Open Big Data Platforms The New Frontier. October 2015

Transcription

1 Open Big Data Platforms The New Frontier October 2015

2 Atoms to Bits 2 An irrevocable, unstoppable and exponential trend * 1 2 Amplification of end users * N. Negroponte

3 Five Dimensions of a Big Data System 3 Go Deep Complex questions Interactive exploration In Real-time Cover Breadth Large amounts of data Many types of data All necessary data Without Pre-processing Recent data Real-time data Within a Window of No pre-aggregates No tuning Fast response Interactive

4 Moore s Law and Hardware Cost 5

5 What Do Customers Really Want? 4 Enable discovery Enable selfservice Process all forms of data Unstructured Data 80% Real-time Dashboards Complex event processing Maximize correlation identification Operationalization of decision Trusted Data 20% Reduce timeto-insights

6 Our Success Stories

7 Singapore-based Communications Major Delights Customers 8 Understand customer behavior, buying patterns and sentiments and delight customers by delivering the right content at the right time Develop and sell Insights from the wealth of Deep Packet Inspection (DPI) data Customer behavior analytics solution integrated and correlated structured/unstructured data from geographic information systems, social and enterprise data sources leveraged to deliver analytics around customer lifestyle, purchase criteria, usage patterns, browsing behavior, location and time Generated automated personalized offers using a recommendation engine A Singapore based infocommunication Company New revenue streams from selling anonymized data Increased ARPU and reduced churn Personalized promotions delivered with improved success rate

8 Bio-pharmaceutical Major Improves Clinical Trials 9 Need to optimize clinical trial monitoring process with risk-based monitoring Improve process visibility, efficiency and time to market, lower costs without quality compromise Scalable and extensible risk-based monitoring solution with predictive analytics driven by pattern matching, machine learning and clustering algorithms Text mining (NLP-based components) used to extract relevant data from past reports both structured and unstructured World s premier biopharmaceutical company Early diagnosis of risks during design, conduct and close-out of clinical interventional studies 360-degree view of risks with data and metrics from trial management systems, CDM systems as well as safety management systems Risk recalibration during conduct of trial

9 Apparel Major Learns About its Customer s Sentiments 9 Social listening tool lacked the ability to analyze unstructured customer inputs on discussion forums Text analysis model gauges qualitative inputs, picture emoticons, checks spam, and delivers powerful insights into customer sentiments Interactive visualization of word clouds and corresponding insights Improved customer perception from better social listening Sentiment analytics prior to product launches improved success rates Improved inventory management from better planning

10 10 Telecom Services Company Prevents Network Faults Using Predictive Maintenance Network faults in the ADSL backbone disrupt data services to customers and lead to lower customer satisfaction Ingested 15GB ADSL data of customers facing no fault, into Hadoop - Infosys Information Platform (IIP) to find Control Signature using signal processing and statistical techniques on connection with properties like attenuation,upload/download rate, etc. Computed FaultSignature from connection propertiesduring fault Developed predictive model to provide probability of current connection state that may result in faults in the next 7 days. This helped us predict faults for connections, DSLAM and geographic area using 16.5 million records in 5 sec on an 8-core, 64GB RAM,5 node cluster Impending network faults predicted a week in advance with a high degree of accuracy to fix potential network failure points Solution based on open source stack including Apache Spark and prebuilt Infosys components was delivered in just 5 days at an impressive price-performance ratio

11 Chocolate Maker validates business hypothesis quickly with advanced analytics 12 A global chocolate manufacturer wanted to develop out-of-stock analysis for their products across retail superstores Infosys Information Platform (IIP) deployed on 5-node AWS cluster Ingested 5 weeks sample data (5.7 million rows) into HDFS. IIP toolset enabled creation of on-demand schemas (models) to access data as well as data wrangling Calculated the historical probability at item, store, register level and ran prediction using R-based Binomial and Geometric distribution model to predict out-of-stock. Visual dashboard was developed in Tableau to demonstrate out-of-stock probability by store, hour, day, and item From no capability to business insights in just 3 weeks along with the required environment to run multiple experiments quickly Out-of-stock analysis demonstrated ability to drill down to details at store, item and register level

12 Freight Railroad Network Major Speeds Up Business by Reducing Unnecessary Stopping of Trains 12 Excessive, unwarranted braking events on locomotives of a freight railroad network generated from their PTC platform were resulting in unnecessary stopping of trains Examined locomotive brake data along with engineer characteristics, wayside data streams, etc., and PTC signal data after ingesting into Infosys Information Platform on AWS Cloud Used R for prediction model and Tableau for visualization Analysis of locomotives by PTC profile and attributes involved in braking events and provided 360-degree view; Pareto analysis of target type of braking events Basic text mining (N-Gram)/word cloud on delay comments and locomotive delay prediction Real-time prediction with higher accuracy helped increase the average velocity of freight trains by 1 MPH. Asset utilization to increase by 45%; potential to yield additional $200 million revenue

13 ATM Manufacturer Improves ATM service levels 13 A leading ATM manufacturing and service company wanted to reduce the cost of maintaining ATMs while improving customer service and SLAs Ability to predict which ATM is going to fail next week (with 80% accuracy) based on alert and incident data from XMS and machine data Ingested ticket data from 8500 ATMs/4 million records into Infosys Information Platform (IIP) - and cleaned it (date, spaces, null fields, etc.) in 27 sec on 10-node AWS cluster (32 CPU, 64GB RAM, 640 GB SSD storage) Enriched data to create required fields for analysis and executed prediction in 60 msec using logistic regression in Spark Data ingested into Oracle and visualized using Tableau Reduced downtime of ATMs by 10%, enabling an increase of 15% in transactions that would have been lost because of faulty ATMs

14 Mining Major Uses Streaming Data from Trucks to Enable Predictive Maintenance 14 The failure of autonomous and unmanned trucks in mining affects the entire supply chain and has critical consequences for business Close to 200 sensors are embedded in each of these vehicles to feed information about both the vehicle and the terrain which is sent back to a remote operations center Data derived from the sensors was streamed into Infosys Information Platform (IIP) at the rate of 27,000 messages/sec through Kafka. Mathematical model developed in Apache Spark was applied on this data to derive the probability of equipment failure, equipment replacement requirement, or if the trucks are in good shape Overlaid results on world map - a native HTML5 application on top of IIP that can be drilled down by clicking on the color codes With the elastic and scalable IIP, we are supporting the client s business plan to increase the trucks by at least 300% with more sensor data loads Real-time analytics to get view on adjustments to production schedule, spare part order release, etc.

15 Predictive Maintenance in Pharma Manufacturing 15 Unexpected equipment breakdown in pharmaceutical manufacturing plants impacts process quality and drug safety apart from issues such as down-time and maintenance costs Identified a specific set of equipment - reactors and upstream degasifier as a logical sub-process for analytics Ingested 18 months SAP PM data, PLC data and alarm patterns in Infosys Information Platform (IIP) to train a logistic model and then validated the model with 4 months of data Built 1-day and 2-day prediction models and model score cut-off was chosen to balance the capture rate vs. false alarm percentage as these two represent a trade-off Predicted major breakdown well ahead (1 or more days vs. just hours) with 80% accuracy and reduced false alarms Maintenance teams appreciated the value and pharmamajor is planning to scale implementation across multiple plants

16 Thank You 2015 Infosys Limited, Bangalore,India.All Rights Reserved.Infosys believes the information in this document is accurate as of its publication date; such information is subject tochange without notice.infosys acknowledges the proprietary rights of other companies tothe trademarks,product names andsuchother intellectualproperty rights mentionedinthis document.except as expressly permitted, neither this documentationnor any part of it may be reproduced,storedin a retrieval system,or transmittedinany formor by any means, electronic,mechanical, printing, photocopying, recordingor otherwise, without the prior permissionof Infosys Limitedand/or any namedintellectualproperty rights holders under this document.