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Big Data: IT does matter
Dr. Sven Niedner, Jettainer
eMail: sven.niedner@jettainer.com, Twitter: S_N_I
London, November, 11th 2013
1
Still true?
Who we are and why IT matters for us





The leading ULD management companies
Reliable partner for 13 airlines wordlwide
100% owned by Lufthansa Cargo







ULD fleet acqusition and lease back
Savings potential enabled by intelligent asset tracking
Over 60k air cargo ULDs – largest fleet in the world
Active control concept (forecast-based)
Zero offloads since …






Role of IT: IT products are a differentiator
Jettware software for ULD management
Used at 350+ stations worldwide
Covers full ULD fleet lifecycle
Our customers

ULD management customers

+ JettLease short term lease

Our USP: 20% fleet size reduction due to our active control concept
Business questions

 Where are my ULDs?

 Where do I have to reposition my ULDs?
 How will the demand develop at an airport?

 Will I survive the next holiday season without any
offloads?

 Is my fleet to big?
 Whom can I borrow ULDs from? (Smart Pooling!)
Innovation is key for success – your Business questions will change

EASY
 Where the hell is that overstock coming from?
 How many ULDs did not move in the last X days?
HARDER
 How did my mean time between repair develop?
 How did the demand develop in the last year before
christmas?
VERY HARD
 Has there been a similar situation as today occured
before and how was it resolved?
 I want to introduce this crazy new KPI, but what was
it„s value last year?

Access to real
time data

Access to
historic data

Link real-time
data and historic
data
Jettware overview

Strategic

Tactical

Operational

JettStart: Demand simulation and
capacity planning

UCD Cockpit: Stock monitoring &
ULD positioning/move requests

2011

2008

Management Cockpit:
Overall performance monitoring

2012

Revised ULD Tracking Application
& DWH reporting tools

2012

Jettware: UCMs, LUCs and stock
checks at stations

2005
JettApp: Digital ULD tracking for
the ground handler

2013
<PARENTHESIS TOPIC=“mobile“>
Real world example: Mobile drives the hunger for realtime Big Data
Mobile devices collect more information than ever
 Not only the NSA, but also your company!
 Changing user beviour: Using application “on-the-go”, not only
“on premise”
People expect real time response & interactive UIs
 User expectations are set by Apple and Google
 Responses need to come within seconds
 You cannot satisfy your users with inferior quality
You think it’s different because your uses are your employees?
 Dream on!
 The pressure will come – from your users
 Think BYOD – consumer goods set the expectation level!
 These guys (Apple, Samsung, Sony, …) are fast and aggressive
 You will have to compete

JettApp device sends
ULD messages from the
ware house in real time






Increased data quality
More information, more
events
Tablet PC
Running apps
Build on Android
</PARENTHESIS >
How to do it?
How to do it

Keep all your data.
Discard nothing.
Architecture
is important
Challenges

implement like:
query = function ( all data )
Keep all your data.
Forever.

http://www.flickr.com/photos/jblndl/3326092093/sizes/l/in/photostream/
So where is “Big Data”?

 30.000 ULD movements per day, 100.000 messages per day (mostly SITA)
 Business is driven by data:
 ULD inventories
 stocks at stations
 flight plans and changes

 ULD histories back to 2005
 20 TB+ (and growing)
 >10.000.000.000 data sets
 Input mostly SITA messages: varying data quality and compliance
 … and our ULD control desk needs to act in real time
Challenges for IT

Really be real big!30.000 50 million USD
fast: (cargo ship) + $
It needs to
It needs to be real fast! (drag boat) + 20.000 USD

Conflicting goals  high cost (aircraft carrier): 9 Billion USD

Foto by Bengt Nymann, http://www.flickr.com/photos/bnsd/6139750090
Challenges for IT

Really be real big! (cargo ship) + 50 million USD
big: 100.000.000 $
It needs to
It needs to be real fast! (drag boat) + 20.000 USD

Conflicting goals  high cost (aircraft carrier): 9 Billion USD

Foto by cuxclipper, http://www.flickr.com/photos/36288065@N04/4949196610
Challenges for IT

It needs to be real big! (cargo ship) + 50 million USD

It needs to be real fast! (drag boat) + 20.000 USD

Conflicting goals  high cost (aircraft carrier): 9 Billion USD

Really fast and really big:
10.000.000.000 $
Foto by US Navy, http://www.flickr.com/photos/usnavy/5781645295
Really clever: The Lambda architecture pattern

Combining real-time and batch procssing:
Realtime
OUTPUT
View
Speed layer

INPUT

Batch
View
Batch
Layer

Serving
Layer

Lambda architecture proposed by Nathan Marz,Twitter

OUTPUT

Data
Views,
Reports,
Apps
Really clever: The Lambda architecture pattern

Realtime
View

OUTPUT

Data Views,
Reports,
Apps

Speed layer

INPUT

Batch
View
Batch
Layer

query =
function(all data)

Serving
Layer

OUTPUT

Design principles:
 Human fault tolerance
 Data immutability
 Recomputation
Different applications have different focus

Strategic
JettStart: Demand simulation and
capacity planning

Tactical
UCD Cockpit: Stock monitoring &
ULD positioning/move requests

Operational
Jettware: UCMs, LUCs and stock
checks at stations

Focus: real-time

Management Cockpit:
Overall performance monitoring

Focus: historic data

Focus: Application DWH
ULD Tracking historic&data
reporting tools

Recommender System

JettApp: Digital ULD tracking for
the ground handler
Build a mental model of you
business domain

And then map it on your
existing data structures

http://www.flickr.com/photos/alreadytaken/2136780794/
Think beyond the obvious
Go for second order effects:
 Network effects
 Correlations
 non-linear phenomenons
Enhanced Business domain insight

 Accept unreliable and
inconsistent data as a
fact
 You cannot fix the quality
of historic data

 Statistics will help you to
detect or cancel out
errors
 Have realistic expectations
regarding the Data Scientist role
 Nice idea in theory, but (almost)
impossible to find in reality
 Better kiss the frog now than
wait for the prince forever

http://www.flickr.com/photos/fcstpauli/2067908929/sizes/l/in/photostream/
Don’t loose your business focus.
For us it’s all about…
1. Demand vs. Supply

2. Overstock vs. Understock (balance network)
3. Demand forecast and tactical ULD movements
4. Strategic movements
<BACKUP >
<PARENTHESIS TOPIC=“agile“>
Implement lean and agile development tactics

The agile manifesto:
Individuals and interactions …
… over processes and tools
Working software …
… over comprehensive documentation
Customer collaboration …
… over contract negotiation
Responding to change …
… over following a plan
</PARENTHESIS >

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Big Data: IT does matter

  • 1. Big Data: IT does matter Dr. Sven Niedner, Jettainer eMail: sven.niedner@jettainer.com, Twitter: S_N_I London, November, 11th 2013 1
  • 3. Who we are and why IT matters for us    The leading ULD management companies Reliable partner for 13 airlines wordlwide 100% owned by Lufthansa Cargo      ULD fleet acqusition and lease back Savings potential enabled by intelligent asset tracking Over 60k air cargo ULDs – largest fleet in the world Active control concept (forecast-based) Zero offloads since …     Role of IT: IT products are a differentiator Jettware software for ULD management Used at 350+ stations worldwide Covers full ULD fleet lifecycle
  • 4. Our customers ULD management customers + JettLease short term lease Our USP: 20% fleet size reduction due to our active control concept
  • 5. Business questions  Where are my ULDs?  Where do I have to reposition my ULDs?  How will the demand develop at an airport?  Will I survive the next holiday season without any offloads?  Is my fleet to big?  Whom can I borrow ULDs from? (Smart Pooling!)
  • 6. Innovation is key for success – your Business questions will change EASY  Where the hell is that overstock coming from?  How many ULDs did not move in the last X days? HARDER  How did my mean time between repair develop?  How did the demand develop in the last year before christmas? VERY HARD  Has there been a similar situation as today occured before and how was it resolved?  I want to introduce this crazy new KPI, but what was it„s value last year? Access to real time data Access to historic data Link real-time data and historic data
  • 7. Jettware overview Strategic Tactical Operational JettStart: Demand simulation and capacity planning UCD Cockpit: Stock monitoring & ULD positioning/move requests 2011 2008 Management Cockpit: Overall performance monitoring 2012 Revised ULD Tracking Application & DWH reporting tools 2012 Jettware: UCMs, LUCs and stock checks at stations 2005 JettApp: Digital ULD tracking for the ground handler 2013
  • 8.
  • 9.
  • 11. Real world example: Mobile drives the hunger for realtime Big Data Mobile devices collect more information than ever  Not only the NSA, but also your company!  Changing user beviour: Using application “on-the-go”, not only “on premise” People expect real time response & interactive UIs  User expectations are set by Apple and Google  Responses need to come within seconds  You cannot satisfy your users with inferior quality You think it’s different because your uses are your employees?  Dream on!  The pressure will come – from your users  Think BYOD – consumer goods set the expectation level!  These guys (Apple, Samsung, Sony, …) are fast and aggressive  You will have to compete JettApp device sends ULD messages from the ware house in real time      Increased data quality More information, more events Tablet PC Running apps Build on Android
  • 13. How to do it?
  • 14. How to do it Keep all your data. Discard nothing.
  • 16. Challenges implement like: query = function ( all data )
  • 17. Keep all your data. Forever. http://www.flickr.com/photos/jblndl/3326092093/sizes/l/in/photostream/
  • 18. So where is “Big Data”?  30.000 ULD movements per day, 100.000 messages per day (mostly SITA)  Business is driven by data:  ULD inventories  stocks at stations  flight plans and changes  ULD histories back to 2005  20 TB+ (and growing)  >10.000.000.000 data sets  Input mostly SITA messages: varying data quality and compliance  … and our ULD control desk needs to act in real time
  • 19. Challenges for IT Really be real big!30.000 50 million USD fast: (cargo ship) + $ It needs to It needs to be real fast! (drag boat) + 20.000 USD Conflicting goals  high cost (aircraft carrier): 9 Billion USD Foto by Bengt Nymann, http://www.flickr.com/photos/bnsd/6139750090
  • 20. Challenges for IT Really be real big! (cargo ship) + 50 million USD big: 100.000.000 $ It needs to It needs to be real fast! (drag boat) + 20.000 USD Conflicting goals  high cost (aircraft carrier): 9 Billion USD Foto by cuxclipper, http://www.flickr.com/photos/36288065@N04/4949196610
  • 21. Challenges for IT It needs to be real big! (cargo ship) + 50 million USD It needs to be real fast! (drag boat) + 20.000 USD Conflicting goals  high cost (aircraft carrier): 9 Billion USD Really fast and really big: 10.000.000.000 $ Foto by US Navy, http://www.flickr.com/photos/usnavy/5781645295
  • 22. Really clever: The Lambda architecture pattern Combining real-time and batch procssing: Realtime OUTPUT View Speed layer INPUT Batch View Batch Layer Serving Layer Lambda architecture proposed by Nathan Marz,Twitter OUTPUT Data Views, Reports, Apps
  • 23. Really clever: The Lambda architecture pattern Realtime View OUTPUT Data Views, Reports, Apps Speed layer INPUT Batch View Batch Layer query = function(all data) Serving Layer OUTPUT Design principles:  Human fault tolerance  Data immutability  Recomputation
  • 24. Different applications have different focus Strategic JettStart: Demand simulation and capacity planning Tactical UCD Cockpit: Stock monitoring & ULD positioning/move requests Operational Jettware: UCMs, LUCs and stock checks at stations Focus: real-time Management Cockpit: Overall performance monitoring Focus: historic data Focus: Application DWH ULD Tracking historic&data reporting tools Recommender System JettApp: Digital ULD tracking for the ground handler
  • 25. Build a mental model of you business domain And then map it on your existing data structures http://www.flickr.com/photos/alreadytaken/2136780794/
  • 26. Think beyond the obvious Go for second order effects:  Network effects  Correlations  non-linear phenomenons
  • 27. Enhanced Business domain insight  Accept unreliable and inconsistent data as a fact  You cannot fix the quality of historic data  Statistics will help you to detect or cancel out errors
  • 28.  Have realistic expectations regarding the Data Scientist role  Nice idea in theory, but (almost) impossible to find in reality  Better kiss the frog now than wait for the prince forever http://www.flickr.com/photos/fcstpauli/2067908929/sizes/l/in/photostream/
  • 29. Don’t loose your business focus. For us it’s all about… 1. Demand vs. Supply 2. Overstock vs. Understock (balance network) 3. Demand forecast and tactical ULD movements 4. Strategic movements
  • 32. Implement lean and agile development tactics The agile manifesto: Individuals and interactions … … over processes and tools Working software … … over comprehensive documentation Customer collaboration … … over contract negotiation Responding to change … … over following a plan