Big Data: Essential Elements to a Successful · PDF fileBig Data: Essential Elements to ... Deloitte Consulting October 23, 2014. ... Business case built upon market sizing, share

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Big Data: Essential Elements to a Successful Modernization Strategy

Ashish VermaDirector, Deloitte Consulting Technology Information ManagementDeloitte Consulting

Big Data and its Implications

for Analytics

Ashish Verma, Director

Deloitte Consulting

October 23, 2014

Analytics and “Big Data”:Nothing new, totally revolutionary

4 Deloitte and SAS Big Data Breakout session Copyright © 2014 Deloitte Development LLC. All rights reserved.

The current “hype cycle” around Analytics and Big Data is booming,

driven by both user expectations and vendor hype.

Analytics and Big Data


With huge amounts of data generated per day coming from a variety of sources

capturing growing volumes of transaction detail about customers, suppliers and

operations

An on-going paradigm shift is transforming the landscape of companies

with data volumes growing exponentially…

Changing the status quo of structured information analysis to unstructured and semi structured data known as

Big Data Analytics

1. International Telecommunication Union 2. How much information? Peter Lyman and Hal Varian 3. Library of Congress Web Site

340 million

tweets generated

every day @Twitter 57% of all Tweets being generated in the US

6 billion

mobile phones34% of adults use tablets

1.2 billion Mobile web

subscribers

30 billion of content pieces shared every day 500TB

60 billion intelligent devices

200+ TBstored data in every sector


Talent

Data Growth

*IdeaWorksCompany Press Release, June 2013

Revenue

~81% in Big Data ecosystem software growth in

2013

650% or greater growth in enterprise data

between 2010–2014

39.4% compound annual growth rate in Big Data

technology and services in 2015

4.4M Big Data jobs will be in demand in 2015

$50 Billion or more revenue from Big Data by

2020

Which implies that Big Data cannot be ignored…

With the explosive growth of data in web, social media, and within organizations,

there is a vast, rich component of underutilized data sources that executives can

no longer ignore in determining the potential competitive advantages it can

provide

80% of new enterprise data between 2010–2014

will be unstructured


Big Data….and small

Analytics

= the “Asset”

= the “Action”

Value

Analytics is the application of a broad set of capabilities looking at data in

new and interesting ways with an eye towards driving value that you can

measure, and should measure in dollars.

Analytics and data — Big or “small”


Material players deliver within and across value chain

components and are expanding into analytics

Technology Incumbents

Analytics & Visualization

Re

pre

se

nta

tive

Pla

ye

r

Ma

rke

t S

ha

re

Go

-to

-ma

rke

t m

od

els

B2C New Entrants

Software Providers

Consulting

Sources: IDC, CIBC, Wikibon, Company websites, Press releases, Annual reports, Deloitte Analysis

Services Providers

Hardware Providers

Software Providers

Key

Data Technology Analytics

Software ServicesInfrastructure Data MgmtData Sources

Consumer and

Business

Applications

Market Research & BIS

40%

38%

22%

Market Share

Services

Hardware

Software

40% represents

$7.4 Billion USD

22% represents

$4.1 Billion USD

38% represents

$7.2 Billion USD

http://www.mu-sigma.com/index.html

http://www.mu-sigma.com/index.html

http://www.google.com/url?sa=i&source=images&cd=&cad=rja&docid=KTMNVpNYUwDlPM&tbnid=o78WRIXSFwEmKM:&ved=0CAgQjRwwAA&url=http://www.kurzweilai.net/kurzweil-joins-google-to-work-on-new-projects-involving-machine-learning-and-language-processing/google-logo-2&ei=pL0uUfa1I83uqAH-_ICQBw&psig=AFQjCNF4cTukmzoNCM51tgDuJid1WSswmA&ust=1362104100682682

http://www.google.com/url?sa=i&source=images&cd=&cad=rja&docid=KTMNVpNYUwDlPM&tbnid=o78WRIXSFwEmKM:&ved=0CAgQjRwwAA&url=http://www.kurzweilai.net/kurzweil-joins-google-to-work-on-new-projects-involving-machine-learning-and-language-processing/google-logo-2&ei=pL0uUfa1I83uqAH-_ICQBw&psig=AFQjCNF4cTukmzoNCM51tgDuJid1WSswmA&ust=1362104100682682

http://www.google.com/url?sa=i&rct=j&q=accenture+logo&source=images&cd=&cad=rja&docid=ViFTnbfqi6I49M&tbnid=w6pdMCyzk28gRM:&ved=0CAUQjRw&url=http://www.seeklogo.com/accenture-logo-2446.html&ei=F_U3Ud_IGcTR2QXFjYHwCA&bvm=bv.43287494,d.b2I&psig=AFQjCNFdxOdPjLVsyJX8pLe-6U2WbiPvHg&ust=1362708115289568

http://www.google.com/url?sa=i&rct=j&q=accenture+logo&source=images&cd=&cad=rja&docid=ViFTnbfqi6I49M&tbnid=w6pdMCyzk28gRM:&ved=0CAUQjRw&url=http://www.seeklogo.com/accenture-logo-2446.html&ei=F_U3Ud_IGcTR2QXFjYHwCA&bvm=bv.43287494,d.b2I&psig=AFQjCNFdxOdPjLVsyJX8pLe-6U2WbiPvHg&ust=1362708115289568


Business needs drive IT capability requirements

Structured

Structured

Semi-

Structured

Un-Structured

Low

High

Low

High

Low

High

Batch

Batch

Batch

Batch

Batch

Near Real Time

Near Real Time

Real Time

Near Real Time

Real Time

Near Real Time

Traditional Data Warehouse / Analytical

Applications

Massively Parallel Processing

Distributed Clusters

Massively Parallel

Processing

In-Memory

Appliances

Massively Parallel

ProcessingTraditional DW

Specialized Massively

Parallel Processing

Distributed

Clusters

Distributed Clusters

Specialized System

Technology

Massive Parallel Processing Vendor Snapshot

EMC GreenPlum

IBM Netezza

Teradata Aster

Oracle Exadata

Distributed Clusters Systems Vendor Snapshot

Hadoop MapReduce

Aster SQL-MapReduce LexisNexis HPCC SAP HANA

Oracle Exalytics

In-Memory Appliances Vendor Snapshot

Kognita

Ne

xt

Ge

ne

rati

on

EIM

& B

ig D

ata

Variety Volume Velocity

Tra

dit

ion

al

EIM

Process structured to un-structured data, low to high volumes, in near real time

Monetizing Big Data for a

Telecom Operator

Sanitized Client Example


Three phases to creating the Big Data business

Identify and Prioritize

Opportunity Areas

Determine Product

Platforms and High-Level

IT Capabilities Build out Detailed

Product and

IT Requirements

• Examine value chain structure,

competitors

• Identify current data assets and

applications

• Size market segments

• High-level comparison of assets

and use cases

• Conduct Deep Dive competitor

analysis

• Compare Client data and analysis

capabilities, identify differentiation

opportunities

• Refine business model elements

and assumptions

• Conduct concept and business

model validation

• Translate platform business

requirements into IT functional

requirements

• Conduct deep dive into IT

capabilities (current, planned)

• Assess IT capability gaps

• Develop organizational

development plan

• Develop detailed product “crunchy

questions” and business

requirements

• Interview customers for second-

level product validation

• Architect IT solution and develop

roadmap

• Engage alpha/beta prospective

partners


Key considerations for Client’s play in Big Data

• How does the strategy create synergies with existing ### assets: network, distribution channels?

• How can Client leverage its current assets and relationships to enter the Big Data space?

• What sequencing and plays would be appropriate given core assets and use cases already

funded?

• How does the strategy support other growth areas such as IoT, M2M and Security?

• Can Client competitively differentiate and assume a leadership position? How does the track

record support the estimated benefits?

Client could play across all the components of the Big Data

value chain but considerations should inform prioritization

Data Technology Analytics

Software ServicesInfrastructureData MgmtPlatformData Sources

Consumer and

Business

Applications

Infrastructure ($X-Y B)

Advertising/ Mktg

Services ($X-Y B)

Consumer Finished

Apps & Services ($X-Y B)

Data Analytics

Services ($X-Y B)

Internal Process Improvements ($X-Y B)

Data Insights

and Monetization

Data Insights and

Monetization ($X-Y B)

External Opportunity

Internal Opportunity

Key

1

0

2

3

4

5


For the most attractive macro

opportunities, Deloitte identified more

detailed business opportunities

Analysis of these opportunities identified

key capabilities that the Client would

need to either integrate internally, or

source from external partners

Based on this assessment and fit with

the client’s capabilities, certain business

opportunities were prioritized for deeper

business case, product, and technology

analysis

In addition to market size, the team considered the

importance of capabilities to each macro opportunity


Product development approach is informed by customer

needs, competitor offerings, and Client’s strength

High value potential customer segments

Marketing intelligence needs of the

customers

Competitor offerings

Competitor

capabilities and

strengths

Competitor

weaknesses

Under/unmet

customer needs

Client’s unique assets

Client’s product

offerings for solving

the unmet needs

Winning Market

Offerings

CUSTOMERSWhat Problem

are we trying

to solve?

COMPETITORSHow are those

problems being

solved now?

COMPANYWhat can Client

do to solve the

problems better?


Three segments show higher propensity for Big Data driven

insights

HighLow

Po

ten

tia

l fo

r U

se

of

Big

Data

1

Notes:

1. Based on velocity, variety and volume of data generated in the industry and its relevance for consumer insights

2. Based on top 100 companies in the US by advertising dollar spend

Source: Gartner, AdAge, Deloitte analysis

Consumer Products

Automotive

Retail, Wholesale & Dist

Pharmaceuticals

Media

Telecomunications

Banking & Securities

Technology

Tourism, Hospitality & Leisure

Insurance

Industrial Prod & ServicesProcess Industry

Public Sector

Med

Hig

h

Wave 2

Wave 1

Relative Marketing and Advertising Spend2


Competitors differentiate on a limited set of individual data

points

Little to no

differentiation

Significant

Differentiation

Market

Leader

Full-service Digital Social/Mobile Others

Data Elements

Com

pa

ny A

Com

pa

ny B

Com

pa

ny C

Com

pa

ny D

Com

pa

ny E

Com

pa

ny F

Com

pa

ny G

Com

pa

ny H

Com

pa

ny I

Com

pa

ny J

Com

pa

ny K

Com

pa

ny

L

Com

pa

ny M

Com

pa

ny N

Com

pa

ny O

Cu

sto

me

r B

eh

avio

r

1. Transactional (PoS etc.)

2. Behavioral and attitude

3. Social influence

4. Decision path

Au

die

nc

e

Me

as

ure

me

nt

5. Audience tracking

6. Digital usage

7.Mobile (device/app/network)

8. Call , video, text tracking

De

mo

gra

ph

ic

9. Demographic/Lifestyle

10. Customer ID

11. Location

12. Financial (credit, income, etc.)

Some

differentiation

Most competitors

derive their strength

from generation of or

access to 1-3

distinctive data sources

Many competitors are

procuring data, or

purchasing

competitors, to keep up

with marketplace

changes

Location-based data is

a relatively new area;

however, many

competitors – both

large and small – are

pursuing opportunities

in this space


Competitor data limitations create opportunity for OperatorsL

oca

tio

n I

ns

igh

tsA

ud

ien

ce

Me

as

ure

men

tS

oc

ial In

tell

ige

nc

e

Static location information,

updated once or twice a

year

Home address (Zip+4)

most common form of

available location

information

Limited ability to track real

time location

Fragmented view of

customer across screens

(TV, internet, and mobile)

Based on panel data

Limited visibility into mobile

internet and application

usage

Ability to supplement

online social media

information with voice and

text usage

Can be a better predictor

of who can influence

whom

Integrated view of customer

across multiple screens

Based on linked users

across TV, internet & mobile

for triple play subscribers

Mobile internet and

application usage visibility into

mobile subscriber base

Dynamic location,

available approximately

every 15 minutes

Location tracked within a

radius of ≤X m; could be

less with WiFi

Real time location (home,

office, shop, cafe, road,

social venues, etc.)

Visibility in communication

patterns limited to online

social media

Network map does not

necessarily identify

influencers or active users

Competitor Limitations Operator Advantages


Operator advantages in data

and capabilities pointed to

differentiated product

platforms, anchored in how to

solve real client problems in a

differentiated offering

High-level requirements were

defined for data, integration,

analytics, and delivery model

dimensions

Operator advantages were turned into three product platforms


Team identified current market

offerings and customer use

cases

Based on Client’s competitive

strengths, opportunities to

bring a differentiated solution

were identified (3 major

platform areas, each with

multiple initial product

concepts)

Team developed storyboards,

tested with Deloitte Subject-

Matter Advisors and selected

CPG and Retail client

executives

Based on feedback, team

pruned less powerful concepts

and built out more detailed

product requirements for the

remainder

Multiple product/service areas for each platform area were

identified and screened


Business case built upon market sizing, share projections,

and preliminary business model


Starting point: Big Data reference architectureD

ata

Pat

hA

no

nym

ize

d &

Agg

rega

ted

Re

al T

ime

an

d B

atch

De

loit

te P

rop

rie

tary

an

d C

on

fid

en

tia

l


Data Sourcing Visualization

Data

Rep

lic

ati

on

Analytics

Geographic

Web Logs

Social Media

Others

Internal

External

Data Preparation / CalculationData Storage

Batch

Streaming

Hadoop Clusters /

Relational

Aggregation/

Query

All Major Databases

Flat files and Reports

Customized

Dashboards

and APIs

Apache Kafka

Scala/MapR

Analytics and

DiscoveryNo SQL/

Columnar

Hive QL / SQL

Visualization

and Discovery

Data At Rest Path

Data In Motion Path

Deloitte Pre-built Big Data Ecosystem

About Deloitte

Deloitte refers to one or more of Deloitte Touche Tohmatsu Limited, a UK private company limited by guarantee (“DTTL”), its network of member firms, and their related entities.

DTTL and each of its member firms are legally separate and independent entities. DTTL (also referred to as “Deloitte Global”) does not provide services to clients. Please see

www.deloitte.com/about for a detailed description of DTTL and its member firms. Please see www.deloitte.com/us/about for a detailed description of the legal structure of Deloitte

LLP and its subsidiaries. Certain services may not be available to attest clients under the rules and regulations of public accounting.

Copyright © 2014 Deloitte Development LLC. All rights reserved.

Member of Deloitte Touche Tohmatsu Limited

http://www.deloitte.com/about

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Big Data: Essential Elements to a Successful · PDF fileBig Data: Essential Elements to ... Deloitte Consulting October 23, 2014. ... Business case built upon market sizing, share