Borges Map How Big Data changes business strategy

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1 Borges Map How Big Data changes business strategy European Leasing & Consumer Credit Industry Annual Conventions Barcelona, October 9, 2014 Philip Evans

2

3 The world becomes its own map What the Google self-driving car sees

4 The world becomes its own map What the Google self-driving car sees Layers of modular, interoperable, frequently open-source software Successive probabilistic algorithmic approximation Machine learning, not 80s AI 50B IPenabled sensors by Trillion sensors of all types The world s stock of data doubling every 2 years 99% digitized 50% has an IP address 2.2B broadband mobile connections by 2015 Smartphones the fastest-adopted technology ever except tablets

5 The world becomes its own map Smart parking

6 The world becomes its own map What the retailer sees and the customer

7 The world becomes its own map Data as infrastructure Côte d Ivoire Source: South-America-Asia-darkness.html

8 Data as infrastructure 2.5 billion call records in Côte d Ivoire Dec 2011-Apr 2012 Mobility network 1 Communication network 2 1 Trajectories of 50,000 randomly selected individuals between sub-prefectures over a 5 month period 2 Number and duration of calls between pairs of cell phone towers aggregated by hour Nodes color-coded by predominant language Source: Exploiting Cellular Data for Disease Containment and Information Campaigns Strategies in Country-Wide Epidemics A. Lima, M. De Domenico, V. Pejovic, and M. Musolesi

9 Data as infrastructure Optimizing the bus network in Abidjan, Côte d Ivoire Current bus network Off-network travel Travel volume to one destination Optimized network Four new routes proposed. No additional resources required 10% reduction in city-wide travel times Source: AllAboard: a system for exploring urban mobility and optimizing public transport using cellphone data Michele Berlingerio, Francesco Calabrese, Giusy Di Lorenzo, Rahul Nair, Fabio Pinelli, Marco Luca Sbodio IBM Research https://www.youtube.com/watch?v=9givvecxnee

10 The hardware: Google Data Center 2013 One of 81: Council Bluffs, Iowa

11 The software: Google Search A technology stack ¼ sec USERS WEB PAGES DNS (FRONT-END) SERVERS MAPREDUCE MAP REDUCE MAP REDUCE MAP REDUCE MAP REDUCE Adwords/ Adsense Server Ad Auction Algorithm Crawler PageRank Algorithm Spellcheck API Spell Algorithm Google Translate API Translate Algorithm BIG TABLE 1,000 servers /query INDEX AND DOCUMENT SERVERS

12 Top of the stack: Mashups Housingmaps.com: the first real estate search engine

13 Walmart, Tesco, Sainsbury s Data aggregation via loyalty cards Walmart (90s) Tesco Nectar Customer Basket Transaction data: SKU x POS x time Customer data: Identity (50 descriptors) x SKU x POS x time Customer data: Identity (50 descriptors) x SKU x POS x time SKU POS Store Retailer Aggregator Manufacturer

14 From value chains to stacks The typical layers Experiments, recombination (low cost of failure) Distributed user & developer innovation, swarming Social capital (reputation, reciprocity) embedded in communities Large, thin platforms, driven by network effects Coexistence of collaboration & competition Hierarchical projects Linear: spec plan execution completion Disciplined replicable processes Engineering, reengineering, end-to-end optimization Co-design of handoffs, interfaces Infrastructure: Build it, and they will come Risk-avoiding, robust, resilient (high cost of failure) Long investment horizons Capital-intensive Scale- & utilization-intensive

15 From value chains to stacks A self-replicating template INSTITUTIONS ORGANIZATION IT Experiments, recombination, Distributed user innovation Social capital, communities Platforms, network effects Collaboration + competition Peer communities trading user-generated content Open source & developer communities Social networks Enterprise 2.0 Curated platforms Contests, innovation jams Revenue/growth Scrum, agile Mashups, hacks Algorithmic iteration Advanced analytics A/B testing [APIs] Hierarchical projects Linear: spec to completion Disciplined replicable process Engineering, reengineering Co-design of handoffs Traditional oligopolists Vertically-integrated or in close vertical relationships Portfolio of parallel businesses Divisional, SBU Tight, vertical end-to-end management ROCE, ROI Projects focused on product/segment requirements (ERP, CRM, etc) Surveys, trials [Virtualization] Infrastructure Risk-avoiding, robust Long investment horizons Capital-intensive Scale- & utilization-intensive Utilities, co-ops Public investment Data repositories Open data Cloud services Functionallyconsolidated services Cost centers Efficiency metrics Data centers Networks Security Compliance Data base integrity

16 The elephant in the room Facebook s default privacy settings (Nov) (Dec) (Apr) Source: Matt McKeon Number of people (logarithmic) K 5M 1B

17 BCG THE BOSTON CONSULTING GROUP

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