This is the wrap up of the first day of the EBU Big Data and Society conference that was held at RTBF on 12 and 13 december 2016. This presentations sumps up the takeaways of the presentation by Jean-Paul Philippot, Prof. Wehenkel, Steven Bourke, Prof. Malthouse, PN Schwab, Evan Estola

1. BIG DATA & SOCIETY WORKSHOP
DAY 2 INTRODUCTORY SPEECH
Pierre-Nicolas Schwab, Big Data/CRM Manager
13 December 2016
RTBF

2. • Personalization is key: PSM
can’t do without
• We have values that need to
be reflected in our
algorithms
• Sharing knowledge among
EBU members is key for
advancement
MAIN IDEAS COVERED
YESTERDAY

3. • Ecosystems and modeling
• Big Data in the newsroom
• A model to understand
audience engagement
• History of recommendation
and filter bubbles
• Ethical recommendations
MAIN IDEAS COVERED
YESTERDAY

4. Big
Data &
Society
Serendipity
Customer
Value
Ethics
Public
Service
Medias
Filter
bubbles
Person-
nalization
Infobesity

5. • Modeling comes first (remember
the many stakeholders in the
electrical case ?)
• You can’t really understand the
world of you don’t model (in
opposition with most Big Data
practices today)
• What is your strategy? (Short-
term vs. long-term goals)
TALK 1 : PROF. WEHENKEL

6. • What is PSM’s position (data-
centric firms vs. governments vs.
all other industries)  what is
our vision for Big Data ?
• Are our projects aligned with
trends in Science / Tech /
Producers / Consumers
• Relevancy / velocity of data
sources
TALK 1 : PROF. WEHENKEL

7. • Content creation can be
supported by Big Data
technologies
• Big Data can be used at each
step of the flow :
– Discover
– Create
– Curate
– Engage
TALK 2. STEVEN BOURKE
(SCHIBSTED)

8. • Meta data is essential (holy
grail !)
• Content creators can
contribute better meta data
• How do we create value for
content creators (metrics,
ease-of-use)
• Balance human / algorithmic
curation
TALK 2. STEVEN BOURKE
(SCHIBSTED)

9. • Engagement is what we
strive for : but do we know
what it is really ?
• Engagement model proposed
(scientifically validated) :
– Brand perceptions
– Brand dialog behaviors
– « Shopping » behaviors
– Brand consumption (RFV)
TALK 3. PROF. MALTHOUSE
(NORTWESTERN UNIVERSITY)

10. • Output of model : SAT, LOY,
Lifetime value, customer value
(remember CLV?)
• Key takeaways :
– where do we get data for those
4 components to measure total
value created
– How many « ecosystems » do
we have?
TALK 3. PROF. MALTHOUSE
(NORTWESTERN UNIVERSITY)

11. • Recommendation algorithms
are not new (1992)
• They have fundamentally
changed in nature and have
become more complex
• Data collection has changed:
– Explicit  Implicit
– Non intrusive  intrusive
TALK 4. PIERRE-NICOLAS
SCHWAB (RTBF)

12. • Two kinds of traps :
– Content trap (filter bubble)
– Ecosystem trap
• How do we create value for
user: E³
– Educate
– Encourage
– Excite
TALK 4. PIERRE-NICOLAS
SCHWAB (RTBF)

13. • Recommendation algorithms
can go very wrong (racist,
sexist, discriminatory)
• Algorithms reflect beliefs of
our society
• When we design
recommendation systems we
must identify those who may
be negatively impacted
TALK 5. EVAN ESTOLA
(MEETUP, NEW-YORK)

14. • Gender may become a
discriminatory factor :
identify it and remove it from
your model
• Differentiate good / bad /
horrible feature
• use ensemble modeling
TALK 5. EVAN ESTOLA
(MEETUP, NEW-YORK)

15. • All presentations already
made available for your
comfort on slideshare :
www.slideshare.net/intotheminds
• I’ll redo the presentation
with Evan and make it
available as video file for
your comfort
ONE LAST WORD