Big Data: Real-life examples of Business Value Generation with Cloudera

Big Data: Real-life examples of Business Value Generation

Richard Brown Global Programme Leader Big Data & Analytics

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Copyright © 2014 Capgemini. All rights reserved.

Big Data: Real-life Examples of Business Value Generation | Richard Brown

Over 40% of the companies that were in the

fortune 500 in 2000 were not there in 2010

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Big Data: A confluence of Sources and Technology

New

Opportunities

New

Sourc

es

Public

Social

Geo RFID

Meters

Mobile

Web

New

Technolo

gie

s

Sensors

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Improving Revenue Existing products & services

Data as a Revenue

Stream

Improving Revenue New products and different

markets

4 Ways in which the opportunity is being exploited

Cost Reduction

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US Retail Chain

Hadoop: Data storage cost down from $30k/Tb $1k/Tb.

The Situation

Massive EDW

4000 stores

650m SKUs

50 Tb per day

The Solution

440 node Hadoop

cluster

POS, Inventory, Social

Data optimized

across EDW &HDFS.

Max use of Opensource

The Benefits

$23m saving p.a.

Higher level of

forecast accuracy

Improved collaboration

with vendors

Improved data integrity

Cost

Reduction

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60% of Hadoop implementations are for new business applications

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Insight at the point of action

Online enquiry

Service B

Product A Product selected

Up-sell

x-sell

Customer

Sales history

Web

transaction

Success rate from 5% → 20%

Stock out

in store

Source D

Source C Sources

indentified Supply

Available

stock

Delivery lead

times

On-shelf Availability up 7%

Product in transit

– RFID location

Model of

optimum revenue

for replenishment

scenarios

Shipping

partner:

Route data

Current

performance

of supply

routes

Stores: live sales

performance

Competitor sales

data in locality

from 3rd party

provider

Recent social

media activity

Success rate for

product in x-sell

scenario

Customer web

profile data

Predictive model

of customer

response

Available

Products

Recent call

history:

Sentiment data

Existing

Revenue

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Global Media & Entertainment company Existing

Revenue

Rapid correlation of social media and operational data

The Ask

How to improve box

office takings?

The Solution

Social media analysis

• Sentiment

• Mentions

Theatre counts

Key influencers

Format release

analysis

The Benefits

Improved release

schedules

Improved marketing

effectiveness

Rapid response to

market changes

Improved yields

($ m)

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$ 90 Billion: Global Pharmaceutical Promotion Spend in 2012*

$19.7 Billion: Pharmaceutical Promotion Spend in 2012 in European Union (EU5).**

European Pharmaceutical

*Source: Cegedim Strategic Data, “2012 Pharmaceutical Promotional Spending: Global Trends”.

** EU top 5 = France, Germany, Italy, Spain, United Kingdom.

Physicians are key opinion leaders on drug

selection

Selection of right Opinion Leaders can allow

more effective targeting of marketing funds

More effective identification of Opinion

leaders can deliver significant ROI.

Emerging Trends in European Pharma Market.

As Pharmaceutical promotional spends shrink (EU5 -3.6% in 2012 *) and sales force is reduced, effective

identification segmentation and targeting of doctors becomes important.

1-to-1

Physician

interaction

(Detailing)

Direct to

Customer

Existing

Revenue

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Opinion Leader Identification Process

Network Influence

Score gives measure of

Influence in the Referral

Network of doctors.

Publication Score gives

measure of Influence in

Publication activities.

Prescription Score gives

measure of Influence from

drugs’ Prescription activities.

Affiliation Perception

Score gives measure of

perception based on

affiliations to Prestigious

Hospitals & Academic

Institutes.

Social Media Score gives measure of influence in

social media, blogs etc.

Opinion Leaders

Prescription Data

Publication Data

Referral Data

Survey Data

Social Media Data

Affiliation Data

Existing

Revenue

Referral Data of doctors.

Prescriptions Data.

3rd Party Survey Data.

Hospitals, Institutes &

Academic Institutes

affiliation Data.

PubMed Data for medical

publications; clinical trials,

research papers, news

etc.

Pharma blogs, forums

and other social

networking sites.

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“Shifting sands”

In today’s information

landscape new islands

of value are appearing

across organisation &

industry boundaries

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1234 123456 12345

Banks owning data on consumer spend behaviours –

moving to sell data to Retailers and Consumer Products

companies

Insurers owning data on car, house... ownership and getting

into services around this - becoming Estate Agents

Telco providers holding information on retail activity

– becoming retailers

Consumer products companies gaining consumer data

through social media to drive connection through on-line

channels, bypassing Retailers

Utilities owning data about households and selling services

around the house – eg security, insurance, maintenance..

Changing Industry...

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Global Telecommunications Provider

The Ask

Traffic volumes

Plan the network

Data to help

negotiate with

partners

The Solution

Socio-thermodynamic

modeling

Network modeling

Fault and fraud

detection

Traffic imbalance

analysis

Opening up new revenue streams

The Benefits

Major cost saving

Increase customer

service / response to

issues

Identified new

business opportunities:

• New markets

• New customer

.

New

Revenue

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Global HR / Employment Consultancy

The Ask

Large volume of

CVs

Large volume of

vacancies

Need to rapidly and

accurately match

The Solution

A learning algorithm

processes the text

within CVs and jobs

Vectorization of

documents

Automatic matching

by geo-location

Visualization of results

Users have been amazed by the accuracy of the system

The Benefits

Better market visibility

Improved role matching

200,000 documents

processed in a 2 hours

15 new business

applications

Advisory service for

employers relocating

.

New

Revenue

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Data Monetization Data

Monetization

“Data is the new oil.”

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Public Sector

Fraud Detection

Crime Prevention

Service Optimization

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How are organisations getting there?

Dig

ital C

apabili

ty

Leadership Capability

The How:

Successful transformations depend as much

on how firms manage digital transformation than solely on

implementing new technologies

The What:

Using digital technology to transform the customer

experience, operational processes and business models

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How are organisations getting there?

Dig

ital C

apabili

ty


DIGITAL MASTERS FASHIONISTAS

CONSERVATIVES BEGINNERS

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Revenue Generation

Dig

ital C

apabili

ty


Revenue Generation

+9%

-10% -4%

+6%

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Profitability

Dig

ital C

apabili

ty


Profitability

+26%

+9% -24%

-11%

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It is not from the edge You need Vision, Strategy

and Business & IT working together

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Cloudera Enterprise Data Hub Next Generation Data Management Platform

Open, agile and reliable platform Open source and enterprise-grade, constantly enhanced. Agility to handle more types

of data without defining structure upfront. Focus on performance and security.

Mixed workload integration Supports and manages different types of workloads for different types of analytics and

users population (Batch processing, Interactive SQL, Search, Machine Learning, In

memory processing)

Easiness of integration CDH certified with all major technologies our customers have already (Informatica,

SAS, Tableau, SAP BOBJ, Microstrategy, Talend, Teradata, etc.) and new

technologies (Revolution Analytics, Splunk’s Hunk, etc;). Cloudera can be an “add-on”

to existing architectures to enhance scalability and analytical features.

Scalable and Cloud “Infinite” Storage capacity as the cost is an order of magnitude lower than traditional

DB. Choice to deploy on-prem as well as in the Cloud (eg. Elastic Analytics).

An open

solution for

Big Data

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Enterprise Data Hub Accelerator Run

Transform

Scale

Initiate

Init

iate

Scale

Tra

nsfo

rm

Ru

n

Business

Drivers

Governance

Platform

Data

Analytics

1st tactical wins

3-6m execution

time

Set up 1st

business driver.

Core team and

core agile

processes

initialized

Kick start

communication.

Adapt current

analytics

processes to

new platform.

Core data blocks

initialized, Initiate

MDM, DQ, Data

Privacy processes.

Set up first

data platform

and learn.

Expand 1st wins

Execute on 1st

business driver

Build 9-12m plan.

Industrialization

& data exploration

processes set up

Increase comm.

on successes.

Keeping same

analytical algorithms,

expend depth, scope

and granularity

of datasets.

Extend scope of

data to peripheral

data and external

data.

Scale to

enterprise

industrialized

deployment.

Set up strategic

drivers needed

for Corporate

Development and

execute.

Broaden

governance to

operational

processes to drive

transformation.

Use new types of

analytics (machine

learning, discovery

algorithms,

scientific algorithms).

Increase

acquisition rhythm,

eg. leverage real

time streaming.

Connect to

operational

mission critical

systems.

Fuel innovation

roadmap with

quarterly updates

on business

drivers priorities.

Iterative

continuous

improvement

and innovation.

Continuously

refine models

Iteratively test out

new algorithms

and new data.

Continuous

improvement

on data

provisioning/data

management.

Continuous

platform

optimization

(TCO, overall

efficiency).

The information contained in this presentation is proprietary.


Rightshore® is a trademark belonging to Capgemini.

www.capgemini.com/bim

About Capgemini

With almost 140,000 people in over 40 countries, Capgemini is

one of the world's foremost providers of consulting, technology

and outsourcing services. The Group reported 2013 global

revenues of EUR 10.1 billion.

Together with its clients, Capgemini creates and delivers

business and technology solutions that fit their needs and drive

the results they want. A deeply multicultural organization,

Capgemini has developed its own way of working, the

Collaborative Business Experience™, and draws on Rightshore®,

its worldwide delivery model.

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