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THE DATA RING:
A CANVAS FOR BIG
DATA PROJECTS
September 21th - Frontiers Conference 2017
CHRISTIAN
RACCA
@pendolare
@i_realize
dati.piemonte.it
LEONARDO
CAMICIOTTI
AGENDA
i. A few words about us (TOP-IX & BIG DIVE course).
ii. BIG DATA opportunities, beyond the buzzword.
iii. Open challenges in applied Data Science.
iv. A canvas “Ring” to rule them all…
80+ Members
(15 in 2003)
NON PROFIT
CONSORTIUM
PUBLIC & PRIVATE
PARTICIPATION
MISSION
TO FOSTER
INNOVATION
BY LEVERAGING
INFRASTRUCTURE
ASSETS
EDUCATION
START-UP
CORPORATE
INNOVATION
CIVIC TECH
FUNDED
PROJECTS
IX NORTH-WEST
ITALY
DP
7 collaborators
16 employees
2 directors
TOP-IX CONSORTIUM
OUR ACTIVITIES ABOUT DATA
010100100110100100
0001010101001001
010101001001001
011101010101010
101010101010100
010101001010101
101010101010010
01010101010100
BIG DIVE HAS BEEN
DESIGNED AS AN INTENSIVE
TRAINING PROGRAM AIMED
AT BOOSTING THE TECH
SKILLS IN ORDER TO
EXTRACT VALUE FROM
DATA AND TO GENERATE
IMPACT.
010100100110100100
0001010101001001
010101001001001
011101010101010
101010101010100
010101001010101
101010101010010
01010101010100
01010101010010
010101001010101
WHAT IS BIG DIVE ?
4 weeks
20 divers
8 countries
2012
5 weeks
15 divers
5 countries
2013
5 weeks
15 divers
6 countries
2014
2015
5 weeks
19 divers
6 countries
2017
5 weeks
22 divers
5 countries
2016
5 weeks
20 divers
6 countries
THE BIG DIVE HISTORY
(BIG) DATA
OPPORTUNITIES
BEYOND THE
BUZZWORD
marketing
quantitative
technology
VS
qualitative
innovation
process
research business
THE TERM AMBIGUITY
YES, BIG DATA IS A “BUZZWORD”
Say BIG DATA again!
WHAT’S “NEW” ABOUT DATA
DATA
SKILLS TO EXTRACT
INFORMATION ARE
NOW MORE
ACCESSIBLE
INFRASTRUCTURE AS
A COMMODITY
/ Cloud
/ HPC & HPN
/ Frameworks
CULTURE & APPROACH
/ Complexity science
/ Network thinking
/ Open Innovation
DATA AVAILABILITY
/ Exponential growth
/ Machine VS human
/ Structured VS
un-structured
BIG DATA + ML = The NEW STACK
Big Data technologies are used to
handle core data engineering
challenges, and machine learning is
used to extract value from the data.
COMMON OPEN CHALLENGES
/THE DATA
/THE SKILLS
/FROM PROTOTYPE TO…
/THE RESULTS INTERPRETATION AND
THE EXPLAINABILITY ISSUE
/“GREY ZONES” IN DATA EXPLOITATION
/THE PURSUIT OF INNOVATION
DATA METADATA FEATURES
CHALLENGE #1
DATA REMAINS THE STARTING POINT
Metadata
Features Selection
Refers to the process of extracting useful
information (or features) from existing data.
“Data” that provides information about other
data.
{Descriptive, Structural, Administrative}
Volume
The effective amount of usable data.
No a-priori objective parameters.
On field validation is required.
ABOUT FEATURES…
FROM SOURCE DATA
TO RELEVANT DATA
Noisy or redundant data
makes it more difficult to
discover meaningful patterns.
High-dimensional dataset
requires more complex
models/algorithms and more
computational power.
Features “reduction” Data augmentation
Enriching existing data
with open data or through
third-party data providers.
THE DATA TEAM
CHALLENGE #2
HARD
(TECH)
SKILLS
SOFT
(HUMAN)
SKILLS
STATS,
MATH
SKILLS
SECTOR,
VERTICAL
SKILLS Danger zone
Re-arrangement of the THE DATA SCIENCE VENN DIAGRAM
by Drew Conway
CODING
SKILLS
STILL LOOKING FOR UNICORNS
FROM PROTOTYPE TO
PRODUCTION
CHALLENGE #3
A BABEL OF (CODING) LANGUAGES
PRODUCTION
JAVA, C, C++, …
DATA-DRIVEN
PROTOTYPE
PYTHON, R, D3.JS
REFACTORING
-
DATA
ENGINEERING
[ SCALA, …]
RESULTS INTERPRETATION
&
EXPLAINABILITY
CHALLENGE #4
THE EXPLAINABILITY ISSUE
LOW EXPLAINABILITY
HIGH EXPLAINABILITY
Inferential statistics
Machine learning
Deep learning
GREY ZONES
IN
DATA EXPLOITATION
CHALLENGE #5
THE DARK SIDE OF BIG DATA
/DATA DEMOCRACY VS BIG DATA
OLIGARCHY
/CORRELATION DOES NOT MEAN
CAUSATION
/BUBBLE FILTERS
/BIAS
/HUMAN RIGHTS
GRID NATIVES
VS
COMPLEX NATIVES
CHALLENGE #6
THE GRID VS THE COMPLEXITY
LEVERAGING (BIG) DATA
OPPORTUNITIES
REQUIRES
A PROPER METHOD
THE DATA RING
THE CANVAS APPROACH
The inspiring
precursor
The Data Ring
DATA RING CANVAS
DOWNLOAD, USE,
COMMENT,
REFINE IT (CC LICENSE)
Project name: Designed by: Date: Version:
(D
ata)output
Data
Infrastructure
GOAL(S)
SKI
LLS
PROCESSO
VALORIZZAZI
O
NE
TOO
LS
Data
input
Implementation
T
uning
Interpretation
VAL
UE
Execution
Planning
Data
strategy
Data skills
O
therskills
Benchmark
Metrics
Budget & timing
Outsourcing
Data
governance
Exploration
Hypothesis
Datapreparation
Dataprocessing
Validation
Iteration
Accessibility
Format
Metadata
Features
DatalakeFramework
Storage
Computing
Coding
Dataengineering
(Applied)Datascience
Dataviz
Business
Legal
Social science
Sector expertise
PRO
C
ESS
THANKS!
leonardo.camiciotti@top-ix.org
christian.racca@top-ix.org
www.top-ix.org
www.bigdive.eu
@top_ix
@bigdive_eu

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