analytics innovation consortium: launch...
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Accenture AnalyticsAccenture and MIT Alliance in Business Analytics
Analytics Innovation Consortium: Launch Event
March 21, 2014
WelcomeBienvenidoBenvenuto
Céad míle fáilte
Copyright © 2014 Accenture All rights reserved. 3
The purpose of the Consortium is to provide a cross-industry forum focused on:• Expanding frontiers in the discovery and application of
business analytics to drive business outcomes
• Shaping future research projects for the Accenture and MIT Alliance in Business Analytics
• Defining and shaping the role of leaders in the emerging area of analytics
Analytics Innovation Consortium
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We are honored to be joined by top analytics executives from leading companies in this Consortium, to date.
Consortium Participation
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Launch Event
• Engage and collaborate in an innovative manner, with clear line of sight to cross-industry implications
• Discuss current research and x-industry application
• Gather input to shape upcoming research
• Identify and collaborate on analytics-specific themes for the Consortium to examine in 2014
Today’s Consortium Objectives
Timing Topic Facilitator8:00 – 8:30 Continental Breakfast
8:30 – 8:40 Morning Welcome David Simchi-LeviNarendra Mulani
8:40 – 9:15 Current Research Projects – Overview of Research PortfolioInsight to new Project Proposals
Andy FanoDavid Simchi-Levi
9:15 – 10:30 Research Spotlight:Linking Analytics to High Performance
Brian McCarthyDavid Simchi-LeviLynn LaFiandra
10:30 – 10:45 Break
10:45 – 11:45 Research Spotlight: Learning & Optimizing Revenue Management
David Simchi-Levi
11:45 – 12:15 Lunch
12:15 – 1:00 Analytics Theme Topic IntroductionBig Data in Enterprise: Machine to Machine
Brian McCarthyJohn WilliamsAndy Fano
1:00 – 1:45 Decision Science Brian McCarthyDavid Simchi-Levi
1:45 – 2:30 Digital Consumer Vivek Farias Paul Nunes
2:30 – 2:50 Shaping the Path Forward Brian McCarthyDavid Simchi-Levi
2:50 – 3:00 Closing Remarks David Simchi-LeviNarendra Mulani
Agenda of Today’s Proceedings
Agenda
Current Research Portfolio Andy FanoDavid Simchi-Levi
Research Project Spotlight:Linking Analytics to High Performance
Brian McCarthyDavid Simchi-LeviLynn La’Fiandra
Research Project Spotlight:Learning & Optimizing Revenue Management
David Simchi-Levi
Lunch
Big Data in Enterprise, Machine to Machine John WilliamsAndy Fano
Decision Science David Simchi-LeviBrian McCarthy
Digital Consumer Vivek FariasPaul Nunes
Shaping Future Research David Simchi-LeviBrian McCarthy
Reflections & Closing David Simchi-LeviNarendra Mulani
Accenture AnalyticsAccenture and MIT Alliance in Business Analytics
Research Overview
The Accenture and MIT Alliance in Business Analytics
Engaging companies in innovative analytics solutions
9Copyright © 2014 Accenture All rights reserved.
The Accenture and MIT Alliance in Business AnalyticsExecutive Summary• Established: December, 2012
• Strategic Intent: Accenture and MIT’s Operations Research Center will jointly develop solutions that can be applied to real-world, client specific issues through the development of new business analytics that bring together data, modeling and analysis to achieve a sustained quantum leap in business performance.
• Current Research Project Portfolio:
– 12 projects
– Cross-industry: Oil & Gas, Retail, Financial Services, Government
• Analytics Innovation Consortium
– Top-to-top Analytics Executive forum
– Launching in March at MIT
10Copyright © 2014 Accenture All rights reserved.
Current Analytics Research Projects Underway
Learning and Optimizing in Revenue Management
Improved Performance for Unconventional Drilling
Plant and Commercial Optimization
Reducing False Positives in Fraud Prevention and Detection
Behavior Data Integration and Offers Platform
Equipment Sensors, Process Maintenance & Optimization
Life Event Monitoring / Life Stage Needs
2014 Data Science Challenge
Social Media Causal Monitoring & Market Indices – Risk; Pricing
Link Analytics to High Improved Performance
Design of Urban Transportation Services *
Improved Mining Operational Performance *
* Planned to begin 201411Copyright © 2014 Accenture All rights reserved.
Principle Investigators
Reducing False Positives in Fraud Prevention and DetectionCity Government
John Williams MIT
Alan O’LoughlinAccenture
Provide improved algorithms and behavior models to identify hidden relationships between people, organizations and events; giving analysts a more holistic understanding of fraud and fraud patterns.
12Copyright © 2014 Accenture All rights reserved.
Principle Investigators
Francis O'Sullivan MIT
Improved Performance for Unconventional DrillingOil & Gas Company
Brian RichardsAccenture
Using analytics modeling to assimilate disparate data sources from rig, equipment, material movements and risk parameters to support more effective decision making during the execution of drilling programs.
13Copyright © 2014 Accenture All rights reserved.
Principle Investigators
Life Event MonitoringBanking Industry
Dimitris BertismasMIT
Marianne Seiler PhDAccenture
Identify which data and mechanisms can be used to increase predictability of significant events in the life of a small to medium sized business, enabling ability to predict services that can be offered to the business.
14Copyright © 2014 Accenture All rights reserved.
Principle Investigators
Plant and Commercial OptimizationRenewable Energy
Cynthia RudinMIT
Cristian CorbettiAccenture
Defining data model and algorithms, predictive maintenance capabilities and time-to-failure analytics models to prevent faults and optimize operations and maintenance strategy of the renewables fleet.
15Copyright © 2014 Accenture All rights reserved.
Principle Investigators
Saurabh Amin MIT
Plant and Commercial OptimizationNatural Resources
Fausto GarcíaMárquezAccenture
Predict gas turbine assets’ future health state, including the associated predictions of abnormal states and the remaining useful life under a projected operational context.
16Copyright © 2014 Accenture All rights reserved.
Principle Investigators
Learning and Optimizing Revenue ManagementOnline Retailer; Airline Carrier
David Simchi-Levi MIT
Matthew O’KaneAccenture
Analyzing customer and product level data to understand product and customer demand in order to improve revenue management through the combination of machine learning and optimization techniques.
17Copyright © 2014 Accenture All rights reserved.
Principle Investigators
Improved Mining Operational Performance Mining
Marcos FonsecaAccenture
Apply analytics to better enable diagnosis and actions on production bottlenecks, inventory levels, and performance enhancement levers in order to increase production throughput and reduce operational costs.
18Copyright © 2014 Accenture All rights reserved.
David Simchi-Levi MIT
Principle Investigators
Social Media Causal Monitoring & Market Indices – for RiskOpen Twitter Data
Tauhid ZamanMIT
Andrew FanoAccenture
Analyzing the duration and parameters of twitter activity around a company event to enable predictions that inform a company’s decision on how, if, when to respond.A
ge
New and going to be popular
Popular, but old
Unpopular
(Eventual) Popularity
Age
19Copyright © 2014 Accenture All rights reserved.
Principle Investigators
Social Media Causal Monitoring & Market Indices – for PricingOnline Retailer
Georgia PerakisMIT
Marjan BaghaieAccenture
Analyzing the digital trace of how social activities of customers are influencing the purchase behavior of online friends and how best to incentivize “influencers”.
20Copyright © 2014 Accenture All rights reserved.
Principle Investigators
Linking Analytics to High PerformanceSurvey of Cross-Industry Companies
Brian McCarthyAccenture
Analysis of the linkage between company overall performance and the maturity of the company’s analytics utilization.
21Copyright © 2014 Accenture All rights reserved.
David Simchi-Levi MIT
Principle Investigators
Carolina Osorio MIT
Design of Urban Transportation ServicesCity Transportation
Andrew FanoAccenture
Identify areas of transportation network inefficiencies and provide insights into types of services that would best meet travellers' needs at those critical locations.
22Copyright © 2014 Accenture All rights reserved.
Principle Investigators
Professor Marta Gonzalez, MIT
Behavior Data Integration and Offers PlatformOpen Data
Professor VivekFarias, MIT
Develop a real time recommender system based on people choice models, mobility, big data sets and advanced statistical models, for offering product recommendations based on destinations and predictable preferences.
23Copyright © 2014 Accenture All rights reserved.
2014 Data Science ChallengeCity of Chicago, MIT students, Accenture Employees
Challenge to develop the most innovative, intriguing, and impactful solutions or visualizations to help the city gain insights into its open data sets and identify opportunities to utilize city resources more effectively.
Challenge is open now through April 30, 2014.
24Copyright © 2014 Accenture All rights reserved.
New Research Projects in Proposal*
Early Warning Detection
Right-Sizing Capacity and Service Levels
Long Term Care Claim Forecasting
Market Prediction and Sustainability
Talent Skills Lifecycle Modeling
* For consideration for research beginning Fall, 2014
25Copyright © 2014 Accenture All rights reserved.
Holistic Risk Assessment
New ResearchProposal
Long Term Care Claim ForecastingInsurance
Develop new models to improve the forecasting of longer term disability claims. Analyze historical data and identify potential additional sources of data that can provide predictive value.
26Copyright © 2014 Accenture All rights reserved.
New ResearchProposal
Early Warning DetectionIndustrial Equipment
Reduce the cost of new product launches by enabling the identification of problems with new models earlier in their lifecycle. Analyze historical data from prior product launches.
27Copyright © 2014 Accenture All rights reserved.
New ResearchProposal
Talent Skills Lifecycle ModelingGlobal Consulting and Outsourcing
Enhance strategic HR decision making by developing predictive models for skills including demand, sourcing, and pricing.
28Copyright © 2014 Accenture All rights reserved.
New ResearchProposal
Right-Sizing Capacity and Service LevelsTelecommunications
Optimize service capacity based on an analysis of service disruption risks to users and cost to company.
29Copyright © 2014 Accenture All rights reserved.
New ResearchProposal
Market Prediction and SustainabilityOil and Gas
Use well data to develop models to predict the level of oil and gas activity, enabling optimization of resources to service oil field operations.
30Copyright © 2014 Accenture All rights reserved.
New ResearchProposal
Holistic Risk AssessmentInsurance
To what extent can examining risks across lines of business improve the prediction of risks within an line of business?
31Copyright © 2014 Accenture All rights reserved.
Research Submission and Approval Process
Initiate Concepts Refine and Propose2. Assess
and Prioritize3. Request
Project Plan 4. PAB reviews
Plans5. Launch Approved
Projects1. Receive Charters
Alliance Leads determine fit
Refine project details
Alliance Boardapproval
Prep for launch
Charter Proposals submitted
1 – 2 months (starting Jan & July) 1 - 2 months June/Sept & January
Agenda
Current Research Portfolio Andy FanoDavid Simchi-Levi
Research Project Spotlight:Linking Analytics to High Performance
Brian McCarthyDavid Simchi-LeviLynn La’Fiandra
Research Project Spotlight:Learning & Optimizing Revenue Management
David Simchi-Levi
Lunch
Big Data in Enterprise, Machine to Machine John WilliamsAndy Fano
Decision Science David Simchi-LeviBrian McCarthy
Digital Consumer Vivek FariasPaul Nunes
Shaping Future Research David Simchi-LeviBrian McCarthy
Reflections & Closing David Simchi-LeviNarendra Mulani
Accenture and MIT Alliance in Business Analytics
Linking Analytics to High Performance: Survey Results
Copyright © 2014 Accenture All rights reserved. 35
Agenda
• Key Themes in the Research
• Objectives of the Research
• High Performance Methodology
• Overall Results
• Industry Differences and Maturity
Linking Analytics to High Performance
Source: HP Analytics Study, October 2013
Copyright © 2014 Accenture All rights reserved. 36
Linking Analytics to High Performance
• Companies are embracing analytics and investing at various levels, but how they invest is the key to high performance
• Talent is a big part of the analytics machine and companies that actively manage and invest in their talent reap the benefits
• Technology plays an obvious key role with those leveraging the more advanced technologies available
• In the journey from dataanalysisinsightsdecisionsoutcomes, decisions -> outcomes is where the main difference is between High and Low Performers
Key Themes: The building blocks of High Performance Analytics
Investment Talent Technology Decision Making
Source: HP Analytics Study, October 2013
Copyright © 2014 Accenture All rights reserved. 37
Linking Analytics to High Performance
Research Objectives
Hypotheses
Method
H1: High performance is associated with companies having analytics as a key element of their strategy
H2: High performance is associated with more extensive and sophisticated analytical capabilities
H3: High performance is associated with enterprise-wide use of analytics
H4: High performance is associated with higher levels of investment in analytics capabilities
• Identify High Performance Businesses and their link to Analytics Performance and analytics best practices in a generalizable way using a large sample across performance levels and industries
• Report on what matters most to achieving HP via Analytics
• Survey of 864 analytics executives (global and cross-industry) regarding analytics practices, capabilities and performance
• Develop and estimator (HPBe) to estimate HPB using survey questions; use estimator HPBe to identify high performers
• Analyze HPBe classification and identity practices which correlate with High Performance
Copyright © 2014 Accenture All rights reserved. 38
This study was fielded – October 2013
Sample
Industry n=864Retail Banking/Wealth Management 123Communications 125Consumer Goods & Services 156Energy 112Health Providers 68Health Payers 22Insurance 106Retail 152
Sample Structure
Revenue n=864$250M-500M 145$500M-$1B 237$1B-$5B 217$5B-$10B 120Greater than $10B 145
Title n=864Chief Data Officer 158Chief Analytics Officer 199Director of Analytics 240Data Scientist 267
Headquarters n=864United Kingdom 94France 87Germany 98United States 116Canada 98Brazil 91China 96India 92Japan 92
Role n=864
Analyze, Generate Insight 315
Analyze, Generate & Use Insight 283
Lead Analytical Group 266
Manage/Analysis n=864Analysis 365Analysis & Group Management 384Group Management 115
Source: HP Analytics Study, October 2013
Copyright © 2014 Accenture All rights reserved. 39
94%
95%
92%
92%
95%
71%
69%
68%
70%
68%
Analytics helps businesses improve theeffectiveness of their decision making
Analytics is required to keep pace in my industry
Analytics improves businesses' ability to respondquickly and more proactively
Analytics is critical to achieving a competitiveadvantage
Analytics helps businesses be more agile
High
Low
Companies generally agree that analytics is important
Q1
Investment Talent Technology Decision Making
Perceived Importance of Analytics
Source: HP Analytics Study, October 2013 Base= High and Low Performers
Copyright © 2014 Accenture All rights reserved. 40
But investment in technology is a true demonstration of commitment
91%
91%
92%
84%
44%
43%
31%
39%
Analytics overall
Analytics as an enabler of businesscapabilities
Technology to support analytics
C-level Support
High
Low
Investment Talent Technology Decision Making
Level of Commitment to Analytics
Q2Source: HP Analytics Study, October 2013 Base= High and Low Performers
Copyright © 2014 Accenture All rights reserved. 41
14% 11%23% 23%
13%11%
43% 40%26%
19%
24%20%
48%59%
9%17%
Three YearsAgo
Today Three YearsAgo
Today
>25%
0-25%
Don'tknowDo notcollect
As High Performers continue to invest more in analytics capability, the gap between High and Low Performers will continue to widen
High Performers Low Performers
2%1%5%7%
43%39%
40%53%
9%
High Low
Significantlyincrease
Increasemodestly
Stay thesame
Decreasemodestly
Significantlydecrease
Investment Talent Technology Decision Making
Portion of technology expenditure made in analytics technology
Expected change in analytics investment over next three years
Q31Source: HP Analytics Study, October 2013 Base= High and Low Performers
Copyright © 2014 Accenture All rights reserved. 42
High performers manage talent from end-to-end
91%
84%
94%
88%
88%
87%
43%
46%
42%
34%
39%
36%
Training is used to keep the analyticsworkforce current and re-training is used rather
than hiring/firing
Analytics leadership encourages innovationand provides employees with opportunities to
share ideas
Performance rewards tie to both individualsuccess and enterprise profitability
Well-defined talent sourcing, selection andallocation strategy is in place for analytics
talent
Formal analytics competency model is in placedefining required skills, career levels and
appropriate curriculum
Global and local communities of practicesexist, effective at sharing knowledge (e.g.,informal networks of people with shared
interests)
High
Low
Investment Talent Technology Decision Making
Organizational capabilities supporting analytics
Q9Source: HP Analytics Study, October 2013 Base= High and Low Performers
Copyright © 2014 Accenture All rights reserved. 43
High performers take a multi-pronged talent sourcing strategy
72%
67%
63%
52%
43%
37%
54%
57%
33%
24%
18%
16%
Recruit people with the right skills aligned to ourneeds
Provide employee training
Partner with companies that bring the skills andcapabilities we need
Partner with academic institutions
Acquire companies that can bring the skills andcapabilities we need
Crowd source for solutions to specific problems
Partner and/or
Acquire80% (High)
vs (56% Low)
Investment Talent Technology Decision Making
Actions used to fill gaps in analytics talent
Q9Source: HP Analytics Study, October 2013 Base= High and Low Performers
Copyright © 2014 Accenture All rights reserved. 44
High performers leverage a wide variety of data sources
70%
65%
65%
66%
57%
55%
51%
48%
46%
45%
43%
38%
39%
48%
53%
48%
46%
39%
35%
31%
26%
23%
26%
21%
23%
22%
17%
32%
Transactional data managed by the IT function(ERP, CRM, etc.)
Corporate personal productivity tools(spreadsheets, documents, email)
Data about competitors
Data repositories, such as data from data marts,data warehouses, operational data stores
Governmental data (e.g., census data, Bureau ofLabor Statistics, weather, mapping)
Syndicated or other purchased 3rd party externaldata ( e.g., AC Nielson, FICO, and/or other…
Customer clickstream data
Data obtained through partner web sites
Customer call center verbatims
Machine/process control/sensor data
User generated social media content
Customer location data
Competitor web sites
Other economic data
High
Low
75%
59%
45%
21%
5 orMore
7 orMore
Investment Talent Technology Decision Making
Types of data used in analysis
Q4Source: HP Analytics Study, October 2013 Base= High and Low Performers
Copyright © 2014 Accenture All rights reserved. 45
High Performers are leveraging more sophisticated technologies and techniques
94%
69%
58%
88%
32%
30%
Basic Tools
IntermediateAnalytics
AdvancedAnalytics and
Big Data
High Low
Note: Basic (MS Excel, SQL); Intermediate (SPSS, SAS, KXEN); Advanced (Hadoop, Cassandra, in memory computing)
Note: Basic (Excel, basic statistics); Intermediate (Simple regression, data mining, cluster analysis); Advanced (Optimization, simulation, crowdsourcing, sentiment analysis)
Investment Talent Technology Decision Making
Tools and technologies used for analytics
Analytical techniques routinely used for analytics
Q6Source: HP Analytics Study, October 2013 Base= High and Low Performers
81%
67%
81%
72%
35%
45%
Basic AnalyticalTechniques(1 or more)
IntermediateAnalytical
Techniques(2 or more)
Advanced AnalyticalTechniques s(2 or more)
Copyright © 2014 Accenture All rights reserved. 46
Moving from insights to decisions is where High Performers outperform their counterparts
13%
20%
28%26%
14%
10%
22%
24%
36%
7%
Data Analysis Insights Decisions Outcomes
High
Low
Investment Talent Technology Decision Making
Point where analytics most frequently breaks down
Base= High and Low Performers
Q19Source: HP Analytics Study, October 2013
Copyright © 2014 Accenture All rights reserved. 47
High Performers are more than twice as likely to embed analytics in decision processes
84%
79%
81%
82%
75%
32%
34%
25%
32%
24%
Monitor decisions and course-correct to fix any problems (closed
loop)
Embed predictive analytics into keybusiness processes (e.g., predictingfraudulent claims before payment)
Foster a culture of experimentationand testing using analytics across
the business
Integrate external and internal datain a robust fashion to provide fact-
base for decision making
Empower decisions at lower levelsin the organization powered by
analytics
94%
92%
91%
89%
90%
87%
89%
44%
48%
46%
45%
39%
40%
37%
Analytics are being used toidentify growth opportunities
Analytics are expanding intostrategy and high-level
decision making
Our analytical capabilities area key element of our business
model and/or strategy
Analytics is central to ourcompany's products and
services
Most important decisions in mycompany are based on data
and analysis
Analytics are being drivencross-functionally into the
organization to deliver value
The C-level is aggressivelysetting and supporting the
analytics agenda
High
Low
Investment Talent Technology Decision Making
Base= High and Low Performers
Analytics Capability for Decision Making Embed Analytics in Decision Process
Q18Source: HP Analytics Study, October 2013
Copyright © 2014 Accenture All rights reserved. 48
The key difference between High and Low Performers lies in execution
58%
59%
50%
37%
54%
47%
33%
48%
42%
42%
31%
61%
44%
55%
55%
43%
46%
49%
44%
35%
37%
28%
Budget
Resource capacity
Internal resistance
Inability to change
Lacking systems and/or tools to implement
Functional silos
Politics
Lack of incentives
Personal risk
Lack of perceived authority
No burning platform
High PerformersRanked Top 5
Low PerformersRanked Top 5
Investment Talent Technology Decision Making
Base= High and Low Performers
Main reasons that inhibit implementation of a good decision
Q14Source: HP Analytics Study, October 2013
Copyright © 2014 Accenture All rights reserved. 49
The inability to implement a good decision is fundamentally a change management issue
Communications Consumer Goods Insurance Retail
Banking Energy Health Retail
Internal Resistance
Functional Silos
Politics
Lack of Incentives
Lacking systems and/or tools to implement
Budget
Personal risk
Resources Capacity
Inability to change
Lack of perceived authority
No Burning Platform
Investment Talent Technology Decision Making
Main reasons that inhibit implementation of a good decision
Copyright © 2014 Accenture All rights reserved. 50
The most successful companies have a centralized analytics function and analytics leadership is at the C-level
58%
58%
18%
12%
Analytic professionals are organized ina single centralized unit that setsanalytical direction for the entire
organization
Analytical leadership is at the C-levelof the organization, supporting
corporate innovation with analyticsworkforces dynamically deployed to
solve functional and businessproblems
High
Low
Analytics organization and operating model
Base= High and Low Performers
Q15Source: HP Analytics Study, October 2013
Copyright © 2014 Accenture All rights reserved. 51
High Performers are able to realize outcomes
• Relatively low levels of commitment to analytics• Talent is a challenge; less willing to use
unconventional methods• Challenged with interpreting analytical insights
due to a lack of visualization / interpretation tools• Inadvertently limit the use of analytics in the
decision process • Few receive a significant ROI from analytics
Low Performers
Focus on Data to Insights
Source: HP Analytics Study, October 2013
High Performers
Focus on Insights to Actions• Invest in their analytical capability and this is
expected to increase• Manage talent from end-to-end and source talent
using a multi-faceted approach• Have greater access to tools for visualization and
interpretation• Embed analytics into the decision process• Receive a significant ROI from analytics
90% or more High Performing companies are satisfied with the contribution analytics has made to financial performance, strategic direction, addressing growth opportunities, informing critical decisions and managing risk, compared with 39% of low performers (on average)
Copyright © 2014 Accenture All rights reserved. 52
Capabilities Retail Banking
Comms CG&S Energy Insurance Retail
The organization views analytics as fundamental to fact-based decision making…
Talent development is focused on ensuring Analytics professionals are proficient…
Highly engaged and effective analytics professionals are inspired by the organization
Analytic professionals have advanced/expert skills and a common methodology around deep statistical modeling…
Analytical leadership is at the C-level of the organization, supporting corporate innovation…
Leaders express, model and reinforce analytic behaviors and provide recognition for analytic achievers
Organization has a comprehensive analytics talent strategy for sourcing
People have insight into the key opportunities and challenges facing the company
Leaders use a structured process and a shared fact-base for decision making
Data for the purposes of decision making is embedded into the management processes
The organization has a robust data discovery capability
Tools are in the form of an efficient and interactive integrated suite
Rigorous analytic processes exist to perform root cause analyses
Analytics inform management decision-making in a systematic way
Speed of decision-making is a source of competitive advantage
…and specific analytics capabilities are particularly important by industry
Copyright © 2014 Accenture All rights reserved. 53
Industries demonstrating the highest maturity in analytics capabilities are Energy, Banking, CGS and Communications
71%67.80% 66.50% 65.70%
59.90%
43.50%39.90% 38.50%
0%
10%
20%
30%
40%
50%
60%
70%
80%
High Performer Cross-Capability Average
Source: HP Analytics Study, October 2013
Copyright © 2014 Accenture All rights reserved. 54
The gap between High and Low Performers is large and growing. For some industries, catching up will be a challenge
60%
54%49%
46%
38%
28%24%
0%
10%
20%
30%
40%
50%
60%
70%
Communication Energy Banking CG&S Retail HealthCare Insurance
Average Gap between High and Low Performers
Source: HP Analytics Study, October 2013
Average Industry Gap between High and Low Performers
Copyright © 2014 Accenture All rights reserved. 55
Our research shows that many companies use analytics and think it is important, but the High Performers are leveraging analytics in a more sophisticated way and reaping significantly greater benefits
Source: HP Analytics Study, October 2013
Hypotheses: High Performance is associated with…
Findings: High Performers…
H1: ...companies having analytics as a key element of their strategy
…are more likely to recognize the importance of analytics to competitiveness
…are more committed to analytics
H2: ...more extensive and sophisticated analytical capabilities
…leverage a wide variety of data sources …use more sophisticated technologies and
techniques
H3: ...enterprise-wide use of analytics
…more than twice as likely to embed analytics into decision processes
H4: …higher levels of investment in analytics capabilities
…invest more in analytics across capabilities, and this will only increase over time
Additional findings …have a centralized analytics function and c-level leadership
…navigate decisions to outcomes better …take a multi-pronged approach to talent
sourcing; manage talent from end-to-end
Copyright © 2014 Accenture All rights reserved. 56
Appendix
Source: HP Analytics Study, October 2013
Copyright © 2014 Accenture All rights reserved. 5757
High Performance measures are applicable across industries; industry specifics affect actual measures used
High PerformingBusiness
Positioning for the Future
Peer Set
5 Components of High PerformanceContinued performance overindustry eras and life cycles
Reliable and predictableperformance
Greater than expectedreturns from investments
Top linerevenue growth
Higher FutureValue (FV) Growth and Level
• 3 Yr. Avg. Spread• 7 Yr. Avg. Spread
• 3 Yr. Revenue Growth CAGR• 7 Yr Revenue Growth CAGR
• 7 Yr. Change in Relative Future Value• 7 Yr Level in Relative Future Value
• 10 Yr. Total Return to Shareholders CAGR• 7 Yr. Total Return to Shareholders CAGR• 5 Yr. Total Return to Shareholders CAGR• 3 Yr. Total Return to Shareholders CAGR
• 7 Yr. Median Outperf in Rev. Growth• 7 Yr. Median Outperf in Spread• 7 Yr. Median Outperf in Future Value
Copyright © 2014 Accenture All rights reserved. 58
Methodology
Linking Analytics to High Performance
Source: HP Analytics Study, October 2013
• How we built the estimator:
HPB=f(Analytics Performance)
– Training and Validation: Partition Dataset
– Developed equation for best estimator using multiple linear regression; Dependent variable = HPB index, Independent variable = analytics performance questions
– Tested equation against validation sample; Estimator proved valid
– HPBe: the resulting High Performance Business estimator provided a valid means to classify our sample of 864 into High, Medium and Low Performance Businesses
High Performers: Top 15 to 20% (by industry)
Low Performers: Bottom 25% (by industry)
Return on Analytics Investment
HighPerformance
(HPBe)
Use list of High/Low HPB companies; determine which companies are also in our dataset (n=864); 206 companies matched.
• We then looked at capabilities differentiating High and Low Performers
Copyright © 2014 Accenture All rights reserved. 59
Retail Banking/Wealth Management
79%
71%
75%
83%
83%
20%
10%
10%
13%
23%
Rigorous analytic processes exist to perform root causeanalyses that are embedded into key decision processes
Talent development is focused on ensuring Analyticsprofessionals are proficient in the quantitative disciplines
across
Analytical leadership is at the C-level of the organization,supporting corporate innovation with analytics workforces d
Organization has a comprehensive analytics talent strategy forsourcing, structuring and optimizing the investment in an
Leaders use a structured process and a shared fact-base fordecision making (data, analysis, and intuitive judgment)
High
Low
Copyright © 2014 Accenture All rights reserved. 60
Communications
80%
85%
80%
75%
13%
3%
0%
7%
The organization views analytics as fundamental to fact-baseddecision making
Talent development is focused on ensuring Analyticsprofessionals are proficient
Highly engaged and effective analytics professionals areinspired by the organization
Data for the purposes of decision making are embedded intothe management processes
High
Low
Copyright © 2014 Accenture All rights reserved. 61
Consumer Goods and Services
65%
68%
68%
74%
71%
10%
15%
15%
18%
18%
Data for the purposes of decision making are embeddedinto the management processes
Tools are in the form of an efficient and interactiveintegrated suite
Rigorous analytic processes exist to perform root causeanalyses
Analytics inform management decision-making in asystematic way
Speed of decision-making is a source of competitiveadvantage
High
Low