Key Data Management Requirements for the IoT

Register to Reserve your Seat

Webinar 1: Vision & Use Cases

Webinar 2: Data Management Requirements for the IoT

Webinar 3: From Concept to Code

Joint Webinar Series

Today’s Speakers

Dirk Slama, Director of Business DevelopmentBosch Software Innovations

20+ years of experience in large-scale distributed application projects including M2M and IoT projects

Joe Drumgoole, Director Partner Technical Services MongoDB

20+years experience of commercial software delivery

• IoT Vision from Bosch: Recap of Part 1

• Data Management Requirements in IoT

• Next Steps

Agenda

5

7,1tn IoT Solutions Revenue | IDC

Some Big Numbers:

1,9tn IoT Economic Value Add | Gartner

309bn IoT Supplier Revenue | Gartner

50bn Connected Devices | Cisco

14bn Connected Devices | Bosch SI

Some small numbers:

http://postscapes.com/internet-of-things-market-size

Peter Middleton, Gartner:“By 2020, component costs will have come down to the point that connectivity will become a standard feature, even for processors costing less than

$1“

IoT Predictions by 2020 - 22

Ten

s

Hu

nd

red

s

T

ho

us

an

ds

Mil

lio

ns

B

illi

on

s

Co

nn

ec

tio

ns

Internet of Things

Machine-to-Machine

Monitored

Smart Systems (Intelligence in Subnets of

Things )

Telemetry and

Telematics

Smart HomesConnected Cars

Intelligent BuildingsIntelligent Transport

SystemsSmart Meters and

GridsSmart Retailing

Smart Enterprise Management

Remotely controlled and managed

Building automation

ManufacturingSecurityUtilities

Internet of Things

SensorsDevicesSystemsThings

ProcessesPeople

IndustriesProducts Services

Source: Machina Research 2014

Growth in connections generates an unparalleled scale of data

Data

Big data

Changing data models

Real-time Processing

Aggregation

Internet of Things

Large estates of devices

Evolving applications

All forms of data

Data streaming and processing

Pre-IoT (M2M)

Limited estate of devices

Single purpose applications

Structured / Semi-structured

Data transfers (sensors and

actuators)

Evolution from M2M to IoT and Big Data

Source: Machina Research 2014

Data

Big data

Changing data models

Real-time processing

Aggregation

Databases will need to address new requirements

Scalability

Flexibility

Analytics

Unified View

Source: Machina Research 2014

IoT Foundation: Bosch Suite for IoT

A

D

C

B

Scale

Flexibility

Analytics

Unified View

IoT Foundation: Bosch Suite for IoT

A

D

C

B

Scale

Flexibility

Analytics

Unified View

Data has Changed

• 90% of the world’s data was created in the last two years

• 80% of enterprise data is unstructured

• Unstructured data growing 2x faster than structured

Yesterday’s Tool for Today’s Data?

IoT Data Management Requirements

Rich Applications Single View

Operational Insight

Real-Time

Business Agility

Continuous Innovation

Enterprise-Ready

Secure & Reliable

Multiple Data Sources Process Convergence

Building Rich Applications

Farm to Fork:Track production through supply chain

Quality Assurance:Proactively reduce product wastage

Fleet Management:Compare drivers & vehicles

• IoT apps generate multi-structured data

• Modeled more efficiently as JSON documents

• Exposed to powerful analytics

• Developers more productive– Less time wrestling ORMs

– More time creating apps

Modeling Complex Data

{ vehicle_id: ‘123abc’, vehicle_driver: ‘Miller’, base: ‘London’, tracking: [ { timestamp: ‘2014-01-17-12:00:00’, location: [51.123,-0.232], speed: 55, … }, { timestamp: ‘2014-01-17-12:15:00’, location: [51.224,-0.238], speed: 5, … } }}

Unlocking Business Agility

Customer Insight:Optimize in-store product placement

Smart Factory:Flexible assembly lines, autonomous production modules

Fleet Management:Extend to fuel efficiency, driver safety

Continuous Integration

ID PSI Temp Loc

New Column

3 months later…

• Dynamic database schema

• No need to define upfront

• Enables agile methodologies– Evolves as the application changes

– Eliminates teams co-ordinating ALTER TABLE operations

Creating a Single View

Single View of the Customer:Across channels

Single View of Production Across multiple lines

Single View of the FleetAcross real-time and service history

Business Process Convergence

New Table

New Table

New Column

• Aggregate data from multiple source systems– Real time sensor data blended

with enterprise data

• Define single schema, update whenever the source systems change…or JOIN hundreds of tables!

• Document model & dynamic schema makes single view a reality

Real-Time Operational Insight

Inventory Management:Track stock levels, predict demand

Optimize Production LinesAnalyze robotic performance

Preventative MaintenanceCorrelate baselines to diagnostics

Powerful Analytics on Live Data

Enterprise Ready Platform

Secure Customer DataPrivacy & compliance

Continuous AvailabilityMaximize production capacity

Scale Data VolumesMore sensors, more vehicles

Scalable, Reliable, Secure

Automatic Sharing & Replica Sets

Defense in Depth

Case Study

Field Data Capturing

Project SCFD Structured Capturing

of Field Data Components: Car

brakes, power steering, etc.

Usage patterns: temperature, voltage, etc.

Predictive maintenance, product optimization

Why MongoDB: Constantly evolving

system, from a data capturing and a data analytics point of view

Large amount of streaming data

Asset Management

Stream Processing

Big Data Manageme

ntAnalytics

BRM BRM

Next Steps

Services to Support IoT Apps

TRAININGTraining for developers and administrators – online and in-person

CONSULTINGExpert resources for all phases of IoT implementations

• Listen On-Demand Part 1: IoT Vision & Use Cases, Bosch & Machina

Research

• Register for Part 3: From Concept to CodeRegister Now

• Download the Bosch SI & MongoDB Whitepaper IoT & MongoDB

Learn More

For More Information

www.mongodb.com

www.bosch-si.com

Any Questions