- Global data is projected to reach 50.5 zettabytes by 2020, with BIM data from a single building containing thousands of assets and gigabytes of information. - Capturing and organizing BIM and other construction data through tools and APIs allows users to gain "superpowers" by analyzing and extracting insights from vast amounts of data. - The next steps are to embrace artificial intelligence and machine learning to automate data classification and enhance BIM tools, empowering users to harness the full potential of the construction industry's big data.

1. IF DATA IS
THE NEW OIL
BIM IS THE
COMBUSTION
ENGINE…and it’s about to seize up!

2. Intro
1: Data
2: Our Approach
3: Next Steps
Q&A
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3. -
INTRO
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4. Simple and affordable services
to enable quality BIM.
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Data
Our
Approach
Next
Steps
If Data is the new Oil, BIM is the Combustion Engine
i ?

5. 0110010
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D a t a E n d U s e
?
Simple and affordable services
to enable quality BIM.
B I M M o d e l
B I M
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Data
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Steps
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i ?

6. Organising the world’s building information.
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DATA
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8. Global Data
Year 2020
= 50.5 zettabytes of data
≡ 1021
bytes
≡ 1,000,000,000,000,000,000,000
≡ 1 trillion gigabytes
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9. BIM Data Brunel Building
• £125m
• 47,692 Assets
• 25 Floors
• 253 Spaces
• 2GB
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i ?

10. BIM Data
B I M U s e r S u p e r p o w e r s !
=
D a t a T o o l s
+
T o o l s
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i ?

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OUR APPROACH
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12. IFC
M o d e l K P I
=
T o o l s
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If Data is the new Oil, BIM is the Combustion Engine
i ?
D a t a
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13. COBie
M o d e l D a t a
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C o n t a c t s
=
A P I
+
T o o l s
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C O B i e

14. BCF
M o d e l C h e c k
D a t a
A m e n d m e n t s
T o o l s
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Steps
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i ?
2 x H e i g h t s
B C FM o d e l
=

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16. Machine Learning / AI
ARTIFICIAL
INTELLIGENCE
MACHINE
LEARNING
DEEP
LEARNING
Early artificial intelligence
stirs excitement
Machine learning begins
to flourish
Deep learning breakthroughs
drive AI boom
1950’s 1960’s 1970’s 1980’s 1990’s 2000’s 2010’s
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Steps
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18. Doing Artificial Intelligence
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A I M o d e l
A I
T o o l s
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Steps
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19. Doing Artificial Intelligence
( U n i c l a s s )
C l a s s i f i c a t i o n
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( A s s e t )
O b j e c t w w w . a i . o p e r a n c e . a p p
Give it a go and help
develop the database!
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20. Doing Artificial Intelligence
T o o l s
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Steps
If Data is the new Oil, BIM is the Combustion Engine
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OUTRO
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22. Summary w w w . o p e r a n c e . a p p
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E x p o n e n t i a l
D a t a G r o w t h
C a p t u r e &
R e f i n e m e n t
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E m b r a c e
I t
H a r n e s s
I t
E m p o w e r
O t h e r s
=
C a p t u r e &
R e f i n e I t

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If Data is the new Oil, BIM is the Combustion Engine
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C o l l a b o r a t e
G a i n
S u p e r p o w e r s !
=
E x p e r i m e n t
+

24. Questions? w w w . o p e r a n c e . a p p
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operance.app/bsl2020/

25. Useful Links w w w . o p e r a n c e . a p p
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• How to automate the boring stuff: Automate the Boring Stuff
• Learn to code for free: Codecademy (for some really good introductory lessons to python,
that require no setup and can run in your browser)
• Learn to code, gain a new skill: Treehouse (another good learning resource with some free
courses)
• The world's largest web developer site: W3schools (provides a reference for most common
python commands plus other languages).
• Open-source software, standards, and services: Jupyter (the easiest way to get up and
running with jupyter notebooks is to use Anaconda).
• A couple of good starter guides for jupyter notebooks, including how to install:
https://realpython.com/jupyter-notebook-introduction/ and
https://www.dataquest.io/blog/jupyter-notebook-tutorial/.
• Online community to learn and share knowledge: Stackoverflow (the no.1 place for
answers!).
• The world's most popular data science platform: Anconda (provides a graphical interface for
installing and accessing jupyter notebooks on both windows and macOS).
• Improve your Python skills and deep learning applications: Google Collab (as an alternative
to running jupyter notebooks directly on your laptop, use this to get a notebook located in
the cloud, up and running in a matter of seconds. Although, this does have the
disadvantage of making it harder to access your local file and data).
• The open source deep learning framework from facebook PyTorch https://pytorch.org/ , we
don’t directly use PyTorch but instead use the FastAI version https://docs.fast.ai/ which
provides a simpler way to access the package.