be live in a world of data a world of big data but do we I actually hate the
big data but do we I actually hate the
big data but do we I actually hate the phrase that we live in a world of data
phrase that we live in a world of data
phrase that we live in a world of data it’s silly at best most of us live in a
it’s silly at best most of us live in a
it’s silly at best most of us live in a real physical world a world we perceived
real physical world a world we perceived
real physical world a world we perceived through our senses and not through
through our senses and not through
through our senses and not through algorithms and statistical calculations
algorithms and statistical calculations
algorithms and statistical calculations now as an inspiring data scientist I
now as an inspiring data scientist I
now as an inspiring data scientist I would prefer to live in a world of data
would prefer to live in a world of data
would prefer to live in a world of data a world where data informs our decisions
a world where data informs our decisions
a world where data informs our decisions but we don’t not yet anyways it’s
but we don’t not yet anyways it’s
but we don’t not yet anyways it’s estimated that every day we create about
estimated that every day we create about
estimated that every day we create about 2.5 quintillion bytes of data but we
2.5 quintillion bytes of data but we
2.5 quintillion bytes of data but we analyze less than one percent that data
analyze less than one percent that data
analyze less than one percent that data the data that makes up our digital
the data that makes up our digital
the data that makes up our digital identity is called big data this big
identity is called big data this big
identity is called big data this big data is typically out of our reach it’s
data is typically out of our reach it’s
data is typically out of our reach it’s protected by gatekeepers and approval
protected by gatekeepers and approval
protected by gatekeepers and approval forms by the time we get access to it
forms by the time we get access to it
forms by the time we get access to it it’s been analyzed aggregated and
it’s been analyzed aggregated and
it’s been analyzed aggregated and distilled into information sometimes
distilled into information sometimes
distilled into information sometimes this information is helpful for instance
this information is helpful for instance
this information is helpful for instance when you access google maps you get
when you access google maps you get
when you access google maps you get real-time traffic information presented
real-time traffic information presented
real-time traffic information presented to you as green orange and yellow
to you as green orange and yellow
to you as green orange and yellow highlights layered over your route
highlights layered over your route
highlights layered over your route there’s no data crunching needed the end
there’s no data crunching needed the end
there’s no data crunching needed the end product is exactly what you need to make
product is exactly what you need to make
product is exactly what you need to make a data informed decision but sometimes
a data informed decision but sometimes
a data informed decision but sometimes information we get is not helpful for
information we get is not helpful for
information we get is not helpful for instance data from biometric devices and
instance data from biometric devices and
instance data from biometric devices and wearable technology is often filtered
wearable technology is often filtered
wearable technology is often filtered through a predetermined visual display
through a predetermined visual display
through a predetermined visual display called a dashboard on these dashboards
called a dashboard on these dashboards
called a dashboard on these dashboards you may have some interactivity such as
you may have some interactivity such as
you may have some interactivity such as the ability to change a scale from steps
the ability to change a scale from steps
the ability to change a scale from steps per hour two sets per day but if you
per hour two sets per day but if you
per hour two sets per day but if you want to examine trends test for
want to examine trends test for
want to examine trends test for significant differences or visualize the
significant differences or visualize the
significant differences or visualize the data differently you can’t because you
data differently you can’t because you
data differently you can’t because you don’t actually have access to the data
don’t actually have access to the data
don’t actually have access to the data in this instance you are knocking the
in this instance you are knocking the
in this instance you are knocking the information you need to make a data
information you need to make a data
information you need to make a data informed decision now I do not live in a
informed decision now I do not live in a
informed decision now I do not live in a world of data I’m fortunate have to work
world of data I’m fortunate have to work
world of data I’m fortunate have to work and play in three amazing world’s data
and play in three amazing world’s data
and play in three amazing world’s data in each of these worlds I’m helping to
in each of these worlds I’m helping to
in each of these worlds I’m helping to build a data informed decision culture
build a data informed decision culture
build a data informed decision culture but I’m doing so in very different ways
but I’m doing so in very different ways
but I’m doing so in very different ways in the first world I’m a researcher in a
in the first world I’m a researcher in a
in the first world I’m a researcher in a higher education institution data in
higher education institution data in
higher education institution data in this world is protected by federal state
this world is protected by federal state
this world is protected by federal state and local privacy laws access to the
and local privacy laws access to the
and local privacy laws access to the data is denied unless an individual in
data is denied unless an individual in
data is denied unless an individual in the organization can show a valid reason
the organization can show a valid reason
the organization can show a valid reason for accessing it and even then their
for accessing it and even then their
for accessing it and even then their access is very limited
access is very limited
access is very limited in the second world of data I’m a
in the second world of data I’m a
in the second world of data I’m a business intelligence consultant for a
business intelligence consultant for a
business intelligence consultant for a private company data in this world is
private company data in this world is
private company data in this world is protected by company policy and
protected by company policy and
protected by company policy and proprietary analytic techniques
proprietary analytic techniques
proprietary analytic techniques employees of the company only get to see
employees of the company only get to see
employees of the company only get to see data that they that directly affects
data that they that directly affects
data that they that directly affects their work but in the third world data
their work but in the third world data
their work but in the third world data in the world I invite you to join me I
in the world I invite you to join me I
in the world I invite you to join me I am a data junkie in a community of a
am a data junkie in a community of a
am a data junkie in a community of a sexiest people on earth data scientist a
sexiest people on earth data scientist a
sexiest people on earth data scientist a data scientist is a lot of things but I
data scientist is a lot of things but I
data scientist is a lot of things but I like to think of them as a programmer a
like to think of them as a programmer a
like to think of them as a programmer a researcher a statistician and an artist
researcher a statistician and an artist
researcher a statistician and an artist in the first two worlds higher education
in the first two worlds higher education
in the first two worlds higher education and business data has primarily been
and business data has primarily been
and business data has primarily been entrusted to three privileged groups and
entrusted to three privileged groups and
entrusted to three privileged groups and organization the database administrators
organization the database administrators
organization the database administrators the analysts and the executives the
the analysts and the executives the
the analysts and the executives the database administrators care and feed
database administrators care and feed
database administrators care and feed for the environment with the data lives
for the environment with the data lives
for the environment with the data lives the analyst consumes the data and
the analyst consumes the data and
the analyst consumes the data and distills it into information and the
distills it into information and the
distills it into information and the executives use that information make
executives use that information make
executives use that information make decisions the average individual in the
decisions the average individual in the
decisions the average individual in the company has little to no access to data
company has little to no access to data
company has little to no access to data for them access the data is often
for them access the data is often
for them access the data is often delayed intermittent or prioritize onto
delayed intermittent or prioritize onto
delayed intermittent or prioritize onto a list of things they’ll never get done
a list of things they’ll never get done
a list of things they’ll never get done I want us all of us to do more data I
I want us all of us to do more data I
I want us all of us to do more data I want us to join the ranks of a data
want us to join the ranks of a data
want us to join the ranks of a data revolution hell-bent on one goal to
revolution hell-bent on one goal to
revolution hell-bent on one goal to democratize data in this data revolution
democratize data in this data revolution
democratize data in this data revolution there are a lot of roles to play but the
there are a lot of roles to play but the
there are a lot of roles to play but the two most important are the curators and
two most important are the curators and
two most important are the curators and the quisitor ‘he’s now the roles are not
the quisitor ‘he’s now the roles are not
the quisitor ‘he’s now the roles are not mutually exclusive but the movement
mutually exclusive but the movement
mutually exclusive but the movement requires both as a curator if you have
requires both as a curator if you have
requires both as a curator if you have accessed the data digitize it share it
accessed the data digitize it share it
accessed the data digitize it share it by that i mean put it into a data set
by that i mean put it into a data set
by that i mean put it into a data set that can be sure electronically with
that can be sure electronically with
that can be sure electronically with anyone and everyone do not let good day
anyone and everyone do not let good day
anyone and everyone do not let good day to go to waste that means sharing it on
to go to waste that means sharing it on
to go to waste that means sharing it on your website or using free services like
your website or using free services like
your website or using free services like Fusion Tables by Google Fusion Tables as
Fusion Tables by Google Fusion Tables as
Fusion Tables by Google Fusion Tables as an experimental data visualization web
an experimental data visualization web
an experimental data visualization web application to gather share and
application to gather share and
application to gather share and visualize data now as in the quisitor
visualize data now as in the quisitor
visualize data now as in the quisitor your primary tasks is to seek out that
your primary tasks is to seek out that
your primary tasks is to seek out that open access data and interrogate it with
open access data and interrogate it with
open access data and interrogate it with fervor ask questions about the data let
fervor ask questions about the data let
fervor ask questions about the data let you curious you run wild find out how
you curious you run wild find out how
you curious you run wild find out how the data was collected find out how was
the data was collected find out how was
the data was collected find out how was coated how was it analyzed and then
coated how was it analyzed and then
coated how was it analyzed and then examined it for yourself find your own
examined it for yourself find your own
examined it for yourself find your own meaning in your own insight
meaning in your own insight
meaning in your own insight last fall i created a meme of myself the
last fall i created a meme of myself the
last fall i created a meme of myself the symbol phrase making you a data lover
symbol phrase making you a data lover
symbol phrase making you a data lover too i truly believe that the average
too i truly believe that the average
too i truly believe that the average individual benefits from working and
individual benefits from working and
individual benefits from working and playing with data the reason i believe
playing with data the reason i believe
playing with data the reason i believe this is rooted in a personal story when
this is rooted in a personal story when
this is rooted in a personal story when i was 10 years old something happened to
i was 10 years old something happened to
i was 10 years old something happened to me that shaped my life forever I was in
me that shaped my life forever I was in
me that shaped my life forever I was in math class and the teacher had just
math class and the teacher had just
math class and the teacher had just dismiss us to work on an exercise our
dismiss us to work on an exercise our
dismiss us to work on an exercise our task was simple collect some data
task was simple collect some data
task was simple collect some data recorded his hash marks and then
recorded his hash marks and then
recorded his hash marks and then translate the hash marks into a bar
translate the hash marks into a bar
translate the hash marks into a bar chart each hash mark was to be a colored
chart each hash mark was to be a colored
chart each hash mark was to be a colored square on a grid paper that would make a
square on a grid paper that would make a
square on a grid paper that would make a book bar chart so as I was busy copy an
book bar chart so as I was busy copy an
book bar chart so as I was busy copy an example from the board my 10 year old
example from the board my 10 year old
example from the board my 10 year old colleague approached me to collect some
colleague approached me to collect some
colleague approached me to collect some data she asked me a simple question
data she asked me a simple question
data she asked me a simple question which of the following is my favorite
which of the following is my favorite
which of the following is my favorite cartoon character there are three
cartoon character there are three
cartoon character there are three answers on her paper now she had
answers on her paper now she had
answers on her paper now she had finished her poll before me and already
finished her poll before me and already
finished her poll before me and already collected data I knew this because there
collected data I knew this because there
collected data I knew this because there was hash marks next to choices on paper
was hash marks next to choices on paper
was hash marks next to choices on paper what I didn’t know was what were the
what I didn’t know was what were the
what I didn’t know was what were the choices now I don’t mean I’d never heard
choices now I don’t mean I’d never heard
choices now I don’t mean I’d never heard the cartoon characters before what I
the cartoon characters before what I
the cartoon characters before what I mean is I couldn’t read the choices the
mean is I couldn’t read the choices the
mean is I couldn’t read the choices the reason I was copying the example from
reason I was copying the example from
reason I was copying the example from the borders because at ten years old I
the borders because at ten years old I
the borders because at ten years old I didn’t know how to read it’s not that I
didn’t know how to read it’s not that I
didn’t know how to read it’s not that I couldn’t it’s just that no one had
couldn’t it’s just that no one had
couldn’t it’s just that no one had taught me how to yet up until this point
taught me how to yet up until this point
taught me how to yet up until this point in my life I’ve learned tricks to get me
in my life I’ve learned tricks to get me
in my life I’ve learned tricks to get me through situations just like this in
through situations just like this in
through situations just like this in this situation I went with option C
this situation I went with option C
this situation I went with option C option C had four hash marks next to it
option C had four hash marks next to it
option C had four hash marks next to it more than any other option so I thought
more than any other option so I thought
more than any other option so I thought it was safe I couldn’t be wrong if
it was safe I couldn’t be wrong if
it was safe I couldn’t be wrong if everyone else liked it too right wrong I
everyone else liked it too right wrong I
everyone else liked it too right wrong I quickly realized how wrong I was my 10
quickly realized how wrong I was my 10
quickly realized how wrong I was my 10 year old colleague burst into laughter
year old colleague burst into laughter
year old colleague burst into laughter and sling you like Hello Kitty I’ve
and sling you like Hello Kitty I’ve
and sling you like Hello Kitty I’ve trust me when I tell you to a
trust me when I tell you to a
trust me when I tell you to a ten-year-old boy in the 90s being
ten-year-old boy in the 90s being
ten-year-old boy in the 90s being laughed at by a girl in math class is
laughed at by a girl in math class is
laughed at by a girl in math class is the end of your world I quickly try to
the end of your world I quickly try to
the end of your world I quickly try to defend myself but she recorded my answer
defend myself but she recorded my answer
defend myself but she recorded my answer is a hash mark and moved on but that
is a hash mark and moved on but that
is a hash mark and moved on but that feeling of anxiety fear doubt confusion
feeling of anxiety fear doubt confusion
feeling of anxiety fear doubt confusion embarrassment has stayed with me it
embarrassment has stayed with me it
embarrassment has stayed with me it fuels my passion for making
fuels my passion for making
fuels my passion for making understanding data a reality for anyone
understanding data a reality for anyone
understanding data a reality for anyone who wants it too often I hear people say
who wants it too often I hear people say
who wants it too often I hear people say I’m not a math person or are they here
I’m not a math person or are they here
I’m not a math person or are they here than say I’m not a stats person and
than say I’m not a stats person and
than say I’m not a stats person and that’s fine in this data revolution of
that’s fine in this data revolution of
that’s fine in this data revolution of curators and inquisitors you don’t have
curators and inquisitors you don’t have
curators and inquisitors you don’t have to be to love data all you need is
to be to love data all you need is
to be to love data all you need is curiosity together we can build a data
curiosity together we can build a data
curiosity together we can build a data informed decision culture we can
informed decision culture we can
informed decision culture we can democratize data but it starts by
democratize data but it starts by
democratize data but it starts by rejecting the notion that analyzed
rejecting the notion that analyzed
rejecting the notion that analyzed aggregated information is the same thing
aggregated information is the same thing
aggregated information is the same thing as data and it’s not let’s do more with
as data and it’s not let’s do more with
as data and it’s not let’s do more with data thank
data thank
data thank [Applause]
”
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