Wie schaut der Prozess für Customer Insights im weiteren Sinne aus? Welche Aspekte sind dabei zu berücksichtigen? Welche technologischen und Architektur-Ansätze können hierbei unterstützen? Angefangen vom Aufbau einer 360 Grad Sicht auf einen eindeutig identifizierten Kunden unter Berücksichtigung des Datenschutzes über die eigentliche Analyse der Kundeninteraktion – in Echtzeit oder nachgelagert – bis hin zur erfolgreichen Nutzung der erzielten Kundenkenntnis in der Interaktion mit dem Kunden.

1. Capgemini Week of
Innovation Networks 2017
Customer Insights Prozess
28. September 2017

2. Der Customer Insights Prozess ist eng mit der Customer
Journey verzahnt
Copyright © Capgemini 2017. All Rights Reserved
2CWIN17-Customer Insights Prozess.pptx
Customer
Insights
Customer
Journey

3. Der Customer Insights Prozess im Überblick
Touch-
point
Daten-
beschaffung
Daten-
schutz
Daten-
haltung
Daten-
sicherheit
360 Grad Sicht
Erkenntnis
Ethik
Erkenntnis-
verwendung
Copyright © Capgemini 2017. All Rights Reserved
CWIN17-Customer Insights Prozess.pptx 3

4. Ansätze zur Unterstützung des Customer Insights
Prozesses
 Data Ingestion
 Streaming / Event-
Processing
 Drittanbieterdaten
 Open Data
 Anonymisierung,
Pseudonymisierung
 Berichtsprozesse
für Regularien
 Big Data
 NoSQL
 Anomalie-
erkennung
 Stammdaten-
Mgmt.
 Daten-
virtualisierung
 Predictive Analytics
 Machine Learning
 Artificial Intelligence
 Business Process Integration
 Customer Relationship Mgmt.
 User Experience
Copyright © Capgemini 2017. All Rights Reserved
CWIN17-Customer Insights Prozess.pptx 4
Touch-
point
Daten-
beschaffung
Daten-
schutz
Daten-
haltung
Daten-
sicherheit
360 Grad Sicht
Erkenntnis
Ethik
Erkenntnis-
verwendung

5. Datenbeschaffung
Copyright © Capgemini 2017. All Rights Reserved
CWIN17-Customer Insights Prozess.pptx 5
Touch-
point
Daten-
beschaffung
Daten-
schutz
Daten-
haltung
Daten-
sicherheit
360 Grad Sicht
Erkenntnis
Ethik
Erkenntnis-
verwendung
 Data Ingestion
 Streaming / Event-
Processing
 Drittanbieterdaten
 Open Data

6. 6Copyright © Capgemini 2017. All Rights Reserved
CWIN17-Customer Insights Prozess
Conceptual perspective on reference architecture for Insights&Data
Manage
Process
Analyze
Act
Information
Source
data
Insight
Value
Explorative
Data Exploration
Descriptive
Reporting
Diagnostic
Ad-hoc Querying
Predictive
Data Mining,
Machine Learning
Prescriptive
Next Best Action
Search
Retrieval
 Data governance
and security
 Data privacy
 Compliance
 Collaboration
 Value generation
 Program delivery
 Data-driven culture
 Information strategy
 Skill development
 Master data mgmt
 Metadata mgmt
 Data quality mgmt
 Operations, SLA’s
 Orchestration
Stream
Structured data
 IT managed applications (ERP, SCM, CRM)
 Master and reference data
 Business owned informal data
 Third party data
Unstructured Data
 Social
 Documents, mail, images, voice,
video
Semistructured data
 Internet
 Internet of Things (machine, sensor)
 Server logs
 B2B
Business ApplicationsBusiness ProcessesDecision makers
Data at rest Data in motion
Data Warehouse Data Asset
Catalog
Load
Extract
Transform
Manage Quality
Aggregate
Historize
Data Lake
Business rules Predictive modelsBusiness results Alerts Signals
Catalog
Mask
Store
Ingest
1) Load ‘as-
is’
2) Distill on
demand
Prepare
Blend
Lifecycle
Refine
3) Operationalize
Planning
Forecast,
Simulation
ETL

7. 7Copyright © Capgemini 2017. All Rights Reserved
CWIN17-Customer Insights Prozess
Lambda, Kappa, Streaming Architecture
Objectives
 Process infinite data sets
 Reduce time-to-insight for those
 Flexibility in consumption of this data
 Historize data
 Recoverable from (logical) errors
 “Beat the CAP theorem”
• Low latency
• High availability
  Buzz word alert : Near Real Time
system
Foundation
 Event sourcing
 Making use of immutable events
 Compose all business logic out of them
What is it good for?

8. 8Copyright © Capgemini 2017. All Rights Reserved
CWIN17-Customer Insights Prozess
Lambda architecture (2011) – What does it look like?
https://www.mapr.com/developercentral/lambda-architecture
https://www.oreilly.com/ideas/questioning-the-lambda-architecture

9. Datenschutz
 Anonymisierung,
Pseudonymisierung
 Berichtsprozesse
für Regularien
Copyright © Capgemini 2017. All Rights Reserved
CWIN17-Customer Insights Prozess.pptx 9
Touch-
point
Daten-
beschaffung
Daten-
schutz
Daten-
haltung
Daten-
sicherheit
360 Grad Sicht
Erkenntnis
Ethik
Erkenntnis-
verwendung

10. EU General Data Protection Regulation (GDPR) in a
nutshell, enforced at end of May 2018
Objectives
• harmonize data
protection across
Europe
• strengthen data
protection for EU
Citizens
Scope
• EU citizen’s data
• Entities from all
countries processing
EU citizen’s data
Sensitive Data
• racial, ethnic origin
• political opinions
• philosophical,
religious beliefs
• health or sex life
• sexual orientation
• genetic, biometric
Data Breach
• third party gains
unauthorised access to
personal data
• must be reported
without undue delay
Responsibilities
• data protection officer
• Data protection impact assessment
• workforce privacy awareness training
• data protection policies
• Data protection by design
• Record of data processing activities
Penalties
for non-compliance
• up to 4% of worldwide
turnover or 20 million €
Rights
• Erase records
• Data portability
• Explain automated
decisions
• Dissent data profiling
Consent
• permits types of
processing
• to be provided explicitly
(opt-in)
• can be withdrawn
Principles
• Lawful, fair,
transparent
• Purpose limitation
• Data minimisation
• Accuracy
• Storage limitation
• Integrity, confidential
• Accountability
Data Processing
• Collecting, storing
• Altering , deleting
• Retrieving
• Transmitting
Data Subject
natural person
• Client
• employee
Supervisory authority
Identification
• name, id. number,
location or physical,
physiological,
genetic, mental,
economic, cultural or
social identity factors
Personal Data
• any information
relating to an identified
or identifiable natural
person
Data Controller Data Processor
• Service provider

11. Datenhaltung
 Big Data
 NoSQL
Copyright © Capgemini 2017. All Rights Reserved
CWIN17-Customer Insights Prozess.pptx 11
Touch-
point
Daten-
beschaffung
Daten-
schutz
Daten-
haltung
Daten-
sicherheit
360 Grad Sicht
Erkenntnis
Ethik
Erkenntnis-
verwendung

12. 12Copyright © Capgemini 2017. All Rights Reserved
CWIN17-Customer Insights Prozess
Manage
Process
Analyze
Information
Source
data
Data ExplorationReporting
Ad-hoc
Querying
Search,
Retrieval
Structured data
 tables
Unstructured Data
 Text, speech, …
Semistructured data
 JSON, XML, …
Implementation perspective on reference architecture for Big Data
Analytics
Data Warehouse Data Asset
Catalog
 Index
 Tags
 Metadata
Data Lake
Analytical
Sandbox
NoSQL databases
Key value
store
Document
store
Column
store
Graph
store
NLP
SQL databases
Row
based
Column
based
Streaming,
Event
Processing
File
system
Analytics
Base
Tables
Data Mining,
Machine Learning
Next Best
Action
High level ingestion and data
preparation
Low level
ingestion
ETL/ELT
Adv. VisualizationSQL
SQL
Java
Scala
Python
R
Batch Processing
Data virtualization
Eventbased
MicroBatch

13. 13Copyright © Capgemini 2017. All Rights Reserved
CWIN17-Customer Insights Prozess
Mapping components for data storage to the reference architecture for
Big Data Analytics
HDFS, MapR-FS,
IBM GPFS
HBase,
Cassandra,
Cloudera
Kudu,
Accumulo
JackRabbit Giraph
Google file
system
Google Big
Table,
MemSQL
MongoDB,
CouchDB,
MarkLogic,
IBM
Cloudant
Redis,
Voldemort,
BerkeleyDB,
Oracle
NoSQL,
MemcacheDB
Neo4J, OrientDB,
Titan, Virtuoso,
ArrangoDB,
Oracle Spatial,
Teradata Aster

14. Datensicherheit
 Anomalie-
erkennung
Copyright © Capgemini 2017. All Rights Reserved
CWIN17-Customer Insights Prozess.pptx 14
Touch-
point
Daten-
beschaffung
Daten-
schutz
Daten-
haltung
Daten-
sicherheit
360 Grad Sicht
Erkenntnis
Ethik
Erkenntnis-
verwendung

15. SIEM and GRC can not prevent any type of data breach -
we need a different approach
Anomalous Behavior
Traditional approaches need to be
complemented – SIEM, GRC are still needed
GRC says what is approved – the tasks you
can do, the gates you can go through.
Abnormal Behavior Detection says whether you
should have
Extend using Anomalous Behavior Detection:
This approach:
1. Learns what is normal [the difference between
approved and allowed]
2. Identifies what is anomalous and categorizes
the risk
3. Alerts so you can react before it becomes a
problem.
New Outcomes are Possible
It is an extension of current security
approaches that enables a reduction in GRC
and can identify threats that GRC cannot
 It shows where “allowed” is not “normal” and
the scope of the deviation from the norm.
 Detect social engineering attacks as well as
network level detections
 Minimize the exposure time and loss
 Potentially predict the leakage areas ahead of
the attack
 This can be applied to both GRC areas
(Snowden) and non-GRC areas (networks,
non-controlled information) to build up a
broader pattern of behavior.

16. Detection of Anomalous Behavior – from Insight to Action
Structured data Machine learning
defines “normal”
across user base
Inform management Adjust policies Lockdown
SIEM
AD
HR
Unstructured data
Images
Social
Email
Video
Automated response based on level of deviation and system criticality
Users accessing key systems within role as defined by GRC
Deviation
from norm
triggers
action

17. 1
Out of policy
access
In policy but
extremely
abnormal access
3
2
In policy but
abnormal access
Examples – SIEM, GRC and Detection of Anomalous
Behavior
User tries to access what
they shouldn’t
GRC says “no”,
notifies SIEM
SIEM collates, alerts, may
reduce privileges via GRC/IAM
User accesses single item out of
norm but in policy
GRC says
“yes”
AB ‘but that isn’t normal’,
alert to SIEM
SIEM collates, alerts, may
reduce privileges via GRC/IAM
User accesses multiple areas
out of ordinary but in policy
GRC says
“yes”
AB ‘this is the ONLY person
EVER to do this!’ alert to SIEM
Shutdown of user
access + manager alerts

18. 360 Grad Sicht
 Stammdaten-
Mgmt.
 Daten-
virtualisierung
Copyright © Capgemini 2017. All Rights Reserved
CWIN17-Customer Insights Prozess.pptx 18
Touch-
point
Daten-
beschaffung
Daten-
schutz
Daten-
haltung
Daten-
sicherheit
360 Grad Sicht
Erkenntnis
Ethik
Erkenntnis-
verwendung

19. Alternative Architekturansätze zur Bildung einer 360 Grad
Sicht
Copyright © Capgemini 2017. All Rights Reserved
19CWIN17-Customer Insights Prozess.pptx
Master Data
Management
Data
Virtualization
Big Data /
Data Lake

20. Erkenntnisse durch Analytics
 Predictive Analytics
 Machine Learning
 Artificial Intelligence
Copyright © Capgemini 2017. All Rights Reserved
CWIN17-Customer Insights Prozess.pptx 20
Touch-
point
Daten-
beschaffung
Daten-
schutz
Daten-
haltung
Daten-
sicherheit
360 Grad Sicht
Erkenntnis
Ethik
Erkenntnis-
verwendung

21. The terms “AI”, “Machine Learning” and “Deep Learning”
form an abstraction hierarchy
Artificial Intelligence
Machine Learning
Neural Networks
Deep Learning
Artificial Intelligence
General term
Machine Learning
Sub-field of research
Neural Networks
Computational
model building approach
Deep Learning
Class of algorithms for
specific kinds of NNs
Copyright © Capgemini 2017. All Rights Reserved
CWIN17-Customer Insights Prozess.pptx 21

22. Cognition
Deep
learning
Image
analysis
Knowledge
engineering
Natural
language
generation
Machine
learning
Robotics
Sensory
perception
Natural
language
processing
Think
Act
Sense
Speech
recognition
Artificial Intelligence has different technological building
blocks working together to form a learning cycle
Copyright © Capgemini 2017. All Rights Reserved
22CWIN17-Customer Insights Prozess.pptx

23. Ethische Aspekte der Nutzung von Daten und abgeleiteten
Erkenntnissen
Copyright © Capgemini 2017. All Rights Reserved
CWIN17-Customer Insights Prozess.pptx 23
Touch-
point
Daten-
beschaffung
Daten-
schutz
Daten-
haltung
Daten-
sicherheit
360 Grad Sicht
Erkenntnis
Ethik
Erkenntnis-
verwendung

24. Bei der Nutzung von Erkenntnissen aus Big Data Analytics müssen
ethische Aspekte geprüft werden
Copyright © Capgemini 2017. All Rights Reserved
24
Mit Daten sollte sparsam
umgegangen werden
Die Big Data Anwendung
muss transparent gemacht
werden
Pauschalisierung darf
Einzelpersonen nicht
benachteiligen
Betroffenen sollte Mitsprache
bezüglich der eigenen Daten
ermöglicht werden
Die Anwendung muss Nutzen
für Betroffene, Kunden oder
Gesellschaft stiften
Daten der Betroffenen müssen
vor Missbrauch geschützt
werden
Gesetzestreue ist Grund-
voraussetzung für ethisch
korrektes Verhalten
Nutzen
Individuelle Prüfung
Ethisch korrekte
Anwendung von
Big Data
Transparenz
Chancen-
gleichheit
Datensparsamkeit
Mitbe-
stimmung
Gesetz
Daten-
sicherheit
Nutzen

25. Der Customer Insights Prozess im Überblick
Touch-
point
Daten-
beschaffung
Daten-
schutz
Daten-
haltung
Daten-
sicherheit
360 Grad Sicht
Erkenntnis
Ethik
Erkenntnis-
verwendung
Copyright © Capgemini 2017. All Rights Reserved
CWIN17-Customer Insights Prozess.pptx 25

26. Über Capgemini
Mit mehr als 140.000 Mitarbeitern in über 40 Ländern ist
Capgemini einer der weltweit führenden Anbieter von
Management- und IT-Beratung, Technologie-Services sowie
Outsourcing-Dienstleistungen. Im Jahr 2013 betrug der Umsatz
der Capgemini-Gruppe 10,1 Milliarden Euro.
Gemeinsam mit seinen Kunden erstellt Capgemini Geschäfts- wie
auch Technologielösungen, die passgenau auf die individuellen
Anforderungen zugeschnitten sind. Auf der Grundlage seines
weltweiten Liefermodells Rightshore® zeichnet sich Capgemini
als multinationale Organisation durch seine besondere Art der
Zusammenarbeit aus – die Collaborative Business ExperienceTM.
Rightshore® ist eine eingetragene Marke von Capgemini
Die in der Präsentation enthaltenen Informationen sind Eigentum.
Copyright © 2016 Capgemini. Alle Rechte vorbehalten.
www.de.capgemini.com