1.
Cognitive/AI: views, perspectives & directions
IDC Cognitive Conference
Milan, 21 November 2018

2.
© IDC Visit us at IDCitalia.com and follow us on Twitter:
@IDCItaly
Agenda
A different approach to the problem of Truth
When machines start learning human tasks
Between rock stars and sorcerers
2

3.
© IDC Visit us at IDCitalia.com and follow us on Twitter:
@IDCItaly
A simple acid test for data science:
who’s who
3

4.
Who fears an algorithmic society?
4
Learning
Networks/
Deep
Learning
Reading &
Writing
Abstraction
&
Integration
Detection &
Visualization
Speaking &
Listening
"The question of whether a
computer can think is no more
interesting than the question of
whether a submarine can swim"
(Edsger Dijkstra)
"Any AI smart enough
to pass a Turing test is
smart enough to know
to fail it“
(Ian McDonald)
"All this talk about artificial
intelligence is really just hype, it
will take at least fifty years before
we have to let them vote"
(Kenneth Boulding)
“By their very nature, heuristic
shortcuts will produce biases, and
that is true for both humans and
artificial intelligence, but the
heuristics of AI are not necessarily
the human ones”
(Daniel Kahneman)

5.
5
From the realm of “really helpful” to the realm of
“pretty strange”
Counterfeiting vs. Anti-
counterfeiting
Chatting with bots
everywhere
Changing the way of
doing banking
Big Data for enhanced
government

6.
© IDC 6
What are we talking about?
Machine Learning/
Deep Learning
Conversational
Technologies
+ +
Vertical
Applications
MACHINE
INTELLIGENCE
ARTIFICIAL
INTELLIGENCE
AUGMENTED
INTELLIGENCE

7.
© IDC Visit us at IDCitalia.com and follow us on Twitter:
@IDCItaly
7
From data to knowledge: key success factors
I. New predictive technologies (ML is the king)
II. Data governance (collection, retention, discovery, reuse) across boundaries
III. Tie structured and unstructured data sources together
IV. Organizational culture valuating information as a key asset
But an important factor is often underestimated, the decision-making style
V. Not all the decision makers are created equal!

8.
© IDC Visit us at IDCitalia.com and follow us on Twitter:
@IDCItaly
A paradigm shift in the way we produce
new knowledge
8
SERENDIPITY
(N.) FINDING SOMETHING GOOD WITHOUT
LOOKING FOR IT
DISCOVERY
(N.) NAVIGATING DATA THROUGH INTUITIVE
INTERFACES
vOLD STYLE
NEW STYLE

9.
© IDC 9
Building the Intelligent Core on the right Platform
Cognitive Processes,
Fueled by Data
The long-term shot:
intelligent business
automation

10.
© IDC Visit us at IDCitalia.com and follow us on Twitter:
@IDCItaly
Steps towards the Intelligent Enterprise
10
Pondering
Transformation
Automation
First
Analytics for
immediate
competitive
edge
Analytics for
long-term
value
“Automation, optimization? Why? We have so many
things to fix, automation is the last of our problems. We
need to cut IT costs!”
“We already implemented some data platforms for
analytics, we are not investing anymore in the brief
term”
“Everyday we are struggling with our information
systems. We are not so confident that data is actually a
competitive asset”
“Why everyone is talking about Big Data? It could be
really useful only for few companies”
“We are focused on an hazy goal: transforming our
business model!”
“Automation at all costs, no matter what!”
“We are evaluating different analytic platforms for better
automation”
“Data could be a competitive asset, but we are not so sure”
“Our future certainly does not depend on Big Data!”
“Our business model is pretty good, we don’t need to change it”
“We collect data and information, but not for automation purpose”
“We regularly use our analytic platform, sometimes investing for
upgrade”
“Transforming the way we manage data is of paramount importance”
“We don’t look too far in the future and have no time to play with
words. You can call it Big Data or whatever, it doesn’t matter”
“Well, our market is well-established. We change our business model
with a great deal of caution ”
“We use intelligent KPIs to ensure the efficiency of our processes,
products and services”
“We heavily invested over the years to build our analytics
capabilities”
“Any winning digital transformation strategy depends on the
way you use data and information”
“Big Data is the future for any intelligent company”
“We already changed many times. Transformation is the
journey!”

11.
© IDC 11
Managing the budget between IT and innovation
Source: IDC Italy, 2017 (n = 500, weighted extrapolation)

12.
© IDC 12
Value for money, and for data too
RELATIONAL CAPITAL
STRUCTURAL CAPITAL
HUMAN CAPITAL
•TRUST & REPUTATION
•CUSTOMERS & SUPPLIERS RELATIONSHIPS
•STAKEHOLDERS’ RELATIONSHIPS
•ORGANIZATION & INFORMATION SYSTEMS
•PROCESS DESIGN & ORGANIZATIONAL ROUTINES
•INTELLECTUAL PROPERTIES & INTANGIBLE ASSETS
•PEOPLE
•KNOW-HOW/ EXPERTISE
•TACIT KNOWLEDGE
BRAND EQUITY
GOODWILL
COGNITIVE EDGE
BIG DATA
PLATFORMS
SHIFTING UNDERLYING ECONOMIC ASSUMPTIONS ON THE ROOTS OF VALUE
FROM RETURN ON INVESTMENTS TO RETURN ON OFF-THE-BOOK ASSETS
DATA & INFORMATION ARE REIFYING INTANGIBLE ASSETS

13.
© IDC Visit us at IDCitalia.com and follow us on Twitter:
@IDCItaly
From CDO to Chief
Monetization Officer
• Revenue leaks
• Infer customer satisfaction/
churn risk & scientific marketing
• Moving from product to service
(from solving production
problems to data logistic)
• Fraud/ piracy detection and
other abusive behaviors
13

14.
© IDC 14
What the heck are they doing all the time?
Build/ run machine learning services improving products/ workflows
Do research that advances the state of the art of machine learning
Build/ run data infrastructure for storing, analyzing, and operationalizing data
Build prototypes to explore applying machine learning to new areas
Analyze and understand data to influence product or business decisions
0% 5% 10% 15% 20% 25% 30% 35%
World Wide
(n=14.282)
Italy (n=234)
Most common activities
Source: IDC elaboration on Kaggle Survey 2018

15.
© IDC 15
The challenges data scientists are facing today
Source: IDC elaboration on Kaggle Survey 2017
Unused results
Protect insights from company politics
Clarity of biz issues and conclusions
Need for analytical data talent
Need to deal with dirty/messy data
0% 10% 20% 30% 40% 50% 60%
Italy (n=111)
World Wide
(n=6.183)
Key Challenges for Data Scientists

16.
© IDC 16
Beyond numbers, what kind of data?
Source: IDC elaboration on Kaggle Survey 2018
Video Data
Geospatial Data
Sensor Data
Image Data
Categorical Data
Time Series Data
Text Data
0% 5% 10% 15% 20% 25% 30% 35%
World Wide
(n=14.282)
Italy (n=234)
The raw matter for insight

17.
© IDC 17
The future of primary alphabetization
Source: IDC elaboration on Kaggle Survey 2018
Visual Basic/VBA
C#/.NET
Bash/ Shell
MATLAB
Java
C/C++
R
Python
0% 10% 20% 30% 40% 50% 60%
World Wide
(n=14.282)
Italy (n=234)
Most required languages for data science

18.
IDC FutureScape: a global perspective on the future of ML/AI
18© IDC
Note: The size of the bubble indicates complexity/cost to address.
Source: IDC, 2019

19.
© IDC Visit us at IDCitalia.com and follow us on Twitter:
@IDCItaly
Concluding
remarks
Transforming a geek passion in a
serious game
Reality check: the power struggle
behind decision making
Against conformism: a community of
artisans and polyglots

20.
20
IDC Italia
Viale Monza 14
20127 Milano
Tel: +39 02 28457339
gvercellino@idc.com
Giancarlo Vercellino
Research & Consulting
Manager
IDC Italy
www.idc.com