Borges Map How Big Data changes business strategy

Transcription

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

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