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Making the Data Work for You
ACI-NA

San Jose, September 2013

This document is confidential and is intended solely for
us...
‘Big Data’ is the new paradigm of handling and analyzing data –
but what does it all mean?
What is Big Data?

Analytical C...
We are typically confronted with a common set of “Big Data”
related questions from senior executives
Primary Client Questi...
1 Business Opportunity

Many successful companies have already found ways to monetize
data
Data Monetization

Monetization...
1 Business Opportunity

Big Data applications currently being used in different industries
could have direct applicability...
1 Business Opportunity

Big Data also offers a broad range of commercial and operational
opportunities for airline partner...
2 New Capabilities

Pursuing Big Data involves developing a more sophisticated
approach to analytical techniques than we’v...
2 New Capabilities

Achieving this new level of “sophistication” requires a focus on
five capabilities
Success factors
Pit...
2 New Capabilities

These new capabilities require both technical and strategic skills,
some of which may need to be sourc...
3 Get Started

Many companies have already embarked on Big Data programs,
even as they continue to enhance existing capabi...
3 Get Started

We typically see a three step process to achieve Big Data Maturity,
each with unique characteristics
Big Da...
3 Get Started

Getting started involves first defining the opportunity followed by
a pilot
Booz & Company Big Data Approac...
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Booz&Co Big Data 2013

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Transcript of "Booz&Co Big Data 2013"

  1. 1. Making the Data Work for You ACI-NA San Jose, September 2013 This document is confidential and is intended solely for use at the 2013 ACI-NA Conference
  2. 2. ‘Big Data’ is the new paradigm of handling and analyzing data – but what does it all mean? What is Big Data? Analytical Capabilities Evolution Big Data Google+ Velocity Facebook Mobile Phone / GPS Blogs Pinterest Twitter Ad Server Data Purchase History Life Event Records Analytical Factory Physician Demographics Claims Data by Members Batch “Big Data is about new methods and technologies to handle and analyze in a highly scalable manner, polystructured data streams, with increasing volume, variety (e.g. structured, unstructured data) and velocity (e.g. in real-time)” Realtime Public Surveys Claims History Experian Health Acxiom DWH Provider Visits by Member Life Event Data Demographic Data Dependents Pricing Data by Procedure Structured Personal Life Events iTriage Online Forums Customer Surveys / Feedback SharePoint Chronic Health Condition Operations Interactions Variety and Volume Images / Video Jawbone External Sensor Data Web Feeds Customer call records Click Streams Market research Web Profiles Care Management Program Data Unstructured Source: BARC / Booz & Company Booz & Company 1
  3. 3. We are typically confronted with a common set of “Big Data” related questions from senior executives Primary Client Questions 1  2  Areas for Discussion Today What is the business opportunity from Big Data?  Managing operating costs Which new capabilities are required?  Strategic Focus  Leadership  Prioritization  Governance  Avoiding potential disruptions  Improving passenger experience  Data Analytics 3  How do I get started?  Business Case  Pilot  Scale-up Booz & Company 2
  4. 4. 1 Business Opportunity Many successful companies have already found ways to monetize data Data Monetization Monetization With Data Aggregate & package data for the purposes of reselling or core product enhancement Leverage data and associated assets & capabilities to create new business Enhance Quality of Information  Real time tracking & routing value added services Generate Insights  Analytical services to sellers  Personalized offers White Label Capabilities & Infrastructure  Customer acquisition & marketing campaigns consulting services  Capacity and know-how outsourcing Create New Data  Driving habits dependent pricing  Warehouse optimization services Create New Products  Market & product information services  Macro economic index based on transactional data Booz & Company 3
  5. 5. 1 Business Opportunity Big Data applications currently being used in different industries could have direct applicability to the airport environment Staff Roster Optimization Threat Identification Pre-emptive maintenance Situation/ problem  Under-staffing clinicians jeopardizes patient care while over-staffing increases costs Solution  Analyzed historical trends to identify patterns in emergency room visits  Combined with external data available in local hospital communities  Enabled to better match supply of doctors and nurses with “demand” Situation/ problem  Disruption of critical infrastructure (protests/attacks) has a significant impact on national defense  Predicting attack timing/location can help target preventative measures Solution  Draws in feeds from news wires, social media and geo-spatial satellites  Combines with proprietary intelligence to create a fuller picture of situation in real-time Situation/ problem  Requirement to increase maintenance efficiency of industrial facilities Solution  Interconnection of industrial equipment to collect data from sensors monitoring machine conditions in real time  Automated review and analysis to pick up any patterns that indicate and detect any possible failures  Preemptive planning/ scheduling of maintenance points Lowered costs while maintaining level of service More accurate identification of nature/size of threats Reduction of downtimes and hence increase of productivity Janitorial / maintenance staffing Customer service staffing Asset Security Weather concerns Ground handling equipment Baggage handling equipment Potential Airport Applications Booz & Company 4
  6. 6. 1 Business Opportunity Big Data also offers a broad range of commercial and operational opportunities for airline partners Example of Big Data Driving Value in Transport BIG DATA real-time / unstructured Commercial Processes Operational Processes VARIETY VELOCITY  Personalized marketing based on deeper and broader customer insights (e.g. from social media)  Real-time / Predictive Disruption and Punctuality modeling based on internal & external data sources  Real-time, context-aware offering, pricing and upselling, (e.g. based on geo location data)  Real-time monitoring of operations/network performance (eg. based on sensor data) with proactive management of potential disruptions  Early warning of customers of on potential travel disruptions (48-72h before trip), based on predictive modeling of common travel patterns, weather, etc  Optimized Yield Management based on improved demand modeling and real-time capacity utilization  Sale of customer insights to third parties based on usage data to shops, media agencies, etc. TRAD. DWH batch / stuctured Booz & Company  Customer segmentation based on historic, aggregated data with segmented offers  Static campaigns, agnostic of individual customer interaction  Fuel Consumption Optimization based on engine sensor data and weather forecasts  Fraud / Theft detection in supply chain  Pro-active Maintenance  Backward oriented analysis of operations performance and quality to further optimize operations 5
  7. 7. 2 New Capabilities Pursuing Big Data involves developing a more sophisticated approach to analytical techniques than we’ve seen in the past A Brief History: Data  IBS  Analytics  ‘Big Data’ Analytics What’s the next breakthrough? 1970s Predictive Modeling Degree of sophistication Foresight How can we improve impact now? Forecasting / Extrapolation How will customer preferences evolve? Statistical Analysis What explains customer behavior? 1972:Consumer credit bureaus gain traction in US and UK 1975:Oil and gas prospectors start digitizing analog data 1977:Intel 8085 (first mass produced digital microprocessor) launched Alerts What actions are needed? Drill Down / Slice & Dice Which customers, when? Ad Hoc Reports How many, how often, where? Standard Reports What happened? 1990s 2000s 2010 onwards 2004:Exxon-Mobil seismic data exceeds 1 Petabyte 2005:Capital One enters top-5 US card issuer league 2008:Google, Facebook, Amazon popularize noSQL, OpenCompute, AWS cloud storage 1992:Capital One applies Information Based Strategy (IBS) to credit cards 1994:Silicon Graphics introduces terabyte storage 1997:Amex hires predictive modelers 1998:Google founded to “organize the world’s information, and make it accessible” 2010:‘Big Data’ gets its own page on Wikipedia 2011:Wearable medical devices proliferate 2012:Palantir gets valued at $2.5Bn Access & Reporting Booz & Company 6
  8. 8. 2 New Capabilities Achieving this new level of “sophistication” requires a focus on five capabilities Success factors Pitfalls 1 Strategic Focus  Big Data Innovation agenda aligned to clearly articulated strategic goals  Ideation driven around high value “themes”  Deep understanding of consumer needs & value drivers 2 Enterprise Demand and Prioritization 3 Enabling IT and Data Capabilities  Staged venture-like approach for project selection prioritization and funding  Business case “light” for new ideas  Balanced idea and new product portfolio  Matching front-end Big Data innovation with backend capabilities  Establishing strong core Big Data team with deep analytics and data-mining capabilities  Agile architecture  Decision makers have to move from intuitive to more fact-based decision making 4 Leadership and Talent 5 Booz & Company Governance and Decision Rights  Fresh talent required to leverage insights from data (e.g. data mining, predictive modelling)  Linkage to business unit process with clear ownership and decision rights  Lack of strategic focus in building pipeline  Failing to co-develop agenda with businesses  Failure to capture/ integrate consumer and competitive insights – incl. digital evolution  Unstructured ideation that lacks focus  Funding too many ideas without carry-through capacity  Risk aversion to big bets  Business case rigor kills ideas  Incompatibility between early stage innovation priorities and broader enterprise strategy  Not working to a joint vision and roadmap with business areas  Insufficient focus on “Little I” innovation platform improvement  Taking a “if we build they will come” approach  Multi-year “technology led” implementations  Failure to identify and develop tailored career models for difference-making positions  Tendency to try to use resources on hand instead of refreshing the talent base  Disconnection of innovation function from the business results in weak buy-in  Agile processes and ability to rapidly prototype  Failure to leverage external partners limits results  Requires substantial adaptation of processes and KPI  Unclear definition of decision ownership and accountability results in unclear priorities 7
  9. 9. 2 New Capabilities These new capabilities require both technical and strategic skills, some of which may need to be sourced externally NOT EXHAUSTIVE Roles Skills & Competencies Data Scientist  Leverage extensive statistical skills and business knowledge to transform large amounts of unstructured and structured data into business-ready data visualizations and models Strategic Data Management  Defines strategic priorities for management and delivery of data throughout the enterprise Business SME Potential Talent Source IT Operations Business Market       Provide extensive knowledge on business strategy, operations, competitive landscape, market conditions and emerging trends, customer demographics and both macro- and microeconomic factors   Functional Expert  Utilize expertise in functional and business areas to provide recommendations for useful analysis and insights that Big Data could support; ensure the integration of Big Data in processes   Data Integration Developers  Design and develop integration processes to handle the full spectrum of data structures in a big data environment   Data Architect  Design the data architecture, guide its development leveraing IT skills in data modeling, data profiling, data quality, data integration and data governance.   BI Developers  Build the required dashboards, reports, visualizations and templates for business users   Infrastructure Architect  Design underlying IT infrastructure that will support big data analytics initiative   Note: Organizations should keep in mind the potential challenges in finding the right talent and should plan for that Source: Booz Analysis; Search Business Analytics , Big data' analytics programs require tech savvy, business know-how by Rich Sherman TechTarget.com Booz & Company 8
  10. 10. 3 Get Started Many companies have already embarked on Big Data programs, even as they continue to enhance existing capabilities Major Industry Movements Leading players enhance existing Data Analytics + Management Capabilities… … and make net new investments into “Big Data” Programs  Continuing to enhance the “holistic view of customer” across businesses to provide consistent interaction patterns across all channels for sales, service, risk and marketing  Renewing information management infrastructure and augment processing power -to support increased data volumes and enhanced analytical capabilities  Standing up separate “Big Data Analytics” units – to explore new technologies and develop new data access and modelling skills +  Establishing a new “Big Data and Analytics Eco-System” – primarily led by the marketing function  Undertaking enterprise-wide information rationalization programs -- to identify simplification and cost reduction opportunities  Implementing new and separate “Big Data Infrastructures” – to explore relevance of new technologies, understand challenges of integrating into enterprise architecture, scope the scale of investments, etc.  Developing self-funding spend programs to manage the large investments required  Net new investments –to “do-learn-evolve” this new Data Management paradigm and technology “Tested“ Business Cases Booz & Company “Unproven“ Business Cases 9
  11. 11. 3 Get Started We typically see a three step process to achieve Big Data Maturity, each with unique characteristics Big Data Maturity Stages Establish Evolve Steady State Maturity Evaluate Maturity Characteristics Time • Assessing “what” and “how” Big Data is relevant to enterprise • Prioritizing Big Data opportunities Booz & Company • Defining technology solution (e.g., architecture, infrastructure, and analytics) • Navigating evolving technology and vendor landscape • Implementing new Operating Model and processes to govern Big Data’s interaction with IT, Ops and Business • Locating and onboarding required skills (e.g., Data Scientists, Strategic Information Officers) 10
  12. 12. 3 Get Started Getting started involves first defining the opportunity followed by a pilot Booz & Company Big Data Approach Data Strategy Baselining of Data Capabilities Target Definition and GapAnalysis Implement further Big Data Pilot(s) Business case& Roadmap  Baseline existing internal data sources and analytics capabilities and benchmark with best-in class, analyze internal showcases  Project future Business Demand for Data and Analytics Services – Develop long list of Big Data business opportunities/use cases/pilots – Prioritize opportunities/pilots  Develop Target Picture and identify capability gaps (Data, Organisation, Skills, IT,…)  Define how to address gaps and develop High-Level Business Case and Roadmap/ Implementation Plan Pilot Concept Operating Model Adaptation Full Capability Building (Strategy Realignment) Pilot Evaluation  Detail pilot business opportunity  Define pilot approach and team  Develop key metrics to measure pilot success  Identify (technology) partners to use for pilot (to avoid lengthy IT programs)  Implement pilot jointly with co-operating solution partner  Measure pilot success and derive learning’s for mid-to-long term agenda  Review business case Top Mgmt. Go-Ahead Booz & Company Pilot Execution Big Data Scale-Up/ Transformation  Review business opportunities in light of pilot results  Define detailed RFP  Identify required changes to Operating model and (IT) capabilities  Define transformation agenda and roadmap  Roll-out enabling information technology and updated architecture  Implement changes to processes and operating model  Monitor progress/implementation success Top Mgmt. Go-Ahead 11

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