1. BIG DATA USES CASES & LESSONS LEARNED Marrakech – March 2016 Alexandre AKROUR, CEO 1

2.  Wondering about big data?  Some definitions and concepts  Big Data Application Usages  Main Big Data Application Domains  Lessons Learned UPS Carrefour  Conclusion  Q&A Agenda 2

3. Wondering about Big Data? 3

4. Order of Magnitude of Data © March 2016 - Broad Solutions & Consulting4 Source: Nokia HERE, Forbes, Idealab, GE, ITF calculations 1 octet Kilobyte ~1000 (10 3 ) octets Megabyte ~1000.000 (10 6 ) octets Gigabyte ~1.000.000.000 (10 9 ) bytes Terabyte ~1.000.000.000.000 (10 12 ) bytes Petabyte ~1.000.000.000.000.000 (10 15 ) bytes Exabyte ~1.000.000.000.000.000.000 (10 18 ) bytes Zettabyte ~1.000.000.000.000.000.000.000 (10 21 ) bytes Yottabyte ~1.000.000.000.000.000.000.000.000 (10 24 ) bytes 25 gigabytes: data analysed per hour by Ford’s Ford Fusion Energi plug-in hybrid 60 gigabytes: data gathered per hour by Google’s self-driving car 140 gigabytes: data gathered per day by Nokia HERE mapping car 30 terabytes: data produced by Boeing 777 on a transatlantic trip Several petabytes: traffic data stored by INRIX to produce traffic analysis for e.g. Google traffic 1 zettabyte: Total amount of visual information conveyed from the eyes to the brains of all humans per day in 2013 4.4 zettabytes: Estimated size of the digital universe in 2013 44 zettabytes: Projected size of the digital universe in 2020

5. Hype cycle for emerging technologies (2015) – Source: Gartner 5© March 2016 - Broad Solutions & Consulting

6. Big Data Application usages 6 Cloud Big Data Analytics (ML, IA, …) Data Visualisation Prediction Analytics Geo Routing Fraud Detection Recommandation Systems Automatic Quality Management Web Semantics analytics Automatic e- Reputation « Commodities » © March 2016 - Broad Solutions & Consulting Value Added Services providing business differentiation Extended IS Applications Full Processes integrated

7. 1.Understanding and Targeting Customers 2.Understanding and optimizing Business Processes 3.Personal Quantification and Performance Optimization 4.Improving Healthcare and Public Health 5.Improving Sports Performance 6.Improving Science and Research 7.Optimizing Machine and Device Performance 8.Improving Security and Law Enforcement 9.Improving and Optimizing Cities and Countries 10.Financial Trading Main Big Data Application Domains 7

8. 8 Text Mining Web Mining Machine Learning Artificial Intelligence Automated Text Summary, data extraction, entity resolution… Automated Web crawling, data gathering & indexing, market trend analyses, Economic Intelligence… Recommandation systems, prediction analytics, reninforcement learning… Unsupervised learning, deep learning, knowledge inference, speach-to-speach translation (Skype)…

9. Lessons Learned - UPS 9 Context:  More than 102.000 vehicles  16 million shipment / day worldwide Application domains of Big Data:  Planning  Routing  Flying  Driving  Geolocation How?  Determination of what packages loaded on each vehicle  Telematics technology solution to gather data from different aspects of fleet operations Data analyzed:  Engine monitoring  Speed  Mileage  Number of stops  Miles per gallon  Safety aspects

10. Lessons Learned – UPS (cont’d) 10 Business Benefits

11. Lessons Learned - Carrefour 11 Context:  10860 Stores WorldWide  33 Countries  More than 100 M of family houses customers WW  12,5 million stores’ notes / day Application domains of Big Data:  Replenishment  Price optimization  Marketing  Purchasing strategy How?  Determination of goods buying habits by customers  Prevision of selling trends by product / day for each day of the year  Gathering of daily competition prices on more than 3000 products Data analyzed:  Clients' Buying history  Inventory rotation speed  Competition prices  Frequency of products association  Periodicity of clients’ shopping at stores

12. Lessons Learned – Carrefour (cont’d) 12 Business Benefits  5% Sales increase due to inventory leakage reduction  3% Purchasing prices reduction by decreasing the number of Products replenishment  6% Waste avoidance by reducing quantities ordered  Manpower optimization during pic times  Just-In-Time Cashier Operation: less than 10 minutes to access to Cashiers during “high traffic” hours 15M€ saved annually since 2012

13. Conclusion 13 Identify Optimization Potential Determine Data Required Deploy Collection Points Gather Projected Data Analyse Data Apply Optimization Actions Iterate Data Strategy is no more an option, yet! Sweet your assets! Spread the Insights over the entire organization

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