Breakout A. Big Data

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2 Breakout A Big Data

3 Facilitator Jay Parmar, Director of Policy and Membership, BVRLA Speakers Niranjan Thiyagarajan, Frost and Sullivan Sheliah Mackie, Blake Morgan LLP Martin Drake, Drive Software Solutions Dr. Steve Cassidy, ESP Group

4 Key Theme Connected vehicles generate valuable data; what is available, who owns it and how can it be monetised?

5 Overview Niranjan Thiyagarajan Frost and Sullivan

6 Breaking down the Big Data Environment with an Automotive Perspective Niranjan Thiyagarajan Senior Consultant July 2014

7 Connected Living 2020 Digital content is doubling every 18 months, most of the data in the world today has been created in the last two years alone. Future of Smart Homes, Virtual Work, and Connected Cities Global Data Traffic in Zettabytes, Global Big Data Market in $ Billion, $122.6 $ $ $

8 The Four Pillars for an Effective Big Data Strategy Shift towards big data strategies is taking place due to the need to cut down on spiralling warranty costs and to create a data sharing network between the dealer, customer, OEM and others. Data Collection Serving Addressable Market Across Automotive Value chain Data Storage Digital Intelligence and Analytics User Experience Clear connectivity strategy necessary to initiate data collection. Data Analytics firms to collect the right data at the right time. Volume, Velocity, Variety and Veracity CRM, Warranty Reduction, OTA 8

9 What are the Benefits Achieved Using Big Data? Frost & Sullivan expects 60% of OEM s to come up with their big data strategy and offerings within the next two years. Reduced Warranty Cost Optimised Manufacturing Enhanced CRM Predictive Maintenance Options Usage Based Insurance OEM s ($ ) Vehicle Users ($ ) $ saving per car Service Providers ($ ) Infrastructure /Society ($ ) Location Based Service Smart Mobility Solutions Traffic Management Systems Smart Parking V2X & V2V Source: Frost & Sullivan analysis. 9

10 Where it can Help - Big-Data Features and Services A successful crowd sourced product offering like Waze which was lapped up by Google for $1 billion (without a revenue model) is a classic example of big data success OEM Product Planning OEM Warranty & Aftersales OEM Marketing Connected Services Providers Fleet Related Services Component Failure Prediction Dynamic Parts Pricing Targeted Digital Marketing Traffic Management Fleet Optimization Optimizing Vehicle Performance Predicting Recall Scenarios Social Media Usage Analytics Public Transport Dynamic Route Planning Apps & HMI Usage Analytics Proactive Diagnostics Brand Loyalty Analytics Multimodal Journey Planning Freight Pricing Feature Packaging (Option/Std) Used Car Valuation Cross Brand Ownership Analytics Disaster Management Driver Behavior Analysis Forward Looking Innovative Services Current Services which will benefit from Big Data Source: Frost & Sullivan analysis. 10

11 Case Study 1 - Impact of Big Data on Vehicle Pricing Transaction pricing to become transparent as internet aggregators enter the market, vehicle pricing transparencies is expected to disrupt dynamics of the industry Big Data Helping Create New Services Transparent Pricing on New Cars Digital Profiles of Used Cars Disruptive Influence that Big Data can have Mystery Shopping Jobs of Pricing Analysts Influencing Customer Decisions 11

12 Case Study 2 - Prognostics in Commercial Vehicles The benefits today are monetized largely by vehicle manufacturers, this will slowly change as aftermarket telematics vendors are launching attractive solutions AFTERMARKET VENDORS INDEPENDENT SERVICE STATIONS ANALYTICS COMPANIES TRUCK FLEET MANAGER FLEET MAINTENANCE SOFTWARE VEHICLE MANUFACTURERS DEALERSHIPS TIER-1 SUPPLIERS Volvo s proactive diagnostics package that ran on a pilot of 1300 telematics equipped vehicles helped reduce diagnostic time by 71% and average repair time by 25%. Volvo groups collaboration with AUTOSAR a standardized software architecture developed in collaboration with other vehicle manufacturers suppliers and developers emphasizes the focus of OMEs in advanced technologies. 12

13 Case Study 3 - Wejo Connecting the Driver to the Car 13

14 Big Data for the Auto Industry: So what? Societal benefits, product differentiation and brand awareness from specific data points are expected to create new revenue opportunities across industry ecosystem Value Creation Driver Services Effective Marketing Customer Loyalty Eg: Food & services coupon, social network, gaming Eg: Over 70% of the pre-sale is done on line Eg: From product to customer centric value add Fleet Services Eg: Advanced tracking, behaviour, scheduling, contextual integration Cost Savings Warranty Cost Product Development Eg: Achieve a 1 3% reduction in warranty costs Fleet downtime Eg: Support data availability & reduce product testing Fleet Operational Costs Eg: Reduce diagnostic time (~70%), repair time (~25%), part availability Eg: Fuel consumption, driver behavior, insurance... 14

15 Image credit: marketoonist.com Thank you, questions? 15

16 Legislation Sheliah Mackie Blake Morgan LLP

17 BVRLA Fleet Technology Congress - July 1 st, 2014 Big Data

18 Big Data what is it? Quantities of data that are too large to process and analyse through traditional database management tools The ability to process and structure large quantities of data in a resource efficient way that derives quality and useability from the data itself Examples include: consumer shopping habits behavioural trends linked to likes / shares GP prescribing patterns telematics 18

19 What is it legally? Likely to start as personal data What is data? One of the following four categories: automatically processed data or data recorded with the intention that it will be so processed data forming part of a relevant filing system data forming part of an accessible record data recorded by a public authority 19

20 What is it legally (contd)? Data also has to be personal data that is: data which relate to a living individual who can be identified: (a) from those data; or (b) from those data and other information which is in the possession or, or is likely to come into the possession of, the data controller living individual individual 20

21 DPA 1998 If the data that you hold is personal data, processing of that data by a data controller will be subject to the Data Protection Act 1998 (the 8 principles) Only data controllers are subject to the DPA Entities processing data on another s behalf are likely to be data processors and not subject to DPA Data processors do not get off scot-free beware of breaching contracts or becoming a data controller in own right! 21

22 Data controllers How do you know if you are a data controller? means a person who (either alone or jointly or in common with other persons) determines the purposes for which and the manner in which any personal data are, or are to be, processed Possible to have joint data controllers or multiple parties who are each data controllers in respect of same data eg hire company collects data to monitor its cars and patterns of those driving them for financial modelling data controller tracking company that decides how to monitor and manipulate that data is also exercising control also a data controller Each controller has to comply with the DPA can decide how to do so in practice in conjunction with employer 22

23 What can controllers do? Before initiating data collection you must: make clear to data subjects what data will be collected explain what that data will be used for and to whom it will / may be passed explain where the data will be stored (nb rules on transfer out of EEA) If the data collected remains linked to an identifiable individual then processing must remain compliant with notification and/or DPA e.g. disclosure for fraud purposes 23

24 Anonymisation If data is anonymised then it is no longer personal data and not subject to DPA Anonymised data is data that: does not relate to any individual; and is unlikely to allow any individual to be identified through its combination with other data at the point of transfer to another party Transfer can be to a third party or to another part of same organisation 24

25 Making money Is anonymisation a magic bullet? depends if done properly depends on the quality of data you want to be left with at end of process does it have value? garbage in, garbage out If your (big) data has quantitative or qualitative value: gives you a competitive advantage in financial modelling could be used for CSR purposes allows you to licence to others for a fee 25

26 Making money (contd) Needs: Ownership data controller(s) not automatically the owners considerable computational and analytical skills skills may not be present in current workforce robust governance and security procedures Sharing with others is potentially problematic: could be anti-competitive if dominant players in market restrict access could be anti-competitive if it leads to industry wide price setting OFT / Competition Commission always were interested in car insurance market! 26

27 Other problems Forthcoming revised EU Data Protection Directive: limited right to conduct automated profiling right to data portability right to be forgotten can it be achieved? Data subjects wanting to monetise their own data Regulation of telematics none in UK yet but: ABI guidance Australian National Transport Commission project ecall Liability for viruses in technology / hacking into other connected technology 27

28 Road Traffic Offences Data ought to be used in order to manage risk: does it tell you if someone is speeding or driving dangerously? If so: no positive duty to report to the police - you are not the police! nor is it likely that you will be prosecuted as a result of their driving difficulties with prosecution in any event owing to risk of challenge by the defence If it is telling you that driver A has a history of speeding, however, then driver A is likely to need re-training 28

29 Corporate manslaughter If an accident occurs then you may need to consider the Corporate Manslaughter and Corporate Homicide Act 2007 In force 6 April 2008 Organisation is guilty of an offence if the way in which its activities are managed or organised:- a) Causes a person s death b) Amounts to a gross breach of a relevant duty of care owed by the organisation to the deceased An organisation is guilty only if the way in which its activities are managed or organised by its senior management is a substantial element in the breach 29

30 Corporate manslaughter (contd) Who are senior management? the persons who play significant roles in: the making of decisions about how the whole or a substantial part of the organisation s activities are to be managed or organised; or the actual managing or organising of the whole or a substantial part of the activities What is the duty of care? the relevant duty of care is one already owed under the law of negligence a question of law to be determined by the Judge, making any findings of fact necessary to decide that question 30

31 Corporate manslaughter (contd) What is a gross breach? The conduct alleged to amount to a breach of that duty falls far below what can reasonably be expected of that organisation in the circumstances falls to jury to decide if gross breach the jury must consider whether the evidence shows that the organisation failed to comply with any health & safety legislation that relates to alleged breach, and if so: how serious that failure was how much risk of death it posed So there is a need to take measures if telematics indicates dangerous driving 31

32 Should you take the plunge? Big data is not going to go away Innovators in sectors can make money / gain market share It s there to be embraced but: consider, plan for and manage risks lead from the top adopt best practice and guidance check your insurance ensure technological and organisational security robust 32

33 Thank you and any questions? Sheilah Mackie Partner

34 Key Fleet Metrics Martin Drake Drive Software Solutions

35 BVRLA Fleet Technology Congress Tuesday 1 July 2014 BIG DATA In Vehicle Leasing and Rental Martin Drake

36 What is Big Data? Big data is high volume, high velocity, and/or high variety information assets that require new forms of processing to enable enhanced decision making, insight discovery and process optimization Gartner Group The importance of Big Data, a definition, 2012

37 What is Big Data? Big data is high volume, high velocity, and/or high variety information assets that require new forms of processing to enable enhanced decision making, insight discovery and process optimization Gartner Group The importance of Big Data, a definition, 2012 The world's technological per-capita capacity to store information has roughly doubled every 40 months since the 1980s Hilbert, Martin; López, Priscila (2011). The World's Technological Capacity to Store, Communicate, and Compute Information" Science 2011

38 Is It Important For Business? Examples of Big Data exploitation by business today Amazon recommending what to buy next Google seems to know what you are searching for Airline seat pricing Supermarkets product pricing and placement What is your competition doing?

39 What Can It Do For Vehicle Businesses? Examples Optimise Pricing For Customers / Markets Understand the characteristics of customers where you are successful Personalise the service do what customer want without being told Predictive costs You only have real cost history for vehicles just before model replacement

40 How? 1. Data Management 2. Analysis 3. Exploitation

41 How? 1. Data Management Database

42 How? 1. Data Management Normalised Consistent Accurate If Data not Normalised The resources you would use for Big Data Exploitation are fully utilised managing your data integration Database

43 How? 1. Data Management Normalised Consistent Accurate If Data not Consistent / Accurate Analysis will be flawed therefore wrong Database

44 Good Big Data: a complex project in itself Project Key points Project Key points Under-estimated Software ability to reach this step Embedded in the top management process Change Management Project Realistic Plan

45 Improving BIG DATA Acquisitions & Disposals Companies & Suppliers 3 rd Party Data Financial Data Drivers Accidents Fuel Data Maintenance Rentals Web Data

46 Analysis? Total Benefit Tool Complexity Reporting: - Exception Analysis Comparative etc Cluster Analysis: - Pattern Recognition: -

47 Exploitation? Analysis gives us models we can use Examples: - Dynamic pricing to customers based on historic success / failure Evolving maintenance base cost models e.business behaviour for customers specific to their needs Improved focus on the best markets Dynamic supplier selection based on price and quality Wider range of fleet improvement services to customers Improved efficiency of in-house staff (productivity, log jams etc.)

48 Exploitation? Key Point 1: Enabling Software Data base architecture & technical environment Normalised data for high quality and low maintenance overhead Fully Coded Data Automated process for operation activities to avoid poor data quality Fully Auditable Data mining engine Reporting / Analysis Tools Expertise

49 Exploitation? Key Point 2: People Skills Top management have to be involved and committed This is business change management not just technology change Significant expertise is required to be successful Evolution of the models / processes becomes business as usual

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51 Modern Mobility Management Dr. Steve Cassidy ESP Group

52 What data? Data from life BVRLA Technology Congress 1st July 2014 Dr Steve Cassidy ESP Group

53 Different Lives ?

54 No Permanent Present Boom Consumer Ownership Bust Collaborate Sharing Individual Simple Connected Complex

55 Mobility as a Service

56 Customer Service Functions profile join inform plan book share match incentivise aware validate pay rate feedback

57 A million stories every day

58 Debate