The presentation focuses on how enterprises can turn Internet-of-Things-Data into Action and outlines the 5-A Model for Data Actionability. 5A stands for Action, Assignment, Analysis, Aggregation and Acquisition. Central questions such as “How do I identify bad quality during or before the process?” or “How do I prevent unplanned downtime?” are addressed in this presentation by Prof. Michael Capone, at the Capgemini Week of Innovation Networks 2016.

1. Copyright © Capgemini 2013. All Rights Reserved
1CWIN München IoT Capone.pptx
IoT Micro-Services
CWIN ´16
München, Telford, Köln
Prof. Dr. Michael J. Capone

2. CSX - Data Actionability
Performance and usage data is
acquired and transmitted every
x seconds.
Data from many
connected devices
is aggregated and
stored
When the data indicates an issue that
can cause a problem or opportunity, a
case is created and a resolution or „Next
Best Action“ is selected
An actor takes action:
1. Customer Care provides
the operator tips and
instructions
2. Sales & Marketing
proactively sends the
customer offers and
orders
3. Technical Service is
dispatched to perform
maintenance
4. System sends messages
to machine or person
We turn IoT data
into ACTION
1
2
4
5
Data is analyzed in
real-time to identify
issues or
opportunities.
3
Control Tower
Connected Service Excellence (CSX)
„2 Steps Ahead“

3. 5-A Model for Data Actionability
Copyright © Capgemini 2013. All Rights Reserved
3CWIN München IoT Capone.pptx
Results often in a use case spceific silo
Functional Layer Description Technology
Action (A5)
A task is automatically created
for sales, contact center, service,
or system.
ERP Integration, Contact Center,
Field Service Management, Mobile
Apps
Assignment (A4) A recommendation or „Next
Best Action“ is proposed.
Knowledge Base, Expert System,
Product Information System
Analysis (A3) Data is viualized, tabulated, and
analyzed. Insights are produced.
Dashboards, Reporting, Analytic
Tools
Aggregation (A2)
Data from multiple, different
sources are combined and
stored.
Big Data, Data Lake, In-Memory
Database
Acquisition (A1) Data is created and transmitted. Sensors, Gateway, Connectivity

4. IoT-Cloud
State Engine
2. Machine Data
5. Alert & Task
Technician
ServiceCloud
Manager
Predictive Model
DB
R
BDLaaS
4. Case
1. Input Stream
PLC Data
Machine Data
Apama
Complex Event Processor
3. Descriptive Stream
EBS
WebMethods
Input Orchestrations
2. Complex Stream
Streaming
Manual
Case
Management
Q: How do I identify bad quality during or before the process?
A: PQM
Aside from the
technician, who else
can benefit from
preditive quality?
What other
processes can
machine data trigger
for which actors?

5. IoT-Cloud
State Engine
2. Machine Data
9. Alert & Task
Knowledge
Technician
7. Model + Error Code
8. Article
ServiceCloud
Manager
Assets
6. Model
5. Location + Error Code
Predictive Model
DB
R
BDLaaS
4. Case
1. Input Stream
PLC Data
Machine Data
Apama
Complex Event Processor
3. Descriptive Stream
EBS
WebMethods
Input Orchestrations
2. Complex Stream
Streaming
Manual
Case
Management
Q: How do I prevent unplanned downtime?
A: PMM
Aside from the
technician, who else
can benefit from
preditive
maintenace? What
other processes can
machine data trigger
for which actors?

6. IoT-Cloud
State Engine
2. Machine Data
9. Alert & Task
Knowledge
Technician
7. Model + Error Code
8. Article
ServiceCloud
Assets
6. Model
5. Location + Error Code
Predictive Model
DB
R
BDLaaS
4. Case
1. Input Stream
PLC Data
Machine Data
Apama
Complex Event Processor
3. Descriptive Stream
EBS
WebMethods
Input Orchestrations
2. Complex Stream
Streaming
Manual
Case
Management
Q: How do I ensure service is performed correctly?
A: PMM w/ VR
Aside from the
technician, who else
can benefit from
preditive
maintenace? What
other processes can
machine data trigger
for which actors?

7. Q: How do I know if my patient is doing well on the new treatment plan?
A: eHealth
Copyright © Capgemini 2013. All Rights Reserved
7CWIN München IoT Capone.pptx
• Blood Pressure
• Heart Rhythm
• Pulse
• Blood Sugar
• EKG
• BMI
• Weight
• Oxygen Saturation
• Movement
• Glucose
• Temperature
• Prescription Adherence
Features
Best-of-Speed
Platform as a Service
Streaming Analytics
Micro-Services
Any Input
Motivation
 Operational Efficiency
 Customer Satisfaction
 New Revenue Model
Benefits
Lower Costs
Shorter Treatment Period
Lower Side Effects
Higher Patient Satisfaction
Aside from the
physician, who else
can benefit from
patient monitoring?
What other
processes can vital
signs and adherence
data trigger for
which actors?

8. Q: How do I satisfy the info needs of partners, customers and users?
A: Micro-Services
Copyright © Capgemini 2013. All Rights Reserved
8CWIN München IoT Capone.pptx
Infrastructure Services
Deliverables
1. Database Hosting
2. Predictive Modelling
3. Streaming Anayltics
4. Dashboards & Reporting
Customer Sales Marketing R&DService
Engineering Services
Competencies
1. Sensor Design & Prototyping
2. Embedded Software
3. Connectivity
4. Gateways, Hubs, Bridges
5. Testing
Application Services
Deliverables
1. App Factory (Development)
2. Rules Engine
3. CRM & Contact Center
4. FieldService
There are many users of IoT data.
These users have different
requirements, they use different
hardware, have different logic,
and want to trigger departmental
specific processes.
An enterprise-ready data lake
consolidates the data from all
„smart appliances“ making it
available to multiple users and
enabling correlative analytics, i.e.
an intelligent home.
Any device can be made „smart“
by retrofitting or embedding
sensors and software. Generating
data is only the first step. The data
has to be validated and
transmitted reliably and securely.
Actors
Drivers
Fleet Managers
Dispatchers
Trainers
Service Stations
Repair Centers
Manufacturer
Motivation
 Operational Efficiency
 Customer Satisfaction
 New Revenue Model
Benefits
Customer Loyalty
User Acceptance
New Revenue Models
Single Point-of-Truth
Lower Operating Costs