layer MDM Web Services Dashboards & Web Reports Ad-hoc reports & Analytics Corporate Data Warehouse Data within range Data Lineage and Metadata Layer Transformation & Calculation Performance & Access Change Data Data not null ! Data in right format Reject Data Normalisation & Storage
  • 19. How most companies approach it ETL Transformation & Access Source Layer BI Abstraction & 19 Reporting Layer Acquisition Layer Extraction & Staging Cleansing Atomic Layer MDM Web Services Dashboards & Web Reports Ad-hoc reports & Analytics Corporate Data Warehouse Data within range Data Lineage and Metadata Layer Transformation & Calculation Performance & Access Change Data Data not null ! Data in right format Reject Data Normalisation & Storage New Business Requirement
  • 20. How most companies approach it ETL Transformation & Access Source Layer BI Abstraction & 20 Reporting Layer Acquisition Layer Extraction & Staging Cleansing Atomic Layer MDM Web Services Dashboards & Web Reports Ad-hoc reports & Analytics Corporate Data Warehouse Data within range Data Lineage and Metadata Layer Transformation & Calculation Performance & Access Change Data Data not null ! Data in right format Reject Data Normalisation & Storage New Business Requirement Understand schema Change & Modify Change & Modify Change & Modify
  • 21. Architecture with MongoDB Source database 1 Source database 2 21 … Source Database 40 Any schema to JSON Document • per product • per customer App 1 App 2 App 3 OLTP/real-time access
  • 22. Single View of a Customer – Why MongoDB? • Dynamic schema => can handle vastly different data together and 22 can keep improving and fixing issues over time easily • High scale/performance => directly impacts customer experience or CSR MTTR so every second counts • Auto-sharding => can automatically add processing power as customers and products are added • Rich querying => supporting ends users directly requires multiple ways of access and key/value is not sufficient • Aggregation framework => database-supported roll-ups for analysis on data hub for customer information by marketing, sales, etc. • Map-reduce capability (Native MR or Hadoop Connector) => batch analysis looking for patterns and opportunities in data hub
  • 23. And We’ve Done it Before • MetLife Leapfrogs Insurance Industry with MongoDB-Powered Big Data Application • New York—May 7, 2013—10gen, the MongoDB company, today 23 announced that MetLife, Inc. selected MongoDB as the data engine for “The Wall”, an innovative customer service application that went live last month. … • http://www.10gen.com/press/metlife-leapfrogs-insurance-industry-mongodb-powered- big-data-application
  • 24. Case Study: Tier 1 Global Insurance Provider Source database 1 Source database 2 24 … Source Database 40 Custom app exports JSON Document • per product • per customer CSR Application Customer Application Agent/RM Application OLTP/real-time access Future phases
  • 25. Customer Records in Source Systems, e.g. banking 25 Personal Bank Accounts • Account ID • Open date • First name • Last name • Joint First Name • Joint Last Name • Joint SSN • Address • City • State • Zip • Address 2 • Home phone • Work phone • APR • Account type • Branch ID • Region ID • …. Credit Cards • CC number • SSN 1 • Full name 1 • Address 1 • City 1 • State 1 • Zip 1 • SSN 2 • Full Name 2 • Address 2 • City 2 • State 2 • Zip 2 • Primary phone 1 • Mobile phone 2 • Issue date • Reward type • …. Mortgages • Mortgage ID • Borrower name • Borrower SSN • Borrower address • Borrower city • Borrower state • Borrower zip • Co-borrower SSN • Co-borrower name • Co-borrower address • Co-borrower city • Co-borrower state • Co-borrower Zzp • Mobile phone • Effective date • Term • Interest • Money down • Principal loan • Total loan • ….
  • 26. Joint Bank Account Document { 26 _id : ObjectId("4e2e3f92268cdda473b628f6"), accountID: 9874983789, accountType: “Checking”, ownershipType: “Joint” accountOwners: [ { firstName : ”John", lastName: “Smith”, address: “52 Vanderbilt Ave.”, …}, { firstName : ”Anne", lastName: “Smith”, address: “52 Vanderbilt Ave.”, …} ] openDate: ISODate("2013-02-15 10:00”) … } > db.accts.find( { accountOwners: {$elemMatch: { lastName: “Smith”, address: “52 Vanderbilt Ave.”}}} )
  • 27. General document per customer per account { 27 _id : ObjectId("4e2e3f92268cdda473b628f6"), sourceIDs: { ABCSystemIDPart1: 8397897, ABCSystemIDPart2: 2937430, ABCSystemIDPart3: 932018 } accountType: “Checking”, accountOwners: [ { firstName : ”John", lastName: “Smith”, contactMethods: [ { type: “phone”, subtype: “mobile”, number: 8743927394}, { type: “mail”, address: “58 3rd St.”, city: …} ] possibleMatchCriteria: { govtID: 2938932432, fullName: “johnsmith”, dob: … } }, { firstName : ”Anne", maidenName: “Collins”, lastName: “Smith”, …} ], openDate: ISODate("2013-02-15 10:00”), accountFeatures { Overdraft: true, APR: 20, … } } OR creditCardNumber: 8392384938391293 OR mortgageID: 2374389 OR policyID: 18374923
  • 28. Infusion. The Way Forward Infusion unites insight, creativity, and technology to accelerate and transform business for leading companies around the world.
  • 29. A global presence Toronto New York Krakow Dallas Raleigh Houston Boston London Wroclaw Malta Singapore
  • 31. A history of success with the largest customers
  • 32. 2014 PARTNER OF THE YEAR Application Development Winner TOP 5 COOLEST INSURANCE APPS MetLife, Infinity Winner 2014 PARTNER OF THE YEAR Winner 2014 WINDOWS MOBILITY PARTNER OF THE YEAR Winner 2014 BEST DIGITAL COMMUNITY National September 11 Memorial & Museum HonorableMention 2014 Awards
  • 33. Drivers and difficulties Single customer view is a goal that many large firms are striving to achieve, but it has its challenges
  • 34. Why have a Single Customer View Source: Experian April 2012 Enhance the customer experience Improve operational efficiency Increase cross-selling opportunities Improved marketing and product development Regulatory requirement
  • 35. It is difficult Consequences of M&A Numerous systems and inconsistent formats Customer centric versus product centric Weary of large programmes The challenge: how does a big company act like a start-up?
  • 36. How to be successful Lessons learned from the many single customer projects that we are involved with
  • 37. How to be successful? Behave like a start-up Strong champion Constrain Modern technology Sell the idea
  • 38. How to leapfrog? Have a vision Make it beautiful Make it real You can do things differently!
  • 40. One day we got an email from Gary… Business context New MetLife CIO with a (30/60/90) plan Get the company moving fast Introduce new technology Solve a problem that would improve the customer experience
  • 41. Business challenge Asked if we could help build an application that would produce a 360° view of his new customers using mongoDB noSQL technology and have a Facebook style interface? 70 different systems 140yrs customer data 45million policies 100million transactions One day we got an email from Gary…
  • 42. 2 weeks 90 days The approach Prototype Delivered the application
  • 44. The benefits – improved customer experience
  • 45. Further information MetLife press release: http://www.metlifegto.com/news/Built-in-record-time-- the-MetL Interview with John Bungert, Senior Architect at MetLife: http://www.mongodb.com/customers/metlife-interview Presentation by Jason Lombardo, AVP, Software Engineer, MetLife: http://www.mongodb.com/presentations/business-track-metlife-leapfrogs-insurance- industry-mongodb-powered-big-data Lots of interviews and articles. Just Search ‘MetLife The Wall’
  • 46. Q&A