Delivering "Significant"Delivering Significant business improvementbusiness improvement

NA IVI Summit – March 11, 2014

Brief Introduction: BCG Presenter BiosBrief Introduction: BCG Presenter BiosInformation Management :

Big Data & Advanced AnalyticsTransformation /

Large Scale Change

Todd CurryPrincipal (Chicago)

Big Data & Advanced Analytics Large Scale Change

Renaud FagesPrincipal (New York)

• Todd is an Expert Principal in both Big Data and Advanced Analytics.

curry.todd@bcg.com

• Renaud is a Principal in BCG's New York Office.

• He is a core member of BCG's Financial

fages.renaud@bcg.com

• He is a core member of the Technology Advantage Practice, where he specializes in helping clients advance their analytical and computing capabilities, better define and price their products and services, and migrate effectively to cloud computing infrastructure.

Institutions and Operations practices.

• Renaud has deep experience in Large Scale transformation. Select experience include:– IT-centric operations transformation of large

Asset ManagerO ti d l t f ti f l di

• He spent his early years at BCG then spent the next 15 running technology-centric businesses in the Semiconductor (Intel), Financial Services (Household, HSBC), Online Travel (Orbitz) and Digital Advertising (Omnicom, IPG) industries before returning to BCG.

– Operating model transformation for leading North American wholesale bank

– Business model transformation of leading North American retail bank

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before returning to BCG.

Delivering "Significant" business improvement

Part 1 – Information Management (Big DataPart 1 Information Management (Big Data & Data Analytics)

Todd Curry

NA IVI Summit – March 11 2014NA IVI Summit March 11, 2014

AgendaAgendaShare our perspectives Big Data and Advanced Analytics

• How we think about it

• How it drives value• How it drives value

• Where IT-CMF fit is

• Vignettes v. Crystal ball

3

Big Data: A hot topic

Trends

Big data

100 100

Big data

Digital E

80

60

80

60Economy

Cloud computing

60

40

60

40computing

eCommerce20 20

4

2005 2007 2009 2011 20130 0

Four fundamental factors suggest that Big Data is realAn inflection point in broader arena Information Management and Analytical / Data Science

Opportunity CapabilityMarket

confidenceCost1 2 3 4

Vast amounts of data have been made

available

New technologies enable to analyse

data we could

Investors' confidence is strong,

the market grows

Economics of data analysis have fundamentally

• "90% of the world's data has been generated in the last

• Both structured and unstructured data can now be added

previously not rapidly

• The market (incl. HW, SW, Cloud and Services) has

• Data can be stored much more cheaply – 10-15x cost

changed

2 years"

• Petabytes entering vernacular; exabytesnot far away

and accessed easily

• Analysis can be done efficiently and quickly across

reached $15bn, growing at ~45% p.a.

• Investment in BIg

reduction per TB in 3 yrs

• And be processed much more quickly

5

distributed servers Data pure players grows at ~80% p.a.

– 50x reduction in cost

Opportunity: find the signals in a sea of data noise

1

noiseClient's event management

system was swamped by alerts!

• 75 million events/alerts per year

• More than 100 thousand incidents/ problems per year –incidents/ problems per year dealt with reactively

• Advanced analytics conducted on 100 GB of historical event/incident data

> 50% reduction in downtime worth $50M indowntime worth $50M in

productivity savings

6

Big data techniques predict incidents ~1 hour before downtime, allowing for proactive intervention before users are impacted

Capability: modern data architecture changes what is possible

2

Data generation was growing ~3x faster than storage capacity

Falling cloud storage costs offered that solution

0.10

$/Tb/month -89%

9515

Doubling time (months) for sequence data vsstorage per $1 spent

0.06

0.08

10Solution

was req'dto close

gap

0.02

0.04

10

5

5

0.00GlacierS3

T i l i bilit d f

0StorageNext Gen

Sequencing

7

Note: AWS S3 and Glacier storage costs current as of May 8, 2013Source:Stein Genome Biology 2010, 11:207, DSHR.org blog, AWS website,

Typical genome parsing capability moved up from 4 requests/sec to more than 60'000 requests/sec

Cost: economics of analytics have changed radically

3

radically

NASA D B L C iNASA

Employees: >18,000Budget: $17.8 billion (FY 2012)Time spent on SPE model: >40 years

Dr. Bruce L. Cragin

Employees: Himself Budget: $30,000 prize offered for best solutionTime spent on SPE model: ~200 hrs

VS

Best SPE prediction accuracy: 50% 4hr window Best SPE prediction accuracy: 75% 24hr window

NASA had been trying to develop an accurate predictive model for solar particle events (SPEs) i th 1960'since the 1960's

In 2010 they engaged InnoCentive, an open innovation intermediary, to publish the problem on the internet

Within 3 months they found a solution that was 50% better than their existing model at a cost of $30,000! (the prize)

8

Bruce's solution represented a major leap forward for NASA's programme for virtually no cost

Market confidence: investment growing; premium for BD

4

premium for BDCompanies invest in Big Data components

at an accelerated ratePE firms finance Big Data pure players

at an even stronger pace 1

30

$Bn

+44%Analytics

29.8

8 3

10

$Bn

20

44%

Cloud services

Implementation21.3

15.2

8.3

4 6

6

8

+81%

1010.0

Hardware

Software4.6

2.5

1.42

4

0

2015201420132012

0

2015201420132012

9

1 Excludes investments by non PE firms, e.g., Google's acquisition of Nest for $3.2bn

Source: Wikibon, Gartner, IDC, BCG analysis.

Rates of investment and market momentum rarely seen

Our view on big data goes beyond the traditional points and focuses on business model impact and value creationand focuses on business model impact and value creation

• X sell campaigns uptake rate up

BCG view of 'Big Data'

Value

Traditional view

• X-sell campaigns uptake rate up 400% when leading bank used advanced analytical behavioral scoring

• Telecom churn reduced by 30%

Value creation

Data

Telecom churn reduced by 30% through longitudinal analyses of client logs

• Bust-out fraud reduced by ~80% through advanced behavioral

AnalyticsTechnology

gpattern recognition

• Telematics data is reinventing car insurance through personalized pricing

Business model impactCapacity to :

• Oiling exploration & drilling cost reduced by 15-20% through analytical insights of seismic data

• Hospital equipment optimized

p y

• Processing large volumes of data efficiently and economically

• Discovering new behavioral patterns at extreme granularity ("segments of one")

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though better prediction of future cases

extreme granularity ( segments of one )

• Building connections between customer characteristics that seemed unrelated

How we look at Big DatagIT-CMF core to business model transformation and capability building

Industry

Bi D t Enablement

Industryview

Consumer/Retail

Energy FinancialInstitutions

Healthcare Industrial Goods

Insurance PublicSector

Technology, Media & Telecom

Big Data StrategyDeveloping an

overall approach to Big Data

EnablementHelping clients build core BD capabilities

Strategic AnalyticsGenerating new business

insights Personal Data & Trust

Platform AnalyticsImproving operational processes

to Big Data

Big Data TransformationTransforming and building new businesses using Big Data

Enterprise Information

Business Model

& Trust

Diagnostic & Roadmap

Strategy

Navigating Data Business Information

ManagementDriving better

decision making

TransformationTransforming

business models

Capability Builde.g. Organisation,

Culture, Technology, Ecosystems

Big Data opportunities

CreationCreating new

revenue streams

IVI Opp:

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IVI Opp: assessments &

deep dives

IVI Opportunity: Building Roadmaps

Delivering "Significant"Delivering Significant business improvement

f iPart 2 – TransformationRenaud FagesRenaud Fages

NA IVI Summit – March 11, 2014

The dark truth: most transformation failThe dark truth: most transformation fail70% of transformation are

deemed unsuccessful75% of transformation do not create value for shareholders

Success rate of 294 large-scale Europeantransformation projects (%)

100%

70%60%

80%

30%20%

40%

30%

0%Unsuccessful

programsSuccessful programs

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Transformations are usually a long and painful processprocess

Today 9 months 15 months 24 months

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3 core tenets that you need to get right3 core tenets that you need to get right

Funding the journey Winning in the medium term Right capabilities

What it k

g j y medium term g

takes

What questions must be answered

• What quick wins can I achieve?

• How can I free up the funding needed ?

• Where will our growth/ efficiency/ scale come from?

• What should our business

• Is my leadership team committed?

• Do I have the right people and capabilitiesfunding needed ?

• How can I keep my key stakeholders with me?

or operating model be?

• What targets should we set?

and capabilities

• How can I create a culture that sustains success?

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Source: BCG transformation initiative

Getting the right capabilities is critical: IT–CMF framework assess organization readiness

Executive Assessment

framework assess organization readinessExec. Assessment +

select deep dives

Use of capabilities to set roadmap

milestones

Measure on-going maturity of critical

capabilities

• Critical first step for business and IT to get on the same page (current state

• Select deep dives in prioritized opportunity areas to build capabilities

• Capability roadmap to include clear milestones to demonstrate

• Establish initial baseline for capability

p p

page (current state, target maturity)

• Executives (business and IT ) to agree

build capabilities

• Often conducted as cluster analysis for a select group of

demonstrate improvement

• Identify tangible measures of

• Measure progress on period basis focused on business value delivery

prioritization for capabilities needed to support transformation

capabilities

• Agree clear steps to achieve improvements

improvement

• Establish forward looking KPIs to test conditions for

• Capture sources of value delivery

• Establish factorsimprovements conditions for capability improvement

• Establish factors with high-correlation for value delivery

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Capability improvement should be tied to business improvements that are measureable

6 success factors for transformation6 success factors for transformation

Burning platform

Clear call for action/burning platform, grounded in long term strategyplatform term strategy

CEO/Management Committee mandating the initiative and actively engaged throughout

Leading from the top

Right people at the right place – internally or externally/with partners

Right capabilities

Continuous change management effort; over invest in keeping all parties alignedMindset shift

Persistent creative and collaborative problem solvingActivist/Smart

Deliver early results; demonstrate regular progressesWinningearly

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Persistent, creative and collaborative problem solving and innovationprogram

management

Wrap up: Transformations are long journeys with many ups & downswith many ups & downs

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