#3 DataBeersBCN - "The impact of data in reality" by Karina Gibert from UPC at 3rd DataBeersBCN

1. K. Gibert©
The impact of data in reality
K. Gibert
Knowledge Engineering and Machine Learning group
Universitat Politècnica de Catalunya, Barcelona
karina.gibert@upc.edu
http://www.eio.upc.edu/homepages/karina
Department of Statistics and Operation Research
3rd DataBeers-Barcelona
Mobile World Center, Barcelona, 21 juliol 2015

2. K. Gibert©
Main messages
Reality highly complex, real decision-making difficult
Data Scientists
&
Intelligent Decision Support Systems
 Use data available
 better understand reality
 better decision-making

3. K. Gibert©
Real-world Systems or Domains
 Strong Complexity:
 Not completely known
 Difficult to understand
 Difficult to characterize
 Difficult to manage
 Difficult to control
 Domains:
Environmental
Medical
Industrial processes
Bussiness
Social Sciences…

4. K. Gibert
Informed decision
Proper decision is critical for management and leadership in most
phenomena and organizations [Zikmund 2010]
Consider facts, context
[Luhn 1958] .
[ Kim 2011] .

5. K. Gibert
The informed decision
Nascent data-centered economy
Craig Mundie, head of research and strategy, Microsoft [The Economist 2010]
Managed well, data can unlock new sources of economic value
[Kenneth 2010]

6. K. Gibert
Bringing data to decision making
Many attempts to formalize the process:
-Bussiness analytics [Kohavi 2002]
-Data-driven decision-making [Hedgebeth 2007]
-Evidence-based decision-making [Mankelwicz 2008]
-Evidence-based management [Rousseau, 2007]
-Data Science [Pentland 2014]

7. K. Gibert
Real impact of data in decisions
High availability: Easy to measure and store
Boom Internet late 1990s [Tim Berners-Lee, 1990], 1995 www free&global
New technologies
Exponentially increasing

8. K. Gibert
Real impact of data in decisions
High availability: Easy to measure and store
Boom Internet late 1990s [Tim Berners-Lee, 1990], 1995 www free&global
New technologies
Exponentially increasing
Low Consumption:
Less than 1% the world's data is currently being analysed [IDC report 2012]
77% of employees are aware of bad decisions in enterprise by unproper
consumption or ignoring available data [Hammond 2004]
Non-analyzed, no deep analysis, no instrinsic value extracted [Hammond 2004]
……….. [El Pais 2009]
When analyzed, no more powerful method used [Cukier 2010]
Leaders admit wrong decisions about 25% of the time [IBM report 2013]

9. K. Gibert©
From Data to Decision-Making
Data-driven
results
Data
?
Decision –
making
Two main research fields
Extract better
knowledge from data
Support
decision-makers
Data mining
Knowledge Discovery from Databases
Data Science…
Decision Theory
Multicriteria decision support
Decision support systems…

10. K. Gibert©
From Data to Decision-Making
Data-driven
results
Data
Data Mining
Decision –
making
Two main research fields
Extract better
knowledge from data
Support
decision-makers
Input/Begining Output/Ending
Scarce
research in
the middle

11. K. Gibert©
Gap Data Mining- Decision making
Data-driven
results
Data
Data Mining
Interpretation
Decision –
making
The Fact Gap: The Disconnect Between Data and Decisions [Hammond 2004]
No analysis No understandable No trust
Needs to be general literacy about data interpretation [A “Sandy” Pentland]
keynote Campus Party Europa Sept 4th 2013 Head of MediaLab Enterpreneurship MIT

12. K. Gibert©
The Data Scientist boom
Data Scientist: The Sexiest Job of the 21st Century [Harvard Buss. Rev. Oct 2012]
It takes skills to explore big data Sooraj Shah [Computing Jan 2013]
Data is widely available; what is scarce is the ability to extract wisdom from them
Hal Varian, Google’s chief economist 2010 [Computing Set 2013]
Data Science: The Numbers of Our Lives [NYTimes April 2013]

13. K. Gibert
Data Scientist Shortage
There are too few data scientists in the world and education needs to change in
order to maximise the true potential of data science
Head of MediaLab Enterpreneurship MIT [Pentland, Sep 2013]
Not enough data scientists, MIT expert says. [Computing Sep 2013]
Shortage of talents with required skills for capturing the whole potential of big
data McKinsey Global Institute Bussiness report [McKinsey 2011]
Shortage of 140,000 to 190,000 data scientist by 2018 [McKinsey 2013].
62% of C-level executives realize that lack of data scientists is causing a real
problem OnePoll Survey, for soft supplier Teradata [ComputerWeekly nov2013]

14. K. Gibert
Transforming Data into Decisional Knowledge
(Big)-Data Mining (1996) + Post-processing (2000)
Artificial
Intelligence
techniques
Statistical/
Numerical
methods
Costs
Ontologies
Environmentsl/health/bussiness
…
Geographical Information Systems
Complementary Systems …
Intelligent Decision
Support Systems
Data
Mining
methods
Optimization
methods
IDSS Validation
models
Data Science (2005)
Intelligent Decision Support Systems (90s)

15. K. Gibert©
Intelligent Decision Support Systems Systems
[Marakas 1999], [Sánchez-Marrè, Gibert et al 2000]
 Integrative/Multidisciplinar solution
 Link data-mining/modelling with decision-making
 Support & follow complex decisions [on-time]
can reason over decisions and explain recommendations

16. K. Gibert
GESCONDA Architecture [Gibert et al 2010]
Pre PROCESSING
DATABASE
Relevant DATA
EXPERT
GOALS
DEFINITION
REASONING
MODELLING LAYER
Data Mining methods
KNOWLEDGE MODELS
CBR
reasoner
Rule-based
Reasoner
Domain ontologies
Prior Expert Know
Other Models
SENSORS
Monitoring
System
USER
Recommended Decision
Priorized Alternatives
Costs
Reporting
Human/Computer
Interaction
META-KNOWLEDGE
UNIFICATION/INTEGRATION of Models
$TIC2000-1011$ (2000-2003) $TIN2004-01368$ (2004-2007)

17. K. Gibert©
Integrative/Multidisciplinar
Intelligent decision support system
Artificial
Intelligence
techniques
Statistical/
Numerical
methods
Costs
Ontologies
Environmentsl/health/bussiness…
Geographical Information Systems
Complementary Systems …
Intelligent Decision Support Systems
Data Mining
methods
Optimization
methods
IDSS Validation
models

18. K. Gibert©
Conclusions
 Reality Highly complex, decision-making difficult
 Data available to better understand, for better decide
 Multidisciplinar and integrative approach required
 Data-driven/knowledge-driven/model-driven
integrative approach
 Data Scientists shortage
 Intelligent Decision Support Systems:
integrative solution that can help

19. K. Gibert
The impact of data in reality
Karina Gibert
Dpt. Statistics and Operation Research
Knowledge Engineering and Machine Learning Research group
Universitat Politècnica de Catalunya-BarcelonaTech (Spain)
karina.gibert@upc.edu
www.eio.upc.edu/homepages/karina
DataBeers 2015
21 july 2015
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