In this session we will present the results of two recent, international studies on the state of data analytics in industrial settings. You will get insights from an in-depth industry survey of 151 analytics professionals and decision-makers in industrial companies, providing a deep-dive into strategies, project types, cost structures and skill-demand in IoT-based analytics. In addition we will present a survey focusing on predictive analytics covering the market potential and expected development until 2022.

1. January
2017
Industrial
Analy.cs
2016
/
2017
Frank
Pörschmann
Frank.poerschmann@digital-­‐analy=cs-­‐associa=on.de

2. 2
• The
focus
is
on
the
promo.on
of
data
competency
in
business,
poli=cs
and
society.
• For
more
than
10
years,
the
Digital
Analy=cs
Associa=on
(DAA),
with
over
5,000
members
worldwide,
has
been
suppor=ng
the
professionaliza=on
of
the
new,
data-­‐driven
professional
images,
such
as
the
digital
analyst
and
the
data
scien=st.
• The
Digital
Analy=cs
Associa=on
e.V.
is
con=nuing
this
commitment
as
an
independent
non-­‐profit
organiza.on
under
its
own
na=onal
leadership.
• The
Digital
Analy=cs
Associa=on
e.V.
supports
ins.tu.ons,
specialists
and
execu.ves
in
the
development
of
professional
and
entrepreneurial
skills
for
the
analysis
of
digital
data
streams.

3. 3
§ Qualifica=on
&
Cer=fica=on
§ Promo=on
of
Young
§ Networking
§ Events
§ Research
&
Development
§ Wegweiser
für
Unternehmen
und
Anwender
§ Career
development
and
support
§ Advisory
services(i.e.
data
rights,
project
management,
tooling)
§ Science
&
Educa=on
||
Promo=on
of
Young
§ Business
&
Governance
§ Soware
Producer
||
Agencies
&
Service
Companies
§ Methods
||
Knowledge
Management
§ Interna=onal
||
Networking
§ Marke=ng,
PR
&
Events
||
Members
§ Legal
Ac.vi.es
Subject
MaMer
Expert
Groups
„Professionaliza-on
of
data-­‐driven
professions
for
data
expert
as
well
as
management.
“

4. Global
representa-ve
decision-­‐maker
study
available
for
download
www.IoT-­‐Analy=cs.de
www.digital-­‐analy=cs-­‐associa=on.de

5. The
concept
of
digital
shadow
applies
to
machines
as
well

6. The
most
expecisve
cost
factor
in
business
s.ll
are
bad
decisions
Signals
Data
Informa=on
Decision
Knowledge
Gathering/management/Distribu=on
Analy=cs
Advanced
Analy=cs
&
Data
Science
-­‐ Learning
systems,
ML,
AI
-­‐ Decision
Support
Systems
-­‐ Automated
decision
support
systems
Conversion
/
Distribu=on
Repor=ng
/
Monitoring
o
Daten-­‐Analy=cs
is
not
new
–
but
different
o 60‘S
&
70‘s:
Opera=onal
Monitoring
o 2017:
Decision
Support
o 2025:
Automated
Decision-­‐making

7. Industrial
Analy-cs...
o is
a
key
success
factor
of
Industrial
Internet
(Industrie
4.0)
o Will
become
a
compe==ve
cri=cal
capability
in
industrial
business
o For
most
of
the
companies
integrated
data
analy=cs
is
a
new
organiza=onal
discipline
(approx.
½
within
one
org.-­‐
unit,
mainly
R&D)
o BUT:
30%
report
about
finalized
projects

8. Development
of
data
competencies
already
on
top-­‐management
agenda
o More
than
half
ini=ated
by
CEO
&
COO
Data
is
not
IT!
o Smallest
responsibility
by
typical
technology
management
CTO/CIO
33,1%

9. Value
expecta-ons
on
industrial
analy-cs
mainly
set
on
growth
instead
of
efficiency
o Expected
benefit
from
analy=cs
mainly
in
growth
and
customer
sa=sfac=on
o Expected
growth
by:
o Extending
exis=ng
products
o Expansion
of
exis=ng
business
models
o New
Business
Models
o Cost-­‐reduc=on
and
efficiency
increase?
Rela=vely
weakly
weighted
despite
numerous
successful
projects
33,1%

10. How
good
are
you
at..?
o Over
half
of
the
companies
are
sa=sfied
with
the
ability
to
access
their
data
o But
about
2/3
of
the
companies
fail
in
genera=ng
sufficiently
relevant
findings
from
the
data
obtained.
-­‐>
But
this
is
the
source
of
future
compe==ve
advantage
33,1%

11. Further:
Cost
and
benefit
structures
are
currently
unbalanced
o Main
costs
of
data
projects
in
IT
&
Technology
disciplines
o Business
benefit
is
generated
by
analy=cs.
Analy=cs
costs
account
for
only
about
25%
of
the
total
costs
o Strategies
for
cost-­‐saving
data
architectures
are
already
relevant
€

12. S-ll
one
Use
Case
seem
to
prevail:
Predic-ve
Maintenance
PdM)

13. Comparison
of
maintenance
approaches
Deep-­‐dive:
Condi=on
Based
Maintenance
vs.
Predic=ve
Maintenance
Copyright
©
2016
by
www.iot-­‐analy=cs.com
All
rights
reserved
13
Condi.on-­‐based
maintenance
Predic.ve
Maintenance
Mobile
Condi-­‐
.on
Monitoring
Online
Condi.on
Monitoring
Sta.s.cs-­‐based
PdM
Stochas.c-­‐based
PdM*
Sensing
Technology
Handheld
Device
Integrated
Sensors
Integrated
Sensors
Integrated
Sensors
Monitoring
Frequency
In
regular
intervals
/
on
demand
Constantly
Constantly
Constantly
Visualiza.on
On
specific
device
Online
/
Mobile
Online
/
Mobile
Online
/
Mobile
IT-­‐Architecture
On-­‐Premise
On-­‐Premise
or
Cloud
On-­‐Premise
or
Cloud
On-­‐Premise
or
Cloud
Real-­‐.me
monitoring
Combina.on
of
data
sources
Analy.cs
Sta=s=cs1
Stochas=cs2
Maintenance
Trigger
If
monitoring
shows
cri=cal
values
If
monitoring
shows
cri=cal
values
When
calculated
health-­‐
score
reaches
cri=cal
value
When
failure
is
predicted
to
occur
a.
b.
a.
b.
*
=
Some=mes
also
referred
to
as
“prognos=cs”
1.
Sta=s=cs
=
Using
sta=s=cal
methods
such
as
SPSS,
regression
2.
Stochas=cs
=
Using
stochas=cal
models
such
as
Bayesian
Networks,
etc.

14. Most
challenging
issues
o Security
remains
strong
obstacle
o However,
the
biggest
hurdles:
o Interoperability
of
systems
o Quality
of
the
data
o Insight
genera=on
by
lack
of
specialists,
skills,
methods,
tools…
33,1%

15. A
rapid
shiK
of
tools
requires
new
skills
and
capabili-es
o The
end
of
spreadsheet
analy=cs
o Rapid
change
of
tools
and
playorms
to
be
experienced
within
5
years
o Importance
of
predic=ve
analy=cs
tools
will
double
o BI
relevance
increases
as
well
33,1%

16. Which
approach
to
use?
Freestyle
or
structured?
o About
2/3
work
on
hypothesis
from
the
begin
o S=ll
1/3
allows
for
gaining
insight
in
their
own
data
33,1%

17. Cri-cal
skill
gap
ahead
Warning
Data
Science
-­‐
Data
Scien=st,
Data
Engineers
-­‐ IT
(Developer,
Architects,
SI,
Infrastructur
(M2M)
-­‐ Agile
PM
-­‐ Industrial
process
know-­‐how
Companies
fail
in
integra=ng
adequate
new
digital
professions
Only
5
years
to
go
un=l
skill
gap
impacts
compe==ve
capability
33,1%

18. Digital
sovereignty
...?
o Promo=on
of
data
competency
among
specialists
and
execu=ves
is
crucial
for
Europe’s
industrial
strategy
o The
German
educa=on
system
in
new
data-­‐driven
professions
is
interna=onally
not
compe==ve.
o Companies
must
take
on-­‐the-­‐job
qualifica=on
and
interna=onalize
skills.
33,1%
HandcraKs
have
many
faces,
so
does
data
art.

19. 19
Contact
Frank
Pörschmann
Crémon
36
20457
Hamburg
0171
–
30
579
20
Frank.Poerschmann@digital-­‐analy=cs-­‐associa=on.de
www.didital-­‐analy=cs-­‐associa=on.de
www.digitalanaly=csassocia=on.org