Do More With Data: Digitize to Democratize | Michael Le | TEDxHumboldtBay

be live in a world of data a world of

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]