How advanced analytics is impacting the banking sector

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A Hitachi Data Systems Company

How advanced analytics is impacting the banking sectorPentaho - Architected for the Future of Analytics MICHAEL HADDAD


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What I can (hopefully) tell you about:

Analytics in banking

Hitachi Data Systems and Pentaho

Blending data

Data scientists

Technology for analytics


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Six to 10 mega trends• investing in compliance

Regulatory changes and complexity

• new challengers to disrupt • changing the way customers interact

Digitalisation and technological advances

Changes in investment and capital sources and returns

• different expectations and ways of interacting with financial institutions

Demographic and behavioural changes

• who wants to be a banker?

Global talent and skills race

Business operating model pressures

Reference: Jagdev Kenth, http://www.resilience.willis.com/;

http://www.resilience.willis.com/

http://www.resilience.willis.com/


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Using Customer Analytics to Drive

Contextual Experiences

Expedited Deployment of Digital

DeliveryMobile-First Design Increasing Digital and

Social Selling

Mass Market Acceptance of Mobile

Payments

Focus on Security and Authentication

Industry Consolidation

Enhanced Customer Incentivisation

Investment in Innovation,

Incubation and Uncommon Alliances

Increased Impact of Digital Disruptors

Six to 10 mega trends

Reference: Jim Marous, http://thefinancialbrand.com/


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Data analytics and these trends


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Use Data Analytics to

Reduce riskReact to

events and circumstances

Deliver personalised

services

Drive new technologies

Retain skills

It can be a differentiator


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Use cases

Customer analytics, segmentation,

attrition analysis, profitability analysis

Marketing analytics, success of campaigns

Fraud analytics, detection analysis

Risk analytics, credit risk analysis


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BI then and BA now

Ref: https://rapidminer.com/


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Hitachi and Pentaho


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Hitachi and Pentaho


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Modern, unified data integration and business analytics platform• Broadest and deepest big data integration• Embeddable, cloud-ready analytics• Big data blending at the source

Fast and Broad Innovation• Open source development model• 100% java, pluggable and extensible

Critical mass achieved• Over 1,200 commercial customers• Over 10,000 production deployments

Pentaho MissionEnabling the future of analytics


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Pentaho Key PointsMeeting the demands of the big data-driven enterprise

Blended Big Data at the source for more accurate insights

Enterprise-ready data integration and simplified embedding for any environment

Simplified analytics experience with a modern interface

Analytics

Blended Big Data

EnterpriseBig Data

Integration


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Simplified Analytics

Experience

Simplified Administration, Management &

DeploymentDifferent Audiences, Different Needs

Higher User Expectations

Landslide of Data Blended Big Data for

Comprehensive Analytics

Enabling the Modern Big Data Driven EnterpriseKey pressure points disrupting the traditional analytics market


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ANY Analytics• Reports• Dashboards• Visualizations• Discovery• Predictive

Analytics

ANY Environment• Data warehouses• Data marts• Stack vendors• Cloud• Embedded

Existing & New Data

Infrastructure & Processes

ANY Data• Relational• Operational• Big Data• Data sources not yet

anticipated…

Billing

Location

Social Media

Customer

Web

Network

The New RealitySimplified Analysis for all Users


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Blended Big Data


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Improve operational effectiveness• Machines/sensors: predict failures, network attacks• Financial risk management: reduce fraud, increase

security

Reduce data warehouse cost• Integrate new data sources without increased database

cost• Provide online access to ‘dark data’

Drive incremental revenue• Predict customer behaviour across all

channels• Understand and monetize customer

behaviour• Begin to monetize data as a service

Customer Value from Big DataMonetizing big data-driven use cases driving need to blend data


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Analytics

Analyze quality of service: • Network outages• Dropped calls • Poor quality• Calls to support center

For profiles of customers:• Up for renewal• Profitable• Multiple agreements/services• In competitive area

Determine best action to take:• Billing Credit• Customer Coupon • No Action

EDW

ExistingETL Tool

or PDI

Customer

Billing

Provisioning

Call Detail Records from: • Billing• Payment• Usage

NoSQLNetwork

Location

PDI

Call Detail Records from Network: • Outages• Drops• Service Quality

PDI

Blend revenue-related and quality-of-service data together to find customers at risk

Why Blending at the Source MattersCustomer Experience Analytics for loyalty and revenue


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• Just in time blending of data from multiple sources for a complete picture• Connect, combine and transform data from multiple sources• Query data directly from any transformation• Access architected blends with the full spectrum of Pentaho Analytics• Manage governance and security of data for on-going accuracy

Accurate, Blended Big Data AnalyticsOptimally stored data, blended when needed

EDW

ExistingETL Toolor PDICusto

mer

Billing

Provisioning

NoSQLNetwork

Location

PDI

PDI Analytics

Just in time blending


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Data Science


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The data analyst / data scientist


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Data scientist


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Why do we need them?


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Why do we need them?



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Analytics allows us to complement the machine

Intelligence Augmentation

An increasing number of humans

work for the machine

Analytics improves human’s insights

Weak human plus computer is better

than a grandmaster


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Analytics technology


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Pentaho and MongoDB


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Pentaho and NoSQL Databases

© Hitachi Data Systems Corporation 2015. All rights reserved.29

Vendor-neutral: Integration of disparate technologies curated and supported via ecosystem of partners

Flexible: Flexibility built into architecture at multiple levels from infrastructure to Data services to analytics libraries

Modular: Pluggable light-weight architecture to allow appropriate tool for data management

Solution focused: Focus on the full stack from the physical infrastructure to analytics

modules to help with solution building

OPAL (Open Platform for Analytics)


Hitachi Big Data Analytics Ecosystem

Data Connectors (HSDP For Streaming

Data)

Data Services (Pentaho Tools for Integration, Orchestration, ETL, Indexing)

Analytic Libraries(R, Weka, …)

System Integration & Customization Services

Visualization(Extracts, Dashboards,

Reports)

Platform Services(Hadoop, Storm, Spark, Cassandra, MongoDB)

Infrastructure(Hyper-Converged Scale-Out Platform)

Cloud-EnabledPublic Private Hybrid

Telecom HealthCare Public SafetyData Center FinanceOTHER ONE

HITACHI SOLUTIONS

Common Analytics Framework(Developed jointly with Hitachi R&D Labs)

BIG DATA LAKE

Video, Image & Audio

Email & Documents

TX

Transactional Data

TX

IT, Sensor & Machine Logs

Social Media

Custom Solutions


Big Data Solution Development Challenges

A big part of solution development for analytics involves orchestration of various moving parts in the infrastructure:

‒ Domain experts need access to appropriate data sets backed by technology stack to gather insights based on intuitions.

‒ Time to value for a solution can be accelerated by providing an agile mechanism to deploy from prototype to production application quickly.

Infrastructure Installation

Component Deployment

Application Logic Implementation Testing

Configure Cluster & Monitoring


Easier to Provision Infrastructure

Easier to Deploy Services

Easier to Monitor, Troubleshoot, and Validate

Easier to Develop & Integrate New Services

Opal – Big Data Analytics Made Easy & Robust

Infrastructure Installation

Component Deployment

Application Logic Implementation Testing

Configure Cluster & Monitoring

Before

With Opal


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Thank You

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How advanced analytics is impacting the banking sector