IoT Analytics

Martin KesegEnterprise Account Manager

26/10/2017

What is IoT Analytics?

Raw Goods Equipment Temperature/Pressure

Equipment Vibration

Equipment Utilization Trip Distance Customer Behavior

SpeedIdle Time Inclement Weather

Equipment Vibration

IoT is a Big Data Problem

Equipment Temperature

Customer Behavior

Equipment Pressure

SpeedEquipment Utilization

Trip Distance

LARGE VARIETY OF SENSOR DATA

LA

RG

E V

OLU

ME

OF

DA

TA P

ER

DA

Y (

> M

ILLI

ON

)

Contextualize the Data

ERP

CRMAsset

Management

STRUCTURED DATA

Sensor Data

Geo-Location DataMachine Data

Image Data

Video or Voice Data

SEMI-STRUCTURED DATAUNSTRUCTURED DATA

What is the solution?How to contextualize all these data?

Contextualize the DataSTRUCTURED DATASEMI-STRUCTURED DATAUNSTRUCTURED DATA

SIMPLIFIED BY PENTAHO IOT ANALYTICS

Ingest Process BlendData Prep

Action

Machine Learning

ERP

CRM

Asset Management

Sensor Data

Geo-Location Data

Machine DataImage Data

Video or Voice Data

6

IoT Analytics Value Tenants

321

Sense & Capture Integrate & Blend Infer & Act

7

IoT Analytics Value Tenants

1

Sense & Capture

1

Asset Mgmt

Register Sensors

Model Assets

Store Sensor Data

Stream Sensor Data

8

IoT Analytics Value Tenants

2

Integrate & Blend

2

Data Refinery

Validate and Cleanse

Process & Blend

Stream Processing

Rules Engine

9

IoT Analytics Value Tenants

3

Infer & Act

3

Analytics

Machine Learning

Report & Alert

Workflow Integration

Business Outcomes

10

3 IoT Platform Modules

1

Asset Mgmt

Register Sensors

Model Assets

Store Sensor Data

Stream Sensor Data

2

Data Refinery

Validate and Cleanse

Process & Blend

Stream Processing

Rules Engine

3

Analytics

Machine Learning

Report & Alert

Workflow Integration

Business Outcomes

IoT Platform Demo Scenario

12

Big Fleet (222 Vehicles)

24

46

152

Web-Based Fleet Management Platform

13

Asset Model Types

14

Asset Models

15

Hierarchical Vehicle Modeling

Store models and sensor data

Asset ModelSensor DataUtility Vehicle Asset

Air Pressure

Axle Vibration

Lights

Load Weight

Movement

Temperature

Sensor Data

Journey

StreamBlend

Infer

Sense

Inspect

Embed & Integrate

Store

16

Adding Context to Sensor 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

Sensor Data Contextual Data

Business Outcomes

• Real-Time Fleet Status and Health

• Repair Recommendations

• Optimized Maintenance Scheduling

• Automated Parts Ordering

IoT Data Refinery

Sensor Data

Journey

StreamBlend

Infer

Sense

Inspect

Embed & Integrate

Store

Pentaho’s IoT Analytics Workflow Orchestration

IOT Data Refinery

Analytic Database

Pentaho Analyzer

Sensor

Traditional Data

Pentaho Data

Integration

Pentaho Reporting

MSG QueueKafka, JMS, MQTT

Machine Learning

R, Python, Weka

Stream Feedback Loop

LOB Applications

Embedded

Pentaho Data

Integration

Why Pentaho?

Industrial IoTwith Hitachi

Expertise in Machine Learning

Solve Big Data Problems

for IoT

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

IOT Transforms Business Outcomes

Business Requirements IoT Analytics Business Outcomes

Q & A

Customer Use Case in Detail

Customer Experience – IMS

Challenges

• Create custom dashboards

• 1.6 Billion data points/day

• Predictive behavior patterns

Solutions

• Seamless integration to IMS data sources

• Embedded IoTAnalytics

• Data-driven insight and predictive analytics

Benefits

• Retain customers

• Reduce driver claims

Predictive Maintenance – Hitachi Rail Europe

Challenges

• 3.6 million data points per second

• Correlating multiple data points

• Visualization in multi-tenant

Solutions

• End-to-end Big Data platform

• Scale to data growth

• Predict operational events

Benefits

• Millions maintenance savings

• More reliable andcost effective

• Better service delivery

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

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

Thank You