BIG DATA & SOCIETY WORKSHOPDAY 2 INTRODUCTORY SPEECH

Pierre-Nicolas Schwab, Big Data/CRM Manager

13 December 2016

RTBF

• 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 COVEREDYESTERDAY

• Ecosystems and modeling

• Big Data in the newsroom

• A model to understand audience engagement

• History of recommendation and filter bubbles

• Ethical recommendations

MAIN IDEAS COVEREDYESTERDAY

BigData & Society

Serendipity

Customer Value

Ethics

Public Service Medias

Filterbubbles

Person-nalization

Infobesity

• 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

• 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

• 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)

• 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)

• 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)

• 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)

• 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)

• 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)

• 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)

• 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)

• 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