IoT Analytics. Martin Keseg Enterprise Account Manager 26/10/2017

Transcription

1 IoT Analytics Martin Keseg Enterprise Account Manager 26/10/2017

2 What is IoT Analytics? Raw Goods Equipment Temperature/Pressure Equipment Vibration Idle Time Inclement Weather Speed Equipment Utilization Trip Distance Customer Behavior

3 LARGE VOLUME OF DATA PER DAY (> MILLION) IoT is a Big Data Problem Equipment Temperature Customer Behavior Equipment Pressure Equipment Utilization Speed Trip Distance Equipment Vibration LARGE VARIETY OF SENSOR DATA

4 Contextualize the Data UNSTRUCTURED DATA SEMI-STRUCTURED DATA STRUCTURED DATA Video or Voice Data Image Data How to What contextualize is the solution? all these data? Sensor Data Machine Data Geo-Location Data CRM ERP Asset Management

5 Contextualize the Data UNSTRUCTURED DATA SEMI-STRUCTURED DATA STRUCTURED DATA Video or Voice Data Sensor Data Geo-Location Data ERP Image Data Machine Data CRM Asset Management SIMPLIFIED BY PENTAHO IOT ANALYTICS Ingest Process Blend Data Prep Action Machine Learning

6 IoT Analytics Value Tenants Sense & Capture Integrate & Blend Infer & Act 6

7 IoT Analytics Value Tenants 1 1 Sense & Capture Asset Mgmt Register Sensors Model Assets Store Sensor Data Stream Sensor Data 7

8 IoT Analytics Value Tenants 2 2 Integrate & Blend Data Refinery Validate and Cleanse Process & Blend Stream Processing Rules Engine 8

9 IoT Analytics Value Tenants 3 3 Infer & Act Analytics Machine Learning Report & Alert Workflow Integration Business Outcomes 9

10 3 IoT Platform Modules Asset Mgmt Register Sensors Model Assets Store Sensor Data Stream Sensor Data Data Refinery Validate and Cleanse Process & Blend Stream Processing Rules Engine Analytics Machine Learning Report & Alert Workflow Integration Business Outcomes 10

11 IoT Platform Demo Scenario

12 Big Fleet (222 Vehicles) Web-Based Fleet Management Platform

13 13 Asset Model Types

14 14 Asset Models

15 Hierarchical Vehicle Modeling Utility Vehicle Asset Sensor Data Asset Model Air Pressure Axle Vibration Lights Load Weight Movement Temperature Store models and sensor data 15 Sensor Data Journey Sense Stream Store Blend Infer Inspect Embed & Integrate

16 Adding Context to Sensor Data Sensor Data Contextual Data Vehicle Location GPS Lat / Long Mapping Movement Vehicle Profile Make Model Mileage Operational Systems Maintenance History Maintenance Schedule Service Centers Parts Ordering Parts Inventory Business Outcomes Real-Time Fleet Status and Health Repair Recommendations Optimized Maintenance Scheduling Automated Parts Ordering IoT Data Refinery 16 Sensor Data Journey Sense Stream Store Blend Infer Inspect Embed & Integrate

17 Pentaho s IoT Analytics Workflow Orchestration Sensor MSG Queue Kafka, JMS, MQTT Machine Learning R, Python, Weka Pentaho Analyzer LOB Applications Stream Pentaho Data Integration IOT Data Refinery Pentaho Data Integration Feedback Loop Embedded Traditional Data Analytic Database Pentaho Reporting

18 Why Pentaho? Solve Big Data Problems for IoT Expertise in Machine Learning Industrial IoT with Hitachi

19 IOT Transforms Business Outcomes Business Requirements IoT Analytics Business Outcomes Telematics Analytics Customer Experience Trains as a Service Driver, Equipment and Fuel Analysis Categorize Customer Preference Predictive Maintenance Reduced Down Time and Cost Savings Customer Retention & Upsell Multi-million Dollars Maintenance Savings

20 Q & A

21 Customer Use Case in Detail

22 Customer Experience IMS Challenges Create custom dashboards 1.6 Billion data points/day Predictive behavior patterns Solutions Seamless integration to IMS data sources Embedded IoT Analytics Data-driven insight and predictive analytics Benefits Retain customers Reduce driver claims

23 Predictive Maintenance Hitachi Rail Europe Challenges 3.6 million data points per second Correlating multiple data points Visualization in multitenant Solutions End-to-end Big Data platform Scale to data growth Predict operational events Benefits Millions maintenance savings More reliable and cost effective Better service delivery

24 Customer Experience Veikkaus Challenges Stream customer behavior 20K terminals Blend structured and unstructured data Data from other gaming platform Slow reporting Casino competition Solutions Open architecture Tight Integration to Cloudera HDFS Professional services 24/7 support Benefits Marketing Timely and accurate data Customized offers Boost sales + Customer experience Identify gambling addiction

25 Telematics Analytics Caterpillar Challenges Integrate sensor data and other data Data integrity was in question Dashboard development Solutions Connected all data sources Ingest, process and blend all sensor data Process high volume of data Faster delivery to market Benefits Adjust power to operate the generators $650K savings Reduced ship s hull build-up $800K fuel reduction cost per ship

26 Thank You