Delivering Significant business improvementbusiness ... · Exec. Assessment + select deep dives Use...
Transcript of Delivering Significant business improvementbusiness ... · Exec. Assessment + select deep dives Use...
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.
• Renaud is a Principal in BCG's New York Office.
• He is a core member of BCG's Financial
• 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
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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
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distributed servers Data pure players grows at ~80% p.a.
– 50x reduction in cost
Opportunity: find the signals in a sea of data noise
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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
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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
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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
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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)
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Bruce's solution represented a major leap forward for NASA's programme for virtually no cost
Market confidence: investment growing; premium for BD
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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
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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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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