Dr. Boris Adryan gave a talk on the impact of IoT analytics on development budgets. He discussed that IoT data problems are often not as complex as perceived and do not necessarily require "big data" solutions or specialists. Basic data storage and processing can often be done cost-effectively using standard tools. True challenges lie in extracting useful insights, which may require specialized machine learning approaches. Not all analytics need to be real-time. The appropriate solution depends on the use case and desired insights.

1. Impact of IoT analytics on
the development budget
Dr. Boris Adryan
@BorisAdryan
Industry of Things World, Berlin, 19th September 2016

2. Dr. Boris Adryan
• with Zühlke Engineering since September 2016
• longstanding IoT enthusiast
• Founder of thingslearn Ltd.
• Board Member & Strategic Advisor for Pycom
(microcontrollers), BioSelf (biosensors) and
OpenSensors (IoT platform)
• before: research group leader for data analytics
and machine learning at University of
Cambridge, England.
@BorisAdryan

3. I disagree with the notion that data is
the new oil. It’s as infinite as the sun,
and just like the power of the sun,
we’re barely using it at the moment.
Mike Gualtieri,
Forrester Research
“
”

4. 5V of Big Data
Velocity
Veracity
Volume
Variety
Value
“doesn’t fit on my
local drive”
“process deals with
hundreds of events
per second”
“wouldn’t even know
how to save this in a
RDBMS”
“actionable insight”
“not sure how current,
valid or complete it is”

5. It’s worth to look at the actual data problem before hiring
a ‘big data specialist’ or buying an ‘analytics solution’.
IoT = Big Data
Sensor devices produce large
and small data.
You may not immediately
know how to deal with them -
but that doesn’t automatically
make them ‘big data’.

6. 39% of survey participants
are worried about the cost
of an industrial IoT
solution.
“Why aren’t you doing IoT?”

7. Hardware is often perceived as
investment that customers
understand and therefore
anticipate.
This talk is about unfounded IoT fears.
There’s an air of magic around
data and analytics.
This leads to fear of:
• having to hire specialists
(for both data plumbing and analytics)
• having to buy expensive services
• losing control over the process due to a lack
of understanding

8. data
You want actionable insight.
data
data
here be
dragons!
whatever you do
in your vertical
✓better
✓faster
✓cheaper
insight
“magic”
how to deal and what
to do with the data

9. ✓small (fits on your drive)
✓you know exactly what you’re looking for
not a ‘data problem’
ask your programmer
✓large (think data centre-scale)
✓you know exactly what you’re looking for
potentially ‘big data’
ask your sysadmin,
then your programmer
Do you need to employ a specialist?
data
data

10. Let’s talk about IoT and the cloud

11. You have a choice.
Actually, too much of it.

13. “My data problem must be special!”
✓ unstructured data
✓ distributed ingestion and storage
My company went to
an IoT conference
&
all I got was this t-shirt
and a bunch of
buzzwords.
Customers fear costs because
they’re facing:
Or they believe from hear-say
that IoT automatically
requires:
✓ real-time analytics
✓ sophisticated machine learning

14. “I receive U NsT Ruc Tur data!”De
RDBMS
name age
Boris 40
name city job
Boris Fra… IoT
key-value DBs
name: Boris
age: 40
city: Frankfurt
name: Boris
job: IoT / data science
name: Ilka
age: 39 name: Ilka
city: Frankfurt
job: pharma R&D
SQL-ish syntax
not a ‘big data’ nor a ‘cloud’ problem
NoSQL DBs run on commodity hardware

15. thing thing thing
time
thing thing thing
thing
thing
thing
thing
thing
thing
thing
broker
broker
broker
broker storage
storage
storage
even standard cloud offerings can do
distributed ingestion and storage very well
“I got too many things!”
not a big data ‘problem’

16. Your apps &
corporate design
Your products
and analytical
services
Your devices
Adapting a PaaS to your needs.
Security
I/O / broker fast storage
device
management
gateway
portal
& user
management
basic
analytics
Zühlke IoT Platform
standard components
(still, tedious to configure)
your USP

17. data
You want actionable insight.
data
data
here be
dragons!
whatever you do
in your vertical
✓better
✓faster
✓cheaper
insight
“magic”
how to deal and what
to do with the data
Basic data plumbing and storage is usually not the issue.

18. The message is that there are known knowns.
There are things we know that we know.
There are known unknowns. That is to say
there are things that we now know we don't
know. But there are also unknown unknowns.
There are things we don't know we don't
know.
Donald Rumsfeld
ex US Secretary of Defense
“
”

19. ✓small or large
✓you don’t know what to connect or how to
find it (the “known unknowns”)
✓you want to increase operational awareness
(the “unknown unknowns”)
a ‘data science problem’
We can help to establish a machine learning
pipeline to extract relevant information
automatically.
data
data
data
data
datadata
data
data
data
data
Do you need to employ a specialist?
you may just need a
one-off solution

20. unsupervised learning - get an
overview what’s in your data set
supervised learning - teach the
machine to classify data on the basis
of some previous training
statistical analysis - find rules and outliers
on the basis of numerical data
What is machine learning?
?
y
4 n n 0
2 n n 1
4 y y 4
6 y y 9
6 y y 2
skates
bike
car
bus
lorry
wheels
motor
windows
seats
very relevant for predictive
maintenance etc.

21. data
weather forecast
airport location
# of gates
# of runways
# of snowploughs
airline
aircraft
BLACK
BOX
training
flights cancelled in
the past
classifier
ranked list of
relevant features
weight of features
thresholds for
features
performance metric
prediction
new data
How does classification work?

22. training
classifier
performance
assessment
good enough?
success!
moredatafortraining
data
no
yes
Is this reliable?
sensitivity
“truepositives”
1-specificity
“false positives”
0 0.2 0.4 0.6 0.8 1.0
1.0
0.8
0.6
0.4
0.2
worse than
random guess

23. data
Where is your classifier located?
data
data
here be
dragons!
whatever you do
in your vertical
insight
“magic”
model building
training
operation
performance tracking
on device, cloud
or mobile app
} R & D
}
✓better
✓faster
✓cheaper

24. Is analytics just data crunching?

25. sound profile
assessment result
“predictive
maintenance
classifier”

26. “Do I need real-time analytics?”
microseconds
to seconds
seconds to
minutes
minutes
to hours
hours to
weeks
on
device
on
stream
in batch
am I falling?
counteract
battery level
should I land?
how many
times did I
stall?
what’s the best
weather for
flying?
in process
in database
operational insight
performance insight
strategic insight
e.g. Kalman filter
e.g. with machine learning
e.g. rules engine
e.g. summary stats

27. Can IoT ever be real-time?
zone 1:
real-time
[us]
zone 2:
real-time
[ms]
zone 3:
real-time
[s]

28. Edge, fog and cloud computing
Edge
Pro:
- immediate compression from raw
data to actionable information
- cuts down traffic
- fast response
Con:
- loses potentially valuable raw data
- developing analytics on embedded
systems requires specialists
- compute costs valuable battery life
Cloud
Pro:
- compute power
- scalability
- familiarity for developers
- integration center across
all data
Con:
- traffic
Fog
Pro:
- same as Edge
- closer to ‘normal’ development work
- gateways often mains-powered
Con:
- loses potentially valuable raw data

29. Some of our examples for
real-time analytics
Choosing the appropriate
method and toolset on
every level.

30. Options for cloud-based real-time analytics
some features can cost a bit, especially
when you don’t really know what
you’re doing and want to ‘try it out’.
a badly configured
SMACK stack on your
own commodity
hardware can be slow
and unreliable
your pre-trained
classifier

31. My current pet hate: Deep Learning
Deep learning has delivered impressive
results mimicking human reasoning,
strategic thinking and creativity.
At the same time, big players
have released libraries such
that even ‘script kiddies’ can
apply deep learning.
It’s already leading to unreflected use of deep learning when
other methods would be more appropriate.

32. Dr. Boris Adryan
@BorisAdryan
‣ Super-fast analytics and state-of-the-art methods
are not automatically the most useful solution.
‣ A good understanding on the type of insight that is
required by the business model is essential.
‣ There are many solutions readily available that
might enable IoT projects very cost-effectively.
Zühlke can advise on your options around
IoT and data analytics, and provide
complete solutions where needed.
Industry of Things World, Berlin, 19th September 2016
Summary