financial,(telco,(retail,(&( manufacturing:(hadoop...

1 © Cloudera, Inc. All rights reserved.

Financial, Telco, Retail, & Manufacturing: Hadoop Business Services for Industries Ho Wing Leong, ASEAN


Cloudera company snapshot

Founded 2008, by former employees of Company Largest Hadoop Company Globally Employees Today 800+ worldwide World Class Support More than 100 24x7 global staff

Pro-‐acQve & predicQve support programs using our EDH Mission CriQcal ProducQon deployments in run-‐the-‐business applicaQons

worldwide – Financial Services, Retail, Telecom, Media, Health Care, Energy, Government

The Largest Ecosystem More than 1,450 Partners Cloudera University Over 40,000 trained Open Source Leaders Cloudera employees are leading developers & contributors to

the complete Apache Hadoop ecosystem of projects.


Customer success across industries

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Financial Services

Telecom

Healthcare & Life Sciences

Media & Technology

Retail & CP

Public Sector


Explore the PossibiliQes of SAS and Cloudera • The combinaQon of SAS analyQcs and Cloudera’s Enterprise Data Hub (EDH) is a common recipe for AnalyQcs at Scale. • While Cloudera’s EDH makes it feasible and economically viable to store and manage extreme volumes of data in one place, SAS’ In-‐Memory AnalyQcs gives you the power to analyze and mine data at Scale … all on a single system.


SAS & Cloudera Partnership The Tightest Product Level IntegraQon

Execu8ve sponsored partnership which spans R&D, Product Management, Sales, Marke8ng, Consul8ng & Educa8on Services. SAS product integra8on with Cloudera is the most extensive of all the commercial Hadoop distribu8ons •  SAS internal development teams have a Cloudera first policy and all internal work is performed on Cloudera clusters. •  Dedicated Cloudera resources at Cloudera HQ and SAS HQ working with SAS R&D •  SAS has dedicated R&D resources to opQmize SAS soluQons for the Cloudera pladorm •  Pordolio includes integraQon with Access to Hadoop, Access to Cloudera, Visual AnalyQcs, In-‐Memory StaQsQcs, High Performance AnalyQcs, Scoring Accelerator for Cloudera Hadoop & Visual StaQsQcs among others…


SAS & Cloudera Partnership Strong Go To Market Alignment

•  Engineering schedule coordinaQon to ensure quick uptake of new releases from each side •  SAS / Cloudera Webinar Series •  Reciprocal Services Agreement in place •  Joint Training course developed to provide educaQon on Cloudera Hadoop and SAS content for analyQcs on big data •  SAS SoluQons OnDemand Preferred Vendor is Cloudera •  SAS Visual AnalyQcs and Cloudera Enterprise Data Hub Starter Service package

Cloudera and SAS ConfidenQal


SAS & Cloudera SoluQon Stack

Next-‐Genera8on SAS

® User

SAS® User

MPI Based

User Interface

Metadata

Data Access

Data Processing

File System

SAS® LASR™ AnalyQc Server

HDFS

Base SAS & SAS/ACCESS® Interface to Hadoop™

SAS Metadata

Pig

Map Reduce

In-‐Memory Data Access

SAS® Display Manager SAS® Visual AnalyQcs SAS® Enterprise Miner™

SAS® Data IntegraQon

SAS® Enterprise Guide®

Hive SAS Embedded

Process DS2 Accelerators

SAS® High-‐ Performance

AnalyQc Procedures

HBASE Impala


Three Factors Entrenching Big Data in Financial Services 1. Compliance and Strategy: Growth in a Stringent Regulatory Environment

Accenture and CEB TowerGroup say…

Sources: Dash, Eric. “FeasQng on Paperwork,” The New York Times. September 8, 2011. Accenture. Coming to Terms with Dodd-‐Frank. January 2013.

believe Dodd-‐Frank will strengthen their compeQQve posiQoning

agree Dodd-‐Frank will benefit their own company’s customers

anQcipate spending $50 million or more on compliance

64%

83%

50%


Three Factors Entrenching Big Data in Financial Services 2. Mass PersonalizaQon: Tailoring Products and Services Across the Value Chain

DeloiVe and Core Profit say…

Average customer acquisiQon costs retail banks

MORE THAN $350 and requires customers to

carry balances NEARING $10,000 just to break even

Sources: Deloire. 2014 Banking Industry Outlook. February 2014. Andera & CoreProfit. The Future of Account Opening 2011. June 2011.


Three Factors Entrenching Big Data in Financial Services 3. Towards CompeQQve Advantage: ConsolidaQon Around High-‐Return OpportuniQes

Morgan Stanley Research and Oliver Wyman say…

During the past 20 years, the margins on deposits and cash

equiQes have DECLINED BY 33% TO 50% while the need for compuQng

power in FinServ has GROWN 200% TO 500% FASTER THAN REVENUE

Sources: Morgan Stanley Research & Oliver Wyman. Wholesale and Investment Banking Outlook 2014. March 2014. Oliver Wyman. The State of the Financial Services Industry 2013. January 2013.


Customer Experience Mgmt. (Customer 360) Network OpQmizaQon

Data MoneQzaQon OperaQonal AnalyQcs

Pusng Big-‐Data to Work for Telcos Key Use Cases and Areas of ApplicaQon for Today’s Telcos


Customer Experience Mgmt. (Customer 360) Network OpQmizaQon

Data MoneQzaQon OperaQonal AnalyQcs

Customer Churn

AnalyQcs ProacQve Care

Targeted MarkeQng/

PersonalizaQon

Network Investment & Planning

Real –Time Network AnalyQcs

Capacity Planning & OpQmizaQon

Revenue Leakage/ Assurance

Enterprise Security AnalyQcs

Order Management

Data AnalyQcs As A Service (DAaaS

Geo-‐LocaQon as a Service

VerQcal Services

Pusng Big-‐Data to Work for Telcos Key Use Cases and Areas of ApplicaQon for Today’s Telcos


Data Sources

Data Systems

Data Access

Business AnalyQcs

Custom ApplicaQons

ExisQng Data

Databases

OperaQonal ApplicaQons

New Data

Tradi&onal Architectures Under Pressure

Limited Data Not efficient to keep exis&ng data, let alone handle new data sources. Time consuming to transform data for analysis in exis&ng systems.

Limited Insights Power users struggle with data.

Many users have no data.

Compliance and Privacy More data, more users, and more tools create complexity. Need to balance business agility with security and governance.


Data Sources

Data Systems

Data Access

Business AnalyQcs

Custom ApplicaQons

ExisQng Data

Databases

OperaQonal ApplicaQons

New Data

More Value from More Data for More Users, in Less Time

Keep Unlimited Data From disparate and limited views,

to unlimited informa&on access.

Unlock Value from Data From analy&cs for some, to

insights for all.

Manage Compliance From risk due to regula&ons and customer privacy concerns,

to trust in a secure and compliant plaGorm.

Enterprise Data Hub

Security and AdministraQon

Unlimited Storage

Process Discover Model Serve


Data Changes How We Work

Everything that can be measured will be measured.

Employees and customers expect more personal interacQons, but not at the cost of their privacy.

The most innovaQve companies embrace experimentaQon and agility.

InstrumentaQon ConsumerizaQon ExperimentaQon


SFR Telecom

Customer Spotlight


Create shared view into the customer journey •  Must collect data from >1B

events generated per day •  Shared view of data on

products, device usage, invoices, contracts, price plans, and call detail records

Cloudera EDH •  Real-‐Qme, self-‐service search,

reporQng, analysis •  Secure via Sentry

SoluQon

Customer Spotlight: SFR Telecom

Improved quality of support & network ops •  Berer customer experience

Challenge Benefit

“Instead of upgrading our DW environment every 3 years, the system will deliver opQmal performance for 8 or 9 years now.”


Mastercard

Customer Spotlight


Joint Customer Spotlight: MasterCard

Fraud costs credit card issuers approximately

$10 billion per year and is only detected at a

40% rate.

Most detecQon models are limited by the

amount of data that is available for analysis at

one Qme, which is constrained by extreme

cost.

Impala extends queries to data sets spanning mulQple years, not just the tradiQonal weeks and months.

SAS® Visual AnalyQcs and SAS Visual StaQsQcs. SAS/ACCESS

SoluQon

Move ETL and storage jobs to Hadoop, which cuts costs and Qmelines significantly.

More data is held in acQve archive, both in original

and digested formats, so it is available for future analysis.

Test new models using historic data on an ad hoc

basis using full, live data sets at zero marginal cost

Challenge Benefit

Test new models using historic data on an ad hoc basis using full, live data sets at zero marginal cost


MarkeQng

Problem

SoluQon

Partners

Next Best Offer Berer profile the customer and use collaboraQve and context-‐based filtering to offer the most appropriate product, product bundle, or offer at any given Qme.

Too Many Sources Disparate data is hard to correlate and analyze for sufficiently personalized product bundling, cross-‐sell, and up-‐sell opportuniQes served in real Qme.

Stream Processing Spark Streaming is used to calculate pricing occasions in real Qme based on live, unstructured data-‐in-‐moQon from the web, sensors, mobile devices, etc.

Use Case


Supply Chain

Problem

SoluQon

Partners

Event CorrelaQon to Store Traffic Model historical store-‐specific sales to event data (e.g., weather, disbursements, TV) to opQmize inventory, assortments, in-‐store merchandising, and staffing.

Can’t Scale Beyond Silos Current systems can not integrate social, telemetric, public, and log data in real Qme with historical data to predict sudden, temporary demand shixs.

Calculate Anything HBase is a real-‐Qme database accommodaQng complex historical data. Spark and Impala converge ETL, analyQcs, and reporQng for on-‐demand modeling.

Use Case


AutomoQve & Industrial

Problem

SoluQon

Partners

ProacQve Quality Assurance Build machine learning algorithms that idenQfy producQon anomalies prior to field tesQng and find performance flaws that could not be idenQfied in R&D.

Silos Limit OpQons Legacy systems hold historical data from producQon line telemetry, factory surveillance and sensors, call centers, in-‐car telemaQcs, etc. That data is useless if it is kept offline and in silos.

Anomaly DetecQon Spark includes MLLib, a library of machine learning algorithms for large data, enabling clustering to idenQfy outliers from typical producQon parerns.

Use Case


The Road to Success

Reference implementaQon to 3 sources, 5 transforms, 1 target Create, execute, test, and review a custom ingesQon/ETL plan

Apply SQL to much larger data sets with Impala, Hive, and Pig Master advanced techniques that boost Hadoop accessibility

Combine batch and stream processing with interacQve analysis OpQmize applicaQons for speed, ease of use, and sophisQcaQon

DescripQve AnalyQcs Pilot

Data Analyst Training

Spark Developer Training

Joint SAS & Cloudera Data ScienQst Training Class taught on SAS tools and SAS scripQng language running in the Cloudera Enterprise Data

Hub

Joint SAS & Cloudera Visual AnalyQcs Starter Package will allow you to get up and running on Visual AnalyQcs quickly

SAS & Cloudera Data ScienQst Class

Visual AnalyQcs Starter Bundle


Cloudera has trained over

40,000 people on Hadoop since

2009

Big Data professionals from

60% of the Fortune 100 have arended live Cloudera

training

Industry leading training and university program

Source: Fortune, “Fortune 500 “ and “Global 500,” May 2012.

Hadoop is at the heart of the big data movement. Nobody knows Hadoop like Cloudera. Visit the Cloudera booth for more informaQon.

[email protected]

financial,(telco,(retail,(&( manufacturing:(hadoop...

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