Data Analytics as a Service

Transcription

1 Data Analytics as a Service David van Steen Product Manager Marcel van den Bosch Data Scientist 3-Feb-15

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3 Data Analytics as a Service Agenda What is Data Analytics? Data Analytics Do-It-Yourself DAaaS platform Join Data Atos 3

4 Data Analytics as a Service What is Data Analytics? Collecting, organizing and analyzing large sets of data to discover patterns and other useful relations Pattern Recognition Text Mining Academic discipline for 25+ years New processing capabilities enable for a powerful extension of usable applications Network Analysis Simulation Advanced Visualisation Optimisation Part of the Information element of Gartner s Nexus of Forces 4

5 Atos ambition Vision, strategy and 2016 ambition presented at Atos investor day, November 15th, 2013 Become a Tier 1 and THE preferred European global IT brand Growth through new offerings Work place / BYOD 25bn Application Management 41bn Merchant Services 30bn IT Consolidation 47bn ECM & Collaboration 15bn Digital Security 21bn MES/PLM 9 bn Smart Utilities 3 bn Big Data / Advanced Analytics 36bn Cloud Services 50bn Mobility Services 7 bn 5% 7.5% 10% 25% 50% 2013 market size Market CAGR over the period* 5

6 Data Analytics as a Service Agenda What is Data Analytics? Data Analytics Do-It-Yourself DAaaS in action Join Data Atos 6

7 Data Analytics: Doe-het-zelf Fraude detectie door de RDW Onderzoeksvraag: Hoe kun je achterhalen wanneer er sprake is van tellerstandfraude? Achtergrond: RDW heeft per 1 januari 2014 de registratie en de database van Stichting Nationale Auto Pas (NAP) overgenomen. Per 1 januari 2014 is het terugdraaien van de tellerstanden verboden. Issue: Voor naar schatting 5% van de voertuigen is de tellerstand gemanipuleerd. Bij import auto s ligt dit percentage nog veel hoger. Focus: Per regio Per autotype Per autobedrijf Per eigenaar 7

8 Data Analytics as a Service Agenda What is Data Analytics? Data Analytics Do-It-Yourself DAaaS in action Join Data Atos 8

9 Vitens Use Case Context Drinking water company Vitens Innovation Playground Production, purification and distribution of drinking water Customers: 5,5 million Turnover: 352,4 meuro Network: km Production locations: 96 Employees: Dedicated part of the distributionnetwork for test and validation of new technologies km 2, 2000 km piping - R&D subjects: Smart-meters, Water quality, Predictive analytics, Asset optimization 9

10 Vitens Use Case Proof of Concept Use data analytics to predict leakages in the drinking water distribution system Data Analytics as a Service Visualization Scope: Vitens Innovation Playground 26 locations, 161 sensors 1 year of data Goal: Develop predictive algorithm Show integration with current IT solutions using standard algorithm XHQ Connector & Cloud Gateway Analytic Apps Cloud Data Analytics Cloud Data Management Cloud Data Sources Partners: KWR Hydrologic Joined Siemens & Atos development Cloud based data analytics platform Siemens XHQ connectivity to datasources Big data enabled solution 10

11 Vitens DAaaS Demo Data in Osisoft PI Dag 1 Dag 2 Lekkage 11

12 Vitens DAaaS Demo Details, Location & Sensors 12

13 Vitens Use Case Results Predictive Algorithm Approach defined, preliminary model build and next steps defined Predictive patterns found in subset of the data Integration PoC shows complete DAaaS workflow Vitens dataset used in demo Osisoft PI Standard algorithm applied Geo-visualization of results within DAaaS portal build Vitens Data DAaaS platform XHQ gateway Next steps: Improve algorithm and extend dataset Connect to real-time data GEO Visualization 13

14 Predictive Maintenance Use Case Case description Short time period Hours/days Low Downtime involved Time to React Cost impact Extended downtime Minutes / seconds High 14

15 Predictive Maintenance Use Case Case description Data from manufacturing lab and specifically abearing dataset. Test Rig Setup (fromqiuet al): Four bearings were installed on a shaft. All bearings are force lubricated. Rotation speed was kept constant at 2000 RPM by an AC motor coupled to the shaft via rub belts. A radial load of 6000 lbsis applied onto the shaft and bearing by a spring mechanism. 15

16 Predictive Maintenance Use Case Analysis flowchart DATA COLLECTION EXTRACT USEFUL FEATURES FROM THE DATA GET VISUAL/STATISTICAL PROPERTIES 16

17 Predictive Maintenance Use Case Analysis flowchart 1 4 GET THE MOST USEFUL FEATURES K-MEANS CLUSTERING ARTIFICIAL NEURAL NETWORK Getting most useful features Go from 46 features to the 14 most useful ones. K-Means clustering See if we can group the failure and suspect states (limited accuracy) Artificial Neural Network Prediction model with 92% accuracy 17

18 Results An Artificial Neural Network (ANN) is best suited for predicting a correct Suspect state leading to a Failure state, with an accuracy of 92%. Fast-Fourier Transform frequency analysis and feature analysis (domain, statistical & visual) provide excellent early warning. One bearing gave us a 12 day early warning. DAaaS has provided us with an analytical environment that is at least 15x faster than a local environment. This methodology is a fundamental building block for predictive cases in different markets, using Machine Learning Clustering and Classification. 18

19 Data Analytics as a Service Agenda What is Data Analytics? Data Analytics Do-It-Yourself DAaaS in action Join Data Atos 19

20 Data Atos Blue Kiwi spaces: Big Data Analytics BTN Industrial Data Analytics (IDA) White Papers: Data Analytics as a Service Typical training: Certified Big data scientist Solutions: Hortonworks, Pivotal, Cloudera Data science: Introduction to R and CRAN libraries, SWIRL Coursera courses Contact us! 20

21 Data Analytics as a Service Questions 21

22 Thank you Atos, the Atos logo, Atos Consulting, Atos Worldline, Atos Sphere, Atos Cloud and Atos WorldGrid are registered trademarks of Atos SA. June Atos. Confidential information owned by Atos, to be used by the recipient only. This document, or any part of it, may not be reproduced, copied, circulated and/or distributed nor quoted without prior written approval from Atos. 3-Feb-15