Big Data for Bank
Content
Why care
© 2013 IBM Corporation
Intrinsic Property of Data … it grows
90%
of the world’s data was created in the last two years
80%
of the world’s data today is unstructured
20%
of available data can be processed by traditional systems
1 in 2
business leaders don’t have access to data they need
83%
of CIO’s cited BI and analytics as part of their visionary plan
5.4X
more likely that top performers use business analytics
© 2013 IBM Corporation
2 Source: GigaOM, Software Group, IBM Institute for Business Value"
“Data is the new Oil”
In its raw form, oil has little value. Once processed and refined, it helps power the world.
“Big Data has arrived at Seton
Health Care Family, fortunately accompanied by an analytics tool that will help deal with the complexity of more than two million patient contacts a year…”
“At the World Economic Forum
last month in Davos, Switzerland, Big Data was a marquee topic. A report by the forum, “Big Data, Big Impact,” declared data a new class of economic asset, like currency or gold.
“Increasingly, businesses are applying
analytics to social media such as Facebook and Twitter, as well as to product review websites, to try to “understand where customers are, what makes them tick and what they want”, says Deepak Advani, who heads IBM’s predictive analytics group.”
“Companies are being inundated
with data—from information on customer-buying habits to supplychain efficiency. But many managers struggle to make sense of the numbers.”
“…now Watson is being put to work digesting millions of pages of research, incorporating the best clinical practices and monitoring the outcomes to assist physicians in treating cancer patients.”
“Data is the new oil.”
Clive Humby
3 3
The Oscar Senti-meter — a tool developed by the L.A. Times, IBM and the USC Annenberg Innovation Lab — analyzes opinions about the Academy Awards race shared in millions of public messages on Twitter.”
© 2013 IBM Corporation
How did we get here?
© 2013 IBM Corporation
5
5
© 2013 IBM Corporation
As was true in prior eras, the 4th era may increase IT‟s share of worldwide GDP to 4% by 2030
Worldwide IT Spend as % of N-GDP
4%
Worldwide IT Spend as % of GDP
3%
2%
3rd era of IT
4th era of IT
2nd era of IT 1st era of IT Personal Computing
1%
Internet Computing
Smarter Planet
0%
Mainframe
UNIX OS DEC PDP-8 minicomputer IBM 7000 mainframes with transistors IBM PC Apple-1 MS Windows 3.0; WW Web
Cloud Computing Mobility eBusiness Apps Netscape IPO
New IT/business architectures Vertical solutions Cross-industry solutions
Learning systems
Advanced robotics
Smart-net
Source: IBM Market Analysis extrapolated from IDC Black Book for IT and IBM Corp Finance for N-GDP, Forrester Research “Next Wave of IT Investment is Smart Computing” Jan 2010, IBM 6 Research GTO 2011 “Frontiers of IT”
© 2013 IBM Corporation
The world is changing and becoming more…
2 Billion internet users
7
4.6 Billion mobile phones
© 2013 IBM Corporation
A growing Interconnected and Instrumented World
30 billion RFID 500+ Million
users posting 55 Million tweets every day tags today (1.3B in 2005)
4.6 billion
camera phones world wide
1.2 Trillion
searches
2012
100s of millions of GPS enabled
devices sold annually
1+ Billion
active users spending 700 Million minutes per month
8
2+ billion 76 million smart
meters in 2009… 200M by 2014
people on the Web by end 2011
© 2013 IBM Corporation
What is it?
© 2013 IBM Corporation
What is it NOT!
Big Data is Primarily for large datasets
We will have to replace all our old systems in a new world of big data Big Data is only Hadoop
Older transaction data doesn‟t matter any more
Traditional RDBMS Data Warehouses are a thing of the past Big Data is for the internet savy companies. Tradition business are immune We do not have the need nor the budget nor skills, so we don‟t need to worry
10
© 2013 IBM Corporation
The characteristics of big data
Cost efficiently processing the growing Volume
50x
Responding to the increasing Velocity
35 ZB
2020
30 Billion
RFID sensors and counting
Collectively Analyzing the broadening Variety
80% of the
worlds data is unstructured
2010
Establishing the Veracity of big data sources
1 in 3 business leaders don‟t trust
the information they use to make decisions
11
© 2013 IBM Corporation
“Big Data” brings new opportunities
Data Scale
Traditional Data warehouse & Business Intelligence Data in Motion
yr mo wk day hr min sec … ms s
Data at Rest
InfoSpher e Big Insights
Streams filters incoming data
Streams reuses in-database Analytics Persistent Data In-Motion Data
Occasional
Frequent
Real-time
Decision Frequency
Source: Global Technology Outlook 2011
12
© 2013 IBM Corporation
Harness the Power of Big Data & Analytics for Improved Business Outcomes in Banking
© 2013 IBM Corporation
Dramatic forces across the industry require new approaches to help maximize profitability and returns
Turbulent Global Economy
Increased Regulations
Competition for Wallet Share
Capital and Liquidity Pressures
Emboldened Customers
Net Margin Pressures
14
© 2013 IBM Corporation
To address these challenges, big data presents a huge opportunity – if banks can harness it
Volume Velocity Variety
180
million
2
trillion
40
million
Loan records analyzed per day
Calculations of securities data in 1 minute
Emails analyzed per month
Analyze more loans for risk and patterns of fraud
say they don‟t trust Uncover risk and the information Dig deep to discover they identify opportunities customer sentiment use to make faster decisions and attitudes than ever before
Establishing the Veracity of big data sources
15
1 in 3 business leaders don’t trust the
information they use to make decisions
© 2013 IBM Corporation
Is Big Data something new (don‟t we do it already today)?
Existing methods may be sufficient, but additional insights could be surfaced
Business Insights Customer
(basic) propensity to buy
Customer
(basic) satisfaction level
Untapped Insights
(advanced) across Customers, the Marketplace and Operations
Operations
(historic) failure events
Sales support
Complaints resolution
Issues ticketing
Data
Untapped Data
data entry web forms
IVR
Contact Centre
• Full breadth of direct customer interactions • Customer interactions with others • Economic and environmental monitors • Full depth of company processes & systems
data entry
Systems Support
Events & Activities
Direct transactions & customer interactions
Customer interactions with others (3rd party, social)
Economic and environmental monitors
Company processes & systems
16
© 2013 IBM Corporation
Studies show that two thirds of banks have big data activities underway
Customer-centric analytics is the primary functional domain to leverage big data capabilities
Big Data Activities
Financial Services
2% 16%
Customercentric outcomes Operational optimization
50%
21%
Risk / financial management
New business model Employee collaboration
11%
17
Source: The real world use of Big Data, IBM & University of Oxford
17
© 2013 IBM Corporation
$GM uses BigInsights as their landing zone to augment their EDW Enterprise Data Warehouse (EDW)
BNP PARIBAS Bank performs social data analytics leveraging BigInsights to enhance their 360o View of the Customer
.
USAA is using BigInsights to run analytics model for their fraud detection at scale
HSBC uses Hadoop-based solution as their landing zone to augment their EDW Enterprise Data Warehouse (EDW)
18
© 2013 IBM Corporation 18
19
© 2013 IBM Corporation
20
© 2013 IBM Corporation
Imagine if you had all the answers you need to win
21
© 2013 IBM Corporation
Top Use Cases for Big Data and Analytics in Banking & Financial Markets
Create a customerfocused enterprise
• Optimize Offers & Cross Sell • Call Center Efficiency &
Optimize enterprise risk management
• Fraud Detection & Investigation • Counterparty Credit Risk • Security Risk Management
Problem Resolution
Increase flexibility & streamline operations
• Data Staging & Management
• System Log Analysis • System Failure Analysis
22
© 2013 IBM Corporation
The current state of customer management for most banks
Limits cross-sell success & provides a poor customer experience “I have an offer – let me find a customer to sell to.”
Customer Needs and Segment Strategies
Mass Market | Mass Affluent | Small Business
Deposits
Offer Offer Offer
Offer Offer Offer
Direct mail
Relevance? Awareness? Value? Understanding? Clarity?
Customers Point-of-View
• You do not know me or understand my needs. • You ask me multiple times about the same thing.
BC
Card
Offer Offer Offer
Offer Offer Offer
Agent, IVR
• Most of your suggestions are for products & services that seem irrelevant to me.
Online, email
Mortgage
Offer Offer Offer
Offer Offer Offer
• I am not offered solutions based on my multiple relationships.
ATM
Investments
Offer Offer Offer
Offer Offer Offer
Mobile, SMS
• When you recognize that I have a need, you send me multiple offers for different products – it’s confusing.
Chat
…customer insight is limited to a sub-set of available data… …limiting the relevance & timeliness of offers to
23
customers…
© 2013 IBM Corporation
Does this sound familiar?
Today we treat Aki like any other customer in her segment… …but Aki is an individual Bank: “Hi <NAME>! Can we interest you in a credit card?”
Aki: “Oh, look! More junk mail from the bank…”
24
24
© 2013 IBM Corporation © 2013 IBM Corporation
By using only our limited segmentation, we treat Aki like anyone else
Aki holds a mortgage and Action a savings account with us Cash Management Acct.
Impact on Retention Likelihood Impact on to respond Customer positively Lifetime Value to action
Set meeting with Private Banking & Wealth Mgt. Advisor for a Portfolio Review
Equity Bank Line / Secured Line-of-Credit Aki‟s current
credit score & profitability her for Preferredqualifies Gold Credit Card a preferred rate
25
25
© 2013 IBM Corporation © 2013 IBM Corporation
Information helps us understand how Aki is different, but do we use it?
Aki holds a mortgage and a savings account with us
Aki has also Last week posted property Aki asked the photos to Call Center Facebook about loan asking friends processing to vote times This week, she checked mortgage rates on the Web Site three times And today she‟s Aki‟s current tweeted a link credit score & to an article profitability about buying a qualifies her for second home a preferred rate
26
26
© 2013 IBM Corporation © 2013 IBM Corporation
By using all the information we can make our service unique to Aki
Aki holds a Impact on mortgage and Action Retention Aki has also a savings Last week posted property account with Aki asked the photos to us Cash Management Call Acct. Center Facebook about loan asking friends processing to vote times This week, she
Preferred Gold Credit Card
Likelihood Impact on to respond Customer positively Lifetime Value to action
Equity Bank Line / current Aki‟s Secured Line-of-Credit
checked mortgage rates on the Web Site three times
credit score & profitability Mortgagequalifies special rate her for discount 25 basis points a preferred mortgage rate
27
27
And today she‟s tweeted a link to an article about buying a second home
© 2013 IBM Corporation © 2013 IBM Corporation
Big Data can optimize offers & cross-sell success
Improving outcomes for the customer & the bank “I have a customer – what do they need most?”
Customer Needs and Segment Strategies
Mass Market | Mass Affluent | Small Business
Deposits
Governance, Prioritization & Optimization
Direct mail
“The bank knows me & values my relationship.”
Brilliant!
BC
Card
“They seem to know what I need & when I need it.” “The bank isn‟t always selling something.” “They always get me to the right place & never fail to follow up.”
Agent, IVR
Mortgage
Online, email
Investments
Customer Experience & Treatment Strategies
ATM
Integrated Customer Analytics
Mobile, SMS
“There is real value to me in getting all my needs met by one bank.”
Chat
28
The customer feels that the bank understands & responds to their changing needs The bank’s KPI’s improve: Customer Profitability / Satisfaction & Advocacy / Retention
© 2013 IBM Corporation
Leveraging Big Data to optimize offers & cross-sell
Analyze information from all customer interactions & data sources
Internal Customer Data
Structured & unstructured Create a customerfocused enterprise
New Capabilities
•
• •
Transactions
All channels (Web, call-center, branch)
Social
Attitudes, preferences
•
Correspondence
Emails & chats
•
Real-time event detection Micro segmentation Score sentiment & satisfaction more accurately Optimize offers & timing Faster & more accurate predictive models
Contact Center
Notes & chats, customer interaction
External Customer Data
Credit bureaus, demographic (purchased data)
Events
Customer behavior triggers
Outcomes
Geospatial
Where is the customer
Pro-active interactions Improved offer acceptance Increased customer satisfaction
29
© 2013 IBM Corporation
THE BIG DATA PLATFORM ADVANTAGE
30
The warehousing & analytic environment of most banks today
Has a number of limitations
Master Data Management • De-duplicated customer information • Reference data & cross-system code mappings
Master Data Repository
Analytics and Reporting Zone
Business Results
Warehousing
Batch Reporting Limited Descriptive & Predictive Models
Limited Targeting
EDW
Data Integration • Batch (daily) movement • Only structured data ODS • Granular data • Limited history • High-concurrency historical queries • Limited granularity • Expensive to change Connectors
Marts
• Repeatable work • Analytic sandboxes
Limited, Disjointed Search & Discovery
Mediocre Customer Experience
Data Security & Governance • Data lineage & impact analysis • Data privacy & security Metadata and Governance Zone
Metadata Repository
How banks are expanding and evolving their environment by leveraging big data capabilities
Master Data Management • Ingestion De-duplicated customer information & Real-Time Analytic Zone • Reference data & cross-system code • mappings Real-time (µs) data movement, filtering, and analysis (annotation, Master Data Repository classification, correlation, etc) • Structured and unstructured data Connectors Analytics, Analytics Reporting and Reporting & Action Zone Real-time Dashboards & Interactions Batch Reporting Deep Limited Descriptive & Predictive Models
MicroLimited Segment Targeting Targeting
Business Results
Warehousing
Right-Time Customer Interaction
EDW
• High-concurrency historical queries • Limited granularity • Expensive to change Analytic Appliances
Data Integration
ODS
• Batch (daily) movement • Granular Landing & Historical Zone • Only structured data data • Limited • Structured and unstructured data history • Full granular history (> PB) volumes
Historical Repository
Marts
Cheap to change • • Repeatable work Deep analytics • • Analytic sandboxes
Limited, Extensive, Disjointed Contiguous Search & Discovery
Personal Mediocre Customer Experience
Quickly Finding Answers
Data Security & Governance • Data lineage & impact analysis • Data privacy & security Metadata and Governance Zone
Metadata Repository
IBM provides the complete platform to support this evolution
Master Data Management •Ingestion De-duplicated customer information & •Real-Time Reference dataStream & cross-system code mappings Computing Analytic Zone
Master Data Repository
Analytics and Reporting Zone
Business Results
BI / Reporting
Warehousing
Systems Management
Batch Reporting Limited Descriptive Application & Predictive Development Models
Exploration / Visualization
Analytic Applications
DataEDW Warehouse • High-concurrency
Data Integration •Landing Batch (daily) movement & •Historical Only structuredHadoop data Zone ODS • Granular data • Limited history historical queries • Limited granularity • Expensive to change
Limited Targeting
Functional App
Connectors
System
Marts
Appliances • Analytic Repeatable work • Analytic sandboxes
Limited, Disjointed Visualization Search & Discovery & Discovery
Mediocre Customer Experience
Predictive Analytics
Industry App
Content Analytics
Data Security & Governance • Data lineage & impact analysis Information Integration & Governance • Data privacy & security Metadata and Governance Zone
Metadata Repository
IBM provides the complete platform to support this evolution
Master Data Management •Ingestion De-duplicated customer information & •Real-Time Reference dataStream & cross-system code mappings Computing Analytic Zone
Master Data Repository
Analytics and Reporting Zone
Business Results
BI / Reporting
Warehousing IBM Big
Data Batch Platform
Reporting Limited Descriptive Application & Predictive Development Models
Systems Management
Exploration / Visualization
Analytic Applications
DataEDW Warehouse • High-concurrency
Data Integration •Landing Batch (daily) movement & •Historical Only structuredHadoop data Zone ODS • Granular data • Limited history
Limited Targeting
Functional App
Connectors
historical queries • Limited granularity Accelerators • Expensive to change
System
Marts
Appliances • Analytic Repeatable work • Analytic sandboxes
Limited, Disjointed Visualization Search & Discovery & Discovery
Mediocre Customer Experience
Predictive Analytics
Industry App
Content Analytics
Data Security & Governance • Data lineage & impact analysis Information Integration & Governance • Data privacy & security Metadata and Governance Zone
Metadata Repository
The Platform Advantage
The platform enables starting small and growing without throwing away work Shared components and integration between systems lowers deployment cost, time and risk Analytic Applications
BI / Exploration / Functional Industry Predictive Content BI / Reporting Visualization App App Analytics Analytics Reporting
IBM Big Data Platform
Visualization & Discovery Application Development Systems Management
Key points of leverage
– Accelerators built across multiple components to address common use cases – Pre-built integrations between the components using open connectors – Common analytic engines across components (i.e. text analytics) – Common metadata, integration design and governance across components
Accelerators Hadoop System Stream Computing Data Warehouse
Information Integration & Governance
35
Products within the IBM Big Data Platform give direct entry points to addressing the challenges
Summary of challenges Analytic Applications
BI / Exploration / Functional Industry Predictive Content Reporting Visualization App App Analytics Analytics
1. Reduce latency to seconds from days
InfoSphere Streams
1. Feedback from actions taken have too much latency 2. The full measure of customer response is unavailable 3. Inability for LOB to model and test new ideas quickly enough 4. Little of the already collected data is actually utilized to inform the offer
36
IBM Big Data Platform
Visualization & Discovery Application Development
Enterprise Marketing Management
2. Allow LOB to selfprovision multiple sources of data from a single go-to data hub
InfoSphere BigInsights
Accelerators Hadoop System Stream Computing Data Warehouse
3. Provide computing power to test new ideas quickly
PureData for Analytics
Information Integration & Governance
4. Provide analytics against both structured and unstructured data
InfoSphere BigInsights & InfoSphere Streams
HOW TO GET STARTED
Expand with the Big Data Platform for future needs
38
1 – Unlock Big Data
• Customer Need
– Understand existing data sources – Expose the data within existing content management and file systems for new uses, without copying the data to a central location – Search and navigate big data from federated sources
• Value Statement
– Get up and running quickly and discover and retrieve relevant big data – Use big data sources in new information-centric applications
• Customer examples
– Proctor and Gamble – Connect employees with a 360° view of big data sources
• Get started with: IBM Vivisimo Velocity
39
2 – Analyze Raw Data
• Customer Need
– – – – Ingest data as-is into Hadoop and derive insight from it Process large volumes of diverse data within Hadoop Combine insights with the data warehouse Low-cost ad-hoc analysis with Hadoop to test new hypothesis
• Value Statement
– Gain new insights from a variety and combination of data sources – Overcome the prohibitively high cost of converting unstructured data sources to a structured format – Extend the value of the data warehouse by bringing in new types of data and driving new types of analysis – Experiment with analysis of different data combinations to modify the analytic models in the data warehouse
• Customer examples
– Financial Services Regulatory Org – managed additional data types and integrated with their existing data warehouse
• Get started with: InfoSphere BigInsights
40
3 – Simplify your Warehouse
• Customer Need
– Business users are hampered by the poor performance of analytics of a general-purpose enterprise warehouse – queries take hours to run – Enterprise data warehouse is encumbered by too much data for too many purposes – Need to ingest huge volumes of structured data and run multiple concurrent deep analytic queries against it – IT needs to reduce the cost of maintaining the data warehouse
• Value Statement
– Speed – 10-100x faster performance on deep analytic queries – Simplicity – minimal administration and tuning of the appliance – Up and running quickly
• Customer examples
– Catalina Marketing – executing 10x the amount of predictive workloads with the same staff
• Get started with: IBM Netezza
41
4 – Reduce costs with Hadoop
• Customer Need
– Reduce the overall cost to maintain data in the warehouse – often its seldom used and kept „just in case‟ – Lower costs as data grows within the data warehouse – Reduce expensive infrastructure used for processing and transformations
• Value Statement
– Support existing and new workloads on the most cost effective alternative, while preserving existing access and queries – Lower storage costs – Reduce processing costs by pushing processing onto commodity hardware and the parallel processing of Hadoop
• Customer examples
– Financial Services Firm – move processing of applications and reports to Hadoop Hbase while preserving existing queries
• Get started with: IBM InfoSphere BigInsights
42
5 – Analyze Streaming Data
• Customer Need
– Harness and process streaming data sources – Select valuable data and insights to be stored for further processing Streaming Data – Quickly process and analyze perishable Sources data, and take timely action
• Value Statement
Streams Computing
– Significantly reduced processing time and cost – process and then store what‟s valuable – React in real-time to capture opportunities before they expire
• Customer examples
ACTION
– Ufone – Telco Call Detail Record (CDR) analytics for customer churn prevention
• Get started with: InfoSphere Streams
43
The Big Data and Analytics journey
Typical Big Data and Analytics Adoption Path
Educate
Focused on knowledge gathering and market observations
Explore
Developing strategy and roadmap based on business needs and challenges
Engage
Piloting Big Data and Analytics initiatives to validate value and requirements
Execute
Deployed two or more Big Data and Analytics initiatives and continuing to apply advanced analytics
Join the business community Big Data and Analytics case studies, whitepapers and IBM Institute for Business Value reports IBM Briefings, Solution Centers
IBM Readiness Assessments for Big Data and Analytics
Solution Design and Proof of Concept -Validate business value for business use cases -Demonstrate Big Data and Analytics capabilities to execute business use cases
Enterprisewide Big Data and Analytics initiatives
-Incremental value across multiple use cases -Leverage investment from re-using the same Big Data and Analytics platform -Enterprise data platform to optimize business outcomes
Self-paced learning, exploration with downloads and test environment
BigDatauniversity.com, YouTube Big Data Channel
Join the technical community
Moving Forward
IBM can assist in choosing the right path to deliver rapid and measurable business results
A workshop to help identify and prioritize potential use cases
A Client Value Engagement to help determine potential business impact
A pilot to Defining the demonstrate new components required as part of the solution capabilities that could be delivered to the architecture organization
46 46
© 2013 IBM Corporation
© 2013 IBM Corporation
Intrinsic Property of Data … it grows
90%
of the world’s data was created in the last two years
80%
of the world’s data today is unstructured
20%
of available data can be processed by traditional systems
1 in 2
business leaders don’t have access to data they need
83%
of CIO’s cited BI and analytics as part of their visionary plan
5.4X
more likely that top performers use business analytics
© 2013 IBM Corporation
2 Source: GigaOM, Software Group, IBM Institute for Business Value"
“Data is the new Oil”
In its raw form, oil has little value. Once processed and refined, it helps power the world.
“Big Data has arrived at Seton
Health Care Family, fortunately accompanied by an analytics tool that will help deal with the complexity of more than two million patient contacts a year…”
“At the World Economic Forum
last month in Davos, Switzerland, Big Data was a marquee topic. A report by the forum, “Big Data, Big Impact,” declared data a new class of economic asset, like currency or gold.
“Increasingly, businesses are applying
analytics to social media such as Facebook and Twitter, as well as to product review websites, to try to “understand where customers are, what makes them tick and what they want”, says Deepak Advani, who heads IBM’s predictive analytics group.”
“Companies are being inundated
with data—from information on customer-buying habits to supplychain efficiency. But many managers struggle to make sense of the numbers.”
“…now Watson is being put to work digesting millions of pages of research, incorporating the best clinical practices and monitoring the outcomes to assist physicians in treating cancer patients.”
“Data is the new oil.”
Clive Humby
3 3
The Oscar Senti-meter — a tool developed by the L.A. Times, IBM and the USC Annenberg Innovation Lab — analyzes opinions about the Academy Awards race shared in millions of public messages on Twitter.”
© 2013 IBM Corporation
How did we get here?
© 2013 IBM Corporation
5
5
© 2013 IBM Corporation
As was true in prior eras, the 4th era may increase IT‟s share of worldwide GDP to 4% by 2030
Worldwide IT Spend as % of N-GDP
4%
Worldwide IT Spend as % of GDP
3%
2%
3rd era of IT
4th era of IT
2nd era of IT 1st era of IT Personal Computing
1%
Internet Computing
Smarter Planet
0%
Mainframe
UNIX OS DEC PDP-8 minicomputer IBM 7000 mainframes with transistors IBM PC Apple-1 MS Windows 3.0; WW Web
Cloud Computing Mobility eBusiness Apps Netscape IPO
New IT/business architectures Vertical solutions Cross-industry solutions
Learning systems
Advanced robotics
Smart-net
Source: IBM Market Analysis extrapolated from IDC Black Book for IT and IBM Corp Finance for N-GDP, Forrester Research “Next Wave of IT Investment is Smart Computing” Jan 2010, IBM 6 Research GTO 2011 “Frontiers of IT”
© 2013 IBM Corporation
The world is changing and becoming more…
2 Billion internet users
7
4.6 Billion mobile phones
© 2013 IBM Corporation
A growing Interconnected and Instrumented World
30 billion RFID 500+ Million
users posting 55 Million tweets every day tags today (1.3B in 2005)
4.6 billion
camera phones world wide
1.2 Trillion
searches
2012
100s of millions of GPS enabled
devices sold annually
1+ Billion
active users spending 700 Million minutes per month
8
2+ billion 76 million smart
meters in 2009… 200M by 2014
people on the Web by end 2011
© 2013 IBM Corporation
What is it?
© 2013 IBM Corporation
What is it NOT!
Big Data is Primarily for large datasets
We will have to replace all our old systems in a new world of big data Big Data is only Hadoop
Older transaction data doesn‟t matter any more
Traditional RDBMS Data Warehouses are a thing of the past Big Data is for the internet savy companies. Tradition business are immune We do not have the need nor the budget nor skills, so we don‟t need to worry
10
© 2013 IBM Corporation
The characteristics of big data
Cost efficiently processing the growing Volume
50x
Responding to the increasing Velocity
35 ZB
2020
30 Billion
RFID sensors and counting
Collectively Analyzing the broadening Variety
80% of the
worlds data is unstructured
2010
Establishing the Veracity of big data sources
1 in 3 business leaders don‟t trust
the information they use to make decisions
11
© 2013 IBM Corporation
“Big Data” brings new opportunities
Data Scale
Traditional Data warehouse & Business Intelligence Data in Motion
yr mo wk day hr min sec … ms s
Data at Rest
InfoSpher e Big Insights
Streams filters incoming data
Streams reuses in-database Analytics Persistent Data In-Motion Data
Occasional
Frequent
Real-time
Decision Frequency
Source: Global Technology Outlook 2011
12
© 2013 IBM Corporation
Harness the Power of Big Data & Analytics for Improved Business Outcomes in Banking
© 2013 IBM Corporation
Dramatic forces across the industry require new approaches to help maximize profitability and returns
Turbulent Global Economy
Increased Regulations
Competition for Wallet Share
Capital and Liquidity Pressures
Emboldened Customers
Net Margin Pressures
14
© 2013 IBM Corporation
To address these challenges, big data presents a huge opportunity – if banks can harness it
Volume Velocity Variety
180
million
2
trillion
40
million
Loan records analyzed per day
Calculations of securities data in 1 minute
Emails analyzed per month
Analyze more loans for risk and patterns of fraud
say they don‟t trust Uncover risk and the information Dig deep to discover they identify opportunities customer sentiment use to make faster decisions and attitudes than ever before
Establishing the Veracity of big data sources
15
1 in 3 business leaders don’t trust the
information they use to make decisions
© 2013 IBM Corporation
Is Big Data something new (don‟t we do it already today)?
Existing methods may be sufficient, but additional insights could be surfaced
Business Insights Customer
(basic) propensity to buy
Customer
(basic) satisfaction level
Untapped Insights
(advanced) across Customers, the Marketplace and Operations
Operations
(historic) failure events
Sales support
Complaints resolution
Issues ticketing
Data
Untapped Data
data entry web forms
IVR
Contact Centre
• Full breadth of direct customer interactions • Customer interactions with others • Economic and environmental monitors • Full depth of company processes & systems
data entry
Systems Support
Events & Activities
Direct transactions & customer interactions
Customer interactions with others (3rd party, social)
Economic and environmental monitors
Company processes & systems
16
© 2013 IBM Corporation
Studies show that two thirds of banks have big data activities underway
Customer-centric analytics is the primary functional domain to leverage big data capabilities
Big Data Activities
Financial Services
2% 16%
Customercentric outcomes Operational optimization
50%
21%
Risk / financial management
New business model Employee collaboration
11%
17
Source: The real world use of Big Data, IBM & University of Oxford
17
© 2013 IBM Corporation
$GM uses BigInsights as their landing zone to augment their EDW Enterprise Data Warehouse (EDW)
BNP PARIBAS Bank performs social data analytics leveraging BigInsights to enhance their 360o View of the Customer
.
USAA is using BigInsights to run analytics model for their fraud detection at scale
HSBC uses Hadoop-based solution as their landing zone to augment their EDW Enterprise Data Warehouse (EDW)
18
© 2013 IBM Corporation 18
19
© 2013 IBM Corporation
20
© 2013 IBM Corporation
Imagine if you had all the answers you need to win
21
© 2013 IBM Corporation
Top Use Cases for Big Data and Analytics in Banking & Financial Markets
Create a customerfocused enterprise
• Optimize Offers & Cross Sell • Call Center Efficiency &
Optimize enterprise risk management
• Fraud Detection & Investigation • Counterparty Credit Risk • Security Risk Management
Problem Resolution
Increase flexibility & streamline operations
• Data Staging & Management
• System Log Analysis • System Failure Analysis
22
© 2013 IBM Corporation
The current state of customer management for most banks
Limits cross-sell success & provides a poor customer experience “I have an offer – let me find a customer to sell to.”
Customer Needs and Segment Strategies
Mass Market | Mass Affluent | Small Business
Deposits
Offer Offer Offer
Offer Offer Offer
Direct mail
Relevance? Awareness? Value? Understanding? Clarity?
Customers Point-of-View
• You do not know me or understand my needs. • You ask me multiple times about the same thing.
BC
Card
Offer Offer Offer
Offer Offer Offer
Agent, IVR
• Most of your suggestions are for products & services that seem irrelevant to me.
Online, email
Mortgage
Offer Offer Offer
Offer Offer Offer
• I am not offered solutions based on my multiple relationships.
ATM
Investments
Offer Offer Offer
Offer Offer Offer
Mobile, SMS
• When you recognize that I have a need, you send me multiple offers for different products – it’s confusing.
Chat
…customer insight is limited to a sub-set of available data… …limiting the relevance & timeliness of offers to
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customers…
© 2013 IBM Corporation
Does this sound familiar?
Today we treat Aki like any other customer in her segment… …but Aki is an individual Bank: “Hi <NAME>! Can we interest you in a credit card?”
Aki: “Oh, look! More junk mail from the bank…”
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© 2013 IBM Corporation © 2013 IBM Corporation
By using only our limited segmentation, we treat Aki like anyone else
Aki holds a mortgage and Action a savings account with us Cash Management Acct.
Impact on Retention Likelihood Impact on to respond Customer positively Lifetime Value to action
Set meeting with Private Banking & Wealth Mgt. Advisor for a Portfolio Review
Equity Bank Line / Secured Line-of-Credit Aki‟s current
credit score & profitability her for Preferredqualifies Gold Credit Card a preferred rate
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© 2013 IBM Corporation © 2013 IBM Corporation
Information helps us understand how Aki is different, but do we use it?
Aki holds a mortgage and a savings account with us
Aki has also Last week posted property Aki asked the photos to Call Center Facebook about loan asking friends processing to vote times This week, she checked mortgage rates on the Web Site three times And today she‟s Aki‟s current tweeted a link credit score & to an article profitability about buying a qualifies her for second home a preferred rate
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© 2013 IBM Corporation © 2013 IBM Corporation
By using all the information we can make our service unique to Aki
Aki holds a Impact on mortgage and Action Retention Aki has also a savings Last week posted property account with Aki asked the photos to us Cash Management Call Acct. Center Facebook about loan asking friends processing to vote times This week, she
Preferred Gold Credit Card
Likelihood Impact on to respond Customer positively Lifetime Value to action
Equity Bank Line / current Aki‟s Secured Line-of-Credit
checked mortgage rates on the Web Site three times
credit score & profitability Mortgagequalifies special rate her for discount 25 basis points a preferred mortgage rate
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And today she‟s tweeted a link to an article about buying a second home
© 2013 IBM Corporation © 2013 IBM Corporation
Big Data can optimize offers & cross-sell success
Improving outcomes for the customer & the bank “I have a customer – what do they need most?”
Customer Needs and Segment Strategies
Mass Market | Mass Affluent | Small Business
Deposits
Governance, Prioritization & Optimization
Direct mail
“The bank knows me & values my relationship.”
Brilliant!
BC
Card
“They seem to know what I need & when I need it.” “The bank isn‟t always selling something.” “They always get me to the right place & never fail to follow up.”
Agent, IVR
Mortgage
Online, email
Investments
Customer Experience & Treatment Strategies
ATM
Integrated Customer Analytics
Mobile, SMS
“There is real value to me in getting all my needs met by one bank.”
Chat
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The customer feels that the bank understands & responds to their changing needs The bank’s KPI’s improve: Customer Profitability / Satisfaction & Advocacy / Retention
© 2013 IBM Corporation
Leveraging Big Data to optimize offers & cross-sell
Analyze information from all customer interactions & data sources
Internal Customer Data
Structured & unstructured Create a customerfocused enterprise
New Capabilities
•
• •
Transactions
All channels (Web, call-center, branch)
Social
Attitudes, preferences
•
Correspondence
Emails & chats
•
Real-time event detection Micro segmentation Score sentiment & satisfaction more accurately Optimize offers & timing Faster & more accurate predictive models
Contact Center
Notes & chats, customer interaction
External Customer Data
Credit bureaus, demographic (purchased data)
Events
Customer behavior triggers
Outcomes
Geospatial
Where is the customer
Pro-active interactions Improved offer acceptance Increased customer satisfaction
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© 2013 IBM Corporation
THE BIG DATA PLATFORM ADVANTAGE
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The warehousing & analytic environment of most banks today
Has a number of limitations
Master Data Management • De-duplicated customer information • Reference data & cross-system code mappings
Master Data Repository
Analytics and Reporting Zone
Business Results
Warehousing
Batch Reporting Limited Descriptive & Predictive Models
Limited Targeting
EDW
Data Integration • Batch (daily) movement • Only structured data ODS • Granular data • Limited history • High-concurrency historical queries • Limited granularity • Expensive to change Connectors
Marts
• Repeatable work • Analytic sandboxes
Limited, Disjointed Search & Discovery
Mediocre Customer Experience
Data Security & Governance • Data lineage & impact analysis • Data privacy & security Metadata and Governance Zone
Metadata Repository
How banks are expanding and evolving their environment by leveraging big data capabilities
Master Data Management • Ingestion De-duplicated customer information & Real-Time Analytic Zone • Reference data & cross-system code • mappings Real-time (µs) data movement, filtering, and analysis (annotation, Master Data Repository classification, correlation, etc) • Structured and unstructured data Connectors Analytics, Analytics Reporting and Reporting & Action Zone Real-time Dashboards & Interactions Batch Reporting Deep Limited Descriptive & Predictive Models
MicroLimited Segment Targeting Targeting
Business Results
Warehousing
Right-Time Customer Interaction
EDW
• High-concurrency historical queries • Limited granularity • Expensive to change Analytic Appliances
Data Integration
ODS
• Batch (daily) movement • Granular Landing & Historical Zone • Only structured data data • Limited • Structured and unstructured data history • Full granular history (> PB) volumes
Historical Repository
Marts
Cheap to change • • Repeatable work Deep analytics • • Analytic sandboxes
Limited, Extensive, Disjointed Contiguous Search & Discovery
Personal Mediocre Customer Experience
Quickly Finding Answers
Data Security & Governance • Data lineage & impact analysis • Data privacy & security Metadata and Governance Zone
Metadata Repository
IBM provides the complete platform to support this evolution
Master Data Management •Ingestion De-duplicated customer information & •Real-Time Reference dataStream & cross-system code mappings Computing Analytic Zone
Master Data Repository
Analytics and Reporting Zone
Business Results
BI / Reporting
Warehousing
Systems Management
Batch Reporting Limited Descriptive Application & Predictive Development Models
Exploration / Visualization
Analytic Applications
DataEDW Warehouse • High-concurrency
Data Integration •Landing Batch (daily) movement & •Historical Only structuredHadoop data Zone ODS • Granular data • Limited history historical queries • Limited granularity • Expensive to change
Limited Targeting
Functional App
Connectors
System
Marts
Appliances • Analytic Repeatable work • Analytic sandboxes
Limited, Disjointed Visualization Search & Discovery & Discovery
Mediocre Customer Experience
Predictive Analytics
Industry App
Content Analytics
Data Security & Governance • Data lineage & impact analysis Information Integration & Governance • Data privacy & security Metadata and Governance Zone
Metadata Repository
IBM provides the complete platform to support this evolution
Master Data Management •Ingestion De-duplicated customer information & •Real-Time Reference dataStream & cross-system code mappings Computing Analytic Zone
Master Data Repository
Analytics and Reporting Zone
Business Results
BI / Reporting
Warehousing IBM Big
Data Batch Platform
Reporting Limited Descriptive Application & Predictive Development Models
Systems Management
Exploration / Visualization
Analytic Applications
DataEDW Warehouse • High-concurrency
Data Integration •Landing Batch (daily) movement & •Historical Only structuredHadoop data Zone ODS • Granular data • Limited history
Limited Targeting
Functional App
Connectors
historical queries • Limited granularity Accelerators • Expensive to change
System
Marts
Appliances • Analytic Repeatable work • Analytic sandboxes
Limited, Disjointed Visualization Search & Discovery & Discovery
Mediocre Customer Experience
Predictive Analytics
Industry App
Content Analytics
Data Security & Governance • Data lineage & impact analysis Information Integration & Governance • Data privacy & security Metadata and Governance Zone
Metadata Repository
The Platform Advantage
The platform enables starting small and growing without throwing away work Shared components and integration between systems lowers deployment cost, time and risk Analytic Applications
BI / Exploration / Functional Industry Predictive Content BI / Reporting Visualization App App Analytics Analytics Reporting
IBM Big Data Platform
Visualization & Discovery Application Development Systems Management
Key points of leverage
– Accelerators built across multiple components to address common use cases – Pre-built integrations between the components using open connectors – Common analytic engines across components (i.e. text analytics) – Common metadata, integration design and governance across components
Accelerators Hadoop System Stream Computing Data Warehouse
Information Integration & Governance
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Products within the IBM Big Data Platform give direct entry points to addressing the challenges
Summary of challenges Analytic Applications
BI / Exploration / Functional Industry Predictive Content Reporting Visualization App App Analytics Analytics
1. Reduce latency to seconds from days
InfoSphere Streams
1. Feedback from actions taken have too much latency 2. The full measure of customer response is unavailable 3. Inability for LOB to model and test new ideas quickly enough 4. Little of the already collected data is actually utilized to inform the offer
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IBM Big Data Platform
Visualization & Discovery Application Development
Enterprise Marketing Management
2. Allow LOB to selfprovision multiple sources of data from a single go-to data hub
InfoSphere BigInsights
Accelerators Hadoop System Stream Computing Data Warehouse
3. Provide computing power to test new ideas quickly
PureData for Analytics
Information Integration & Governance
4. Provide analytics against both structured and unstructured data
InfoSphere BigInsights & InfoSphere Streams
HOW TO GET STARTED
Expand with the Big Data Platform for future needs
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1 – Unlock Big Data
• Customer Need
– Understand existing data sources – Expose the data within existing content management and file systems for new uses, without copying the data to a central location – Search and navigate big data from federated sources
• Value Statement
– Get up and running quickly and discover and retrieve relevant big data – Use big data sources in new information-centric applications
• Customer examples
– Proctor and Gamble – Connect employees with a 360° view of big data sources
• Get started with: IBM Vivisimo Velocity
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2 – Analyze Raw Data
• Customer Need
– – – – Ingest data as-is into Hadoop and derive insight from it Process large volumes of diverse data within Hadoop Combine insights with the data warehouse Low-cost ad-hoc analysis with Hadoop to test new hypothesis
• Value Statement
– Gain new insights from a variety and combination of data sources – Overcome the prohibitively high cost of converting unstructured data sources to a structured format – Extend the value of the data warehouse by bringing in new types of data and driving new types of analysis – Experiment with analysis of different data combinations to modify the analytic models in the data warehouse
• Customer examples
– Financial Services Regulatory Org – managed additional data types and integrated with their existing data warehouse
• Get started with: InfoSphere BigInsights
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3 – Simplify your Warehouse
• Customer Need
– Business users are hampered by the poor performance of analytics of a general-purpose enterprise warehouse – queries take hours to run – Enterprise data warehouse is encumbered by too much data for too many purposes – Need to ingest huge volumes of structured data and run multiple concurrent deep analytic queries against it – IT needs to reduce the cost of maintaining the data warehouse
• Value Statement
– Speed – 10-100x faster performance on deep analytic queries – Simplicity – minimal administration and tuning of the appliance – Up and running quickly
• Customer examples
– Catalina Marketing – executing 10x the amount of predictive workloads with the same staff
• Get started with: IBM Netezza
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4 – Reduce costs with Hadoop
• Customer Need
– Reduce the overall cost to maintain data in the warehouse – often its seldom used and kept „just in case‟ – Lower costs as data grows within the data warehouse – Reduce expensive infrastructure used for processing and transformations
• Value Statement
– Support existing and new workloads on the most cost effective alternative, while preserving existing access and queries – Lower storage costs – Reduce processing costs by pushing processing onto commodity hardware and the parallel processing of Hadoop
• Customer examples
– Financial Services Firm – move processing of applications and reports to Hadoop Hbase while preserving existing queries
• Get started with: IBM InfoSphere BigInsights
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5 – Analyze Streaming Data
• Customer Need
– Harness and process streaming data sources – Select valuable data and insights to be stored for further processing Streaming Data – Quickly process and analyze perishable Sources data, and take timely action
• Value Statement
Streams Computing
– Significantly reduced processing time and cost – process and then store what‟s valuable – React in real-time to capture opportunities before they expire
• Customer examples
ACTION
– Ufone – Telco Call Detail Record (CDR) analytics for customer churn prevention
• Get started with: InfoSphere Streams
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The Big Data and Analytics journey
Typical Big Data and Analytics Adoption Path
Educate
Focused on knowledge gathering and market observations
Explore
Developing strategy and roadmap based on business needs and challenges
Engage
Piloting Big Data and Analytics initiatives to validate value and requirements
Execute
Deployed two or more Big Data and Analytics initiatives and continuing to apply advanced analytics
Join the business community Big Data and Analytics case studies, whitepapers and IBM Institute for Business Value reports IBM Briefings, Solution Centers
IBM Readiness Assessments for Big Data and Analytics
Solution Design and Proof of Concept -Validate business value for business use cases -Demonstrate Big Data and Analytics capabilities to execute business use cases
Enterprisewide Big Data and Analytics initiatives
-Incremental value across multiple use cases -Leverage investment from re-using the same Big Data and Analytics platform -Enterprise data platform to optimize business outcomes
Self-paced learning, exploration with downloads and test environment
BigDatauniversity.com, YouTube Big Data Channel
Join the technical community
Moving Forward
IBM can assist in choosing the right path to deliver rapid and measurable business results
A workshop to help identify and prioritize potential use cases
A Client Value Engagement to help determine potential business impact
A pilot to Defining the demonstrate new components required as part of the solution capabilities that could be delivered to the architecture organization
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© 2013 IBM Corporation
