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Each week I come across 
an article or a report

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that asserts that data is the new oil,

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that the use of data will lead
to a new era of knowledge,

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or even that it can predict the future.

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This has been particularly true since
everyone started talking about big data.

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You know, the use of large-scale data, 
mega data.

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For example, Sergei Brin, 
the founder of Google,

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who is focusing on the use of medical data

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to cure Parkinson's disease, 
for which he is at risk.

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During the World Cup, many people said

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that the German team was able to
beat the Brazilian team 7-1

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thanks to the use of match data.

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It's clear

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that there is no field or 
type of organization

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for which Big Data

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isn't supposed to be
a magic wand that will enable

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the resolution of extremely 
complex problems.

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And I must admit that I feel uneasy

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about these kinds
of simplistic statements,

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which I see as overshadowing 
a number of issues, including the economy,

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the environment,

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politics,

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and the ethics 
of the massive production of data.

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Please don't think that I am skeptical

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or doubtful about data,

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or that I am opposed 
to all forms of quantification.

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On the contrary,

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I live surrounded by data.

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During the day, I'm working on a thesis
in sociology at Telecom ParisTech

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where I study Open Data.

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The important effort
to provide open access to public data.

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And I study the consequences of Open Data

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for the operation of government.

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At night, I am the administrator
for an association,

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Open Knowledge Foundation France,

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which campaigns for open knowledge
and for data that benefits everyone.

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Today, I would like to persuade you

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that, at this time, when data
is becoming obtrusive,

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we need to take a step back.

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This coronation of data 
that we are witnessing

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during the era of Open Data and Big Data

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demands a new culture of
critical thought about data.

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We must be able to understand
how it is produced and used,

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and how we can become
independent from it.

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I also want to share 
the results of an experiment

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that we did at
Open Knowledge Foundation France

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called "the School of Data."

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I hope to show that, 
through the use of data,

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we can manage to develop
this culture of critical thought

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and that we can develop 
new checks and balances.

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So, what are the problems with data?

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The first problem is that
data is always right.

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Now, don't believe that this is
anything new.

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Historically, the word 'data'
comes from the Latin word "datum"

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which, in mathematics and theology

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in the 15th century, referred to

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the facts taken as given in an argument


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and which were not to be 
called into question.

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Today, as you know, data refers

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to everything that flows 
in your computer.

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That is to say, the 1's and 0's
that pass from USB stick to hard disk

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are considered data.

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On the other hand,

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the sense that data is a given,

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that it is factual,

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that it is not to be questioned,

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has remained.

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The second problem with data

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is that we don't really know
where it comes from.

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In general, when someone uses data,

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he or she has very little information
about the way

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in which it was produced.

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At best, you will have access to metadata,

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that is to say, data about the data,

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which will tell you the contents
of the file and, occasionally,

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how the data was produced.

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However, that data has a long history.

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It was collected.

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It was processed, formatted,

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aggregated, processed by algorithms,

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and visualized before reaching you.

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This is why sociologist 
Bruno Latour asserts

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that we should say 'obtaineds' 
instead of data

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to accurately reflect this long history

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which will constrain a number of uses.

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Finally, the third problem with data

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is that we can't really see it.

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Have you ever seen a data center,

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even if only from outside, 
or from the road?

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Do you have any idea 
of where your data is stored?

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I mean, physically, where it is stored?

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Do you have any idea what will happen
to it in 10 years?

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In any case, I have no answer 
for these three questions.

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However, even if we can't see our data,
we can measure its effects.

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At the individual level,

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when Facebook changes its terms of service

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or modifies its algorithm, it has 
consequences for your private life

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and for the way in which you present 
yourself as an individual.

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And on the most macroscopic level,
the Snowden affair has shown

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that the massive production of data

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can have consequences 
for the sovereignty of the State

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or for diplomacy.

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This is why we must develop a culture

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of critical thought about data.

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To encourage myself,

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I was inspired by a book 
called "Statactivism."

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Statactivism is a neologism

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proposed by researchers and artists

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that refers to those experiences 
that permit one to liberate oneself

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from the power of data.

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The fundamental basis of statactivism

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is that data controls us,

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and that it imposes on us
like an argument from authority.

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The goal of statactivism 
is almost revolutionary.

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It asserts that other kinds of data
must be possible.

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It is not necessary to be opposed
to all data.

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Instead, we should use the power of data

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to propose other realities

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to critique data more effectively,

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or to propose other measures.

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In short, to propose other data.

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There is a motif in the book
which I find particularly meaningful,

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that of the judoka.

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Judoka use the strength of their opponents
in order to turn it back on them.

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That is what I want to invite you 
to do today:

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think about how to use data 
to better analyze it.

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I think, precisely at this moment
in the development of Open Data,

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the need to develop a culture 
of critical thought about data

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is increasingly crucial.

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Don't be misled: Open Data represents
an extraordinary opportunity.

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The volume of data is exploding

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and data is no longer 
the privilege of the powerful.

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Today, you can use data

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without asking anyone's permission.

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And this is a good idea, 
because public data is available.

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But I think that there is a risk

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to thinking 
that the simple diffusion of data

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will be enough to emancipate society,

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that individuals can emancipate 
themselves from the power of data

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just because they have access to data.

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There is a Canadian sociologist
named Michael Gurstein

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who has proposed an expression 
that sums up a risk of Open data,

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namely, "Empower the Empowered,"

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meaning to give more power 
to those who already have it.

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That is why it's crucial
to develop a culture of critical thought

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to be able to understand how data
is produced,

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used, and how you can use it 
to take a step back.

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Well, that's the theory.

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I would like to share with you
the first results from an experiment

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that we did in my association: 
Open Knowledge Foundation France.

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We are part of a worldwide network 
dedicated to open knowledge and open data.

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We have groups in more than 50 countries.

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And the idea of our association
and of this worldwide movement

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is that each person can benefit,
can profit,

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from works, scientific articles, 
and content,

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to create, play, educate, 
or to start up a business.

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Open Knowledge has 
a large number of projects.

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I'm going to talk about one project,
the "School of Data."

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We participated together 
in the translation of this project,

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this "School of Data."

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The School of Data consists 
of online resources

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that are free and accessible to all,

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and also events.

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We first proposed classes.

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In these classes, you do not even have 
to know what data is.

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Or how to use a spreadsheet,
which is really the tool of choice.

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You will be taught about that
in our class.

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No expertise is required,

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you are guided step by step
in the use of data.

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We also use another format
which is particularly educational,

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namely, the recipe.

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Recipes are just like in cooking - 
you have ingredients

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and steps.

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The ingredients will be data,

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software - free if possible,

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so that you can use data.

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The idea is that making a map
of electoral results,

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or a graph of results 
of the French soccer team

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should be as easy to do as making
a tarte Tatin or Bechamel sauce.

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You find the resources online

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and we walk you through the project
step by step.

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We also have tried to develop
another format for in-person sessions,

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which we call expeditions.

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For expeditions, it's like
mountain climbing:

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you have a guide, a "data sherpa,"

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who will accompany you,

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attached by a rope.

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There will be 10 or 20 participants

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who work together during a weekend
or sometimes for a few hours.

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Our first data expedition

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focused on the question of air pollution
in Île-de-France.

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I don't know if you have seen

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these images of Paris 
with black clouds of pollution.

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They left their mark on us, 
and we said to ourselves:

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"Well, let's dig into this set of data."

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The first step, when we undertook
this data expedition,

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was to identify the available data.

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We realized that there is
no available data

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that is freely reusable, that is to say,
that you have the right to reuse

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without asking for permission,
on this crucial question.

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Therefore, we had to extract data
from websites,

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reports, or even from graphics.

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Imagine what a mess it is to expose
data that is in a graphic.

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We also realized that Airparif,

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the organization responsible 
for the production of data

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relevant to the question 
of air pollution in Île-de-France

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does not allow you to use
its data freely.

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One must ask permission, or pay.

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We were able to overcome
these constraints

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and to conduct this expedition

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guided by our sherpa, Pierre.

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During this data expedition

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we broke into small groups,
and each group was assigned an angle.

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One of the principles of the expeditions:
you have an angle, like in journalism,

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we ask ourselves questions that could be
the title of an article.

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The first group asked itself

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if bicycle riding had led to a decrease
in air pollution in Paris.


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The second group,

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since it was during a strike,

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asked itself if public transport strikes

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cause air pollution in Île-de-France 
to increase.

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And the third group asked if
all regions are equal

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with regard to air pollution,
or if geography and environment

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could have an effect, and if so, 
could be seen in the data.

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The results of this expedition,

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I am sorry to say, will be a bit 
disappointing.

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We did not find any correlation
or causal connection

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with nice data points, 
a fitting curve, or a straight line,

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that proves that our hypotheses
are correct.

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We did not succeed at that,
but we worked for four hours.

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What we did manage to show,
on the other hand,

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is that it is extremely difficult

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to use data concerning a question
as crucial as air pollution,

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to understand how it is produced,

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extremely difficult to use it,

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that the most simple measurements
are not accessible,

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and that you do not necessarily have
the right to reuse them.

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That is just what we tried 
to do at this event:

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to develop a culture of critical thought
on the way in which data

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is used concerning the question
of air pollution.

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We also tried to develop this format 
of expeditions and training events

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with another group

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that is less expected,

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that of children.

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We asked ourselves the question
during an event that we did with Etalab,

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the government institution
in charge of data.gouv.fr,

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the open data portal
of the French government.

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We suggested the idea 
of radically different open data portals.

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They were fictional projects,
just prototypes.

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There is a group that has come out
with a prototype called Tada.gouv.fr.

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Tada.gouv.fr is a fictional portal,
a bit idealistic, destined for children.

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The data is presented
not by government department or minister,

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but by discipline, that is to say
that you have data

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about history and geography,
physics and chemistry,

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or life and Earth sciences.

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On this occasion, we realized
that open data

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can be a fantastic resource
for school

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because it allows the development
of inter-disciplinary work,

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and this culture of critical thought
about data I have mentioned.

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We did not leave things at observation.

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We tried to do a first experiment

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and I would like to tell you 
about the first results.

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We joined with Silicon Banlieue,

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which is a site dedicated
to data in Argenteuil,

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and we proposed to do an event

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with children between 8 and 14 years old

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who came to the Open World Forum,

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an event dedicated 
to open computing in Paris.

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There, you can see me from the back.

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With the 8 to 14 year old children,
we worked on the question of cinema,

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because that interested them,
and it is a simple enough subject.

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First we collected data,

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nothing very complicated,
it was just a paper form.

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We asked them how many times a month
they go to the cinema,

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which movies they saw from a list;
then we compared that with data

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that is available from the survey
of French cultural practices,

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on which you have 
exactly the same type of data.

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With the children, we produced
an infographic at this time.

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Now, I am really bad at math,
I got a 7,5 on the Bac,

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I found myself explaining
the concept and calculation

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of averages using a spreadsheet,
which was rather surprising.

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I explained how it works.

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We emerged with an infographic
and we were able on this occasion,

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I think that this is the important point,
to develop a culture of critical thought.

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I explained to them about data,
how it is used,

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how they can use it,

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how it controls us in a certain way,

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but that we can also take back
the power over data.

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I assure you that with a topic
as attractive as cinema

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we can deliver this kind of message

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and have a discussion on these questions.

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I hope that I have convinced you
that it is necessary today

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to take a step back with regard to data,

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to develop a culture of critical thought,
to understand

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how it is produced 
and how you can use it,

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to prevent data from being forced on you.

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So from today, 
get your hands dirty,

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find a sherpa,
all of the resources are online,

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and go on a data expedition.

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Thank you.

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(Applause)