analytics innovation consortium: launch...

Accenture AnalyticsAccenture and MIT Alliance in Business Analytics

Analytics Innovation Consortium: Launch Event

March 21, 2014

WelcomeBienvenidoBenvenuto

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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


We are honored to be joined by top analytics executives from leading companies in this Consortium, to date.

Consortium Participation


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


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.



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.



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.



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.



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.



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.



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.




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



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”.



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.




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.



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.


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.


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


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.



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.



Talent Skills Lifecycle ModelingGlobal Consulting and Outsourcing

Enhance strategic HR decision making by developing predictive models for skills including demand, sourcing, and pricing.



Right-Sizing Capacity and Service LevelsTelecommunications

Optimize service capacity based on an analysis of service disruption risks to users and cost to company.



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.



Holistic Risk AssessmentInsurance

To what extent can examining risks across lines of business improve the prediction of risks within an line of business?


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


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



• 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




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


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



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


Perceived Importance of Analytics

Source: HP Analytics Study, October 2013 Base= High and Low Performers


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


Level of Commitment to Analytics

Q2Source: HP Analytics Study, October 2013 Base= High and Low Performers


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


Portion of technology expenditure made in analytics technology

Expected change in analytics investment over next three years



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


Organizational capabilities supporting analytics



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)


Actions used to fill gaps in analytics talent



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


Types of data used in analysis



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)


Tools and technologies used for analytics

Analytical techniques routinely used for analytics


81%

67%

81%

72%

35%

45%

Basic AnalyticalTechniques(1 or more)

IntermediateAnalytical

Techniques(2 or more)

Advanced AnalyticalTechniques s(2 or more)


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


Point where analytics most frequently breaks down

Base= High and Low Performers

Q19Source: HP Analytics Study, October 2013


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



Analytics Capability for Decision Making Embed Analytics in Decision Process



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



Main reasons that inhibit implementation of a good decision



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


Main reasons that inhibit implementation of a good decision


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




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


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)


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


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



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


Average Industry Gap between High and Low Performers


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


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


Appendix



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


Methodology



• 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


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


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


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

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