The most important skills of data scientists | Jose Miguel Cansado | TEDxIEMadrid

I want you to close your eyes and

I want you to close your eyes and imagine the image of a data scientist

imagine the image of a data scientist

imagine the image of a data scientist are you picturing it but woody much

are you picturing it but woody much

are you picturing it but woody much comes through your mind okay you may

comes through your mind okay you may

comes through your mind okay you may open your eyes when people think about a

open your eyes when people think about a

open your eyes when people think about a data scientist detained to imagine a

data scientist detained to imagine a

data scientist detained to imagine a picture of a man in a you know lab coat

picture of a man in a you know lab coat

picture of a man in a you know lab coat who maybe hasn’t come for a while during

who maybe hasn’t come for a while during

who maybe hasn’t come for a while during an artificial intelligence applying

an artificial intelligence applying

an artificial intelligence applying algorithms and analyzing data to struck

algorithms and analyzing data to struck

algorithms and analyzing data to struck insights for industrial or commercial

insights for industrial or commercial

insights for industrial or commercial use but in reality the profile of a data

use but in reality the profile of a data

use but in reality the profile of a data scientist requires the skills of the

scientist requires the skills of the

scientist requires the skills of the polymath of a person whose expertise

polymath of a person whose expertise

polymath of a person whose expertise spans across a significant number of

spans across a significant number of

spans across a significant number of different areas of a person who embodies

different areas of a person who embodies

different areas of a person who embodies the enterpreneur spirit and curiosity of

the enterpreneur spirit and curiosity of

the enterpreneur spirit and curiosity of know Na da da Vinci in 2006 Singh LT

know Na da da Vinci in 2006 Singh LT

know Na da da Vinci in 2006 Singh LT Smith the former CEO of Google said that

Smith the former CEO of Google said that

Smith the former CEO of Google said that five exabytes of data were created since

five exabytes of data were created since

five exabytes of data were created since the dawn of humanity until 2003 and that

the dawn of humanity until 2003 and that

the dawn of humanity until 2003 and that today we are generating the same amount

today we are generating the same amount

today we are generating the same amount of data every two days that’s a huge

of data every two days that’s a huge

of data every two days that’s a huge amount of data but the good news is that

amount of data but the good news is that

amount of data but the good news is that the technology today enabled us to

the technology today enabled us to

the technology today enabled us to handle process and analyze all the best

handle process and analyze all the best

handle process and analyze all the best amount of information with the help of

amount of information with the help of

amount of information with the help of artificial intelligence of machine

artificial intelligence of machine

artificial intelligence of machine learning big data is being used for a

learning big data is being used for a

learning big data is being used for a number of things for better profiling

number of things for better profiling

number of things for better profiling users for doing personalized

users for doing personalized

users for doing personalized recommendations for

recommendations for

recommendations for health care and diagnosis for predicting

health care and diagnosis for predicting

health care and diagnosis for predicting political revolution preventing crime on

political revolution preventing crime on

political revolution preventing crime on even creating fine arts it’s clear the

even creating fine arts it’s clear the

even creating fine arts it’s clear the big data enhances human potential at

big data enhances human potential at

big data enhances human potential at Alta data analytics we help large

Alta data analytics we help large

Alta data analytics we help large organizations drive actionable insights

organizations drive actionable insights

organizations drive actionable insights from all kinds of data to make a

from all kinds of data to make a

from all kinds of data to make a difference and then when there’s a

difference and then when there’s a

difference and then when there’s a social debate like in the months after

social debate like in the months after

social debate like in the months after the brexit we can analyze the data and

the brexit we can analyze the data and

the brexit we can analyze the data and understand and map where the social

understand and map where the social

understand and map where the social debate happens and what communities were

debate happens and what communities were

debate happens and what communities were created we can see how the remainders

created we can see how the remainders

created we can see how the remainders were opposed to the live community how

were opposed to the live community how

were opposed to the live community how the media community was seated in the

the media community was seated in the

the media community was seated in the middle kind of neutral how there was a

middle kind of neutral how there was a

middle kind of neutral how there was a technical community which who were the

technical community which who were the

technical community which who were the people talking about the economic and

people talking about the economic and

people talking about the economic and social and political implications of the

social and political implications of the

social and political implications of the back seat that were closer to the

back seat that were closer to the

back seat that were closer to the remainer community we can see us all the

remainer community we can see us all the

remainer community we can see us all the communities emerging like the Scottish

communities emerging like the Scottish

communities emerging like the Scottish the supported Scottish which are closer

the supported Scottish which are closer

the supported Scottish which are closer to the testes of the remainders or how

to the testes of the remainders or how

to the testes of the remainders or how the US Republicans appear more in the

the US Republicans appear more in the

the US Republicans appear more in the side of the brexit we can also analyze

side of the brexit we can also analyze

side of the brexit we can also analyze and understand who were the most

and understand who were the most

and understand who were the most influential people in the debate and how

influential people in the debate and how

influential people in the debate and how the median is our birth position and

the median is our birth position and

the median is our birth position and even predict what agendas will prevail

even predict what agendas will prevail

even predict what agendas will prevail and as for a movie we can analyze far

and as for a movie we can analyze far

and as for a movie we can analyze far more than what the analysts do that what

more than what the analysts do that what

more than what the analysts do that what that critics say would say in Leonardo

that critics say would say in Leonardo

that critics say would say in Leonardo DiCaprio’s the documentary before the

DiCaprio’s the documentary before the

DiCaprio’s the documentary before the flood documentary to raise awareness

flood documentary to raise awareness

flood documentary to raise awareness about climate change we did this we

about climate change we did this we

about climate change we did this we capture all the photo runs so that we

capture all the photo runs so that we

capture all the photo runs so that we could analyze them understanding how

could analyze them understanding how

could analyze them understanding how many of them DiCaprio Pearson how many

many of them DiCaprio Pearson how many

many of them DiCaprio Pearson how many of them policymakers appear to

of them policymakers appear to

of them policymakers appear to understand and determine which were the

understand and determine which were the

understand and determine which were the aspects of climate change then trigger

aspects of climate change then trigger

aspects of climate change then trigger more debate or we can understand as well

more debate or we can understand as well

more debate or we can understand as well which of the visual sequences were share

which of the visual sequences were share

which of the visual sequences were share and amplified in social media

an infection we can even predict trends

an infection we can even predict trends in fashion by analyzing the photographs

in fashion by analyzing the photographs

in fashion by analyzing the photographs that people share in Instagram with

that people share in Instagram with

that people share in Instagram with hashtags such as outfit of the day we

hashtags such as outfit of the day we

hashtags such as outfit of the day we collect all the hundreds of thousands of

collect all the hundreds of thousands of

collect all the hundreds of thousands of photos then we can analyze the

photos then we can analyze the

photos then we can analyze the underlying influence of people sharing

underlying influence of people sharing

underlying influence of people sharing those photos because we can see who

those photos because we can see who

those photos because we can see who interacts with who and then see what

interacts with who and then see what

interacts with who and then see what patterns emerge in the photos that the

patterns emerge in the photos that the

patterns emerge in the photos that the most influential people share it’s a

most influential people share it’s a

most influential people share it’s a paradigm shift where we used to make

paradigm shift where we used to make

paradigm shift where we used to make decisions made based on intuition and

decisions made based on intuition and

decisions made based on intuition and guesswork now we can manage based on

guesswork now we can manage based on

guesswork now we can manage based on evidence we can move to data-driven

evidence we can move to data-driven

evidence we can move to data-driven decisions management guru Peter Drucker

decisions management guru Peter Drucker

decisions management guru Peter Drucker said that you can not manage what you

said that you can not manage what you

said that you can not manage what you cannot measure that’s now there’s no

cannot measure that’s now there’s no

cannot measure that’s now there’s no excuse you can measure and you can

excuse you can measure and you can

excuse you can measure and you can manage without the help of artificial

manage without the help of artificial

manage without the help of artificial intelligence machine learning and all

intelligence machine learning and all

intelligence machine learning and all the Big Data technologies we will not be

the Big Data technologies we will not be

the Big Data technologies we will not be able to handle all this data revolution

able to handle all this data revolution

able to handle all this data revolution but still the most important element in

but still the most important element in

but still the most important element in driving inside out of data is what makes

driving inside out of data is what makes

driving inside out of data is what makes a data scientist irreplaceable is a

a data scientist irreplaceable is a

a data scientist irreplaceable is a human factor

the key to turn data into insight lies

the key to turn data into insight lies in what we can do that machines can’t

in what we can do that machines can’t

in what we can do that machines can’t curiosity we all have access to Google

curiosity we all have access to Google

curiosity we all have access to Google with billions of data points in Google

with billions of data points in Google

with billions of data points in Google but it’s your curiosity what little

but it’s your curiosity what little

but it’s your curiosity what little Minds would you learn would you search

Minds would you learn would you search

Minds would you learn would you search and therefore also would you filter and

and therefore also would you filter and

and therefore also would you filter and how do you discover what’s relevant to

how do you discover what’s relevant to

how do you discover what’s relevant to you empathy and that is the key to

you empathy and that is the key to

you empathy and that is the key to connect with others and to understand

connect with others and to understand

connect with others and to understand what other people need Henry Ford said

what other people need Henry Ford said

what other people need Henry Ford said if I would get if I could have asked

if I would get if I could have asked

if I would get if I could have asked people what he wanted they would have

people what he wanted they would have

people what he wanted they would have said faster horses imagination is the

said faster horses imagination is the

said faster horses imagination is the key to his allies what nothing assists

key to his allies what nothing assists

key to his allies what nothing assists yet creativity creativity is the key to

yet creativity creativity is the key to

yet creativity creativity is the key to invent and articulate solutions to solve

invent and articulate solutions to solve

invent and articulate solutions to solve problems communications is the key to

problems communications is the key to

problems communications is the key to persuade to influence and to spread

persuade to influence and to spread

persuade to influence and to spread ideas that create change and leadership

ideas that create change and leadership

ideas that create change and leadership is the key to step up and move all these

is the key to step up and move all these

is the key to step up and move all these people to action and at the center of

people to action and at the center of

people to action and at the center of them all is curiosity because it is

them all is curiosity because it is

them all is curiosity because it is curiosity for emotions that drives

curiosity for emotions that drives

curiosity for emotions that drives empathy it is curiosity for ideas the

empathy it is curiosity for ideas the

empathy it is curiosity for ideas the tribe’s imagination with his curiosity

tribe’s imagination with his curiosity

tribe’s imagination with his curiosity for solutions the drives creativity it

for solutions the drives creativity it

for solutions the drives creativity it is curiosity for influencing that draws

is curiosity for influencing that draws

is curiosity for influencing that draws communications and it is curiosity for

communications and it is curiosity for

communications and it is curiosity for results that in the end drives

results that in the end drives

results that in the end drives leadership the most important skill of a

leadership the most important skill of a

leadership the most important skill of a data scientist is asking the right

data scientist is asking the right

data scientist is asking the right question to data the curiosity of a data

question to data the curiosity of a data

question to data the curiosity of a data scientist to us that drive those right

scientist to us that drive those right

scientist to us that drive those right questions to iterate to understand human

questions to iterate to understand human

questions to iterate to understand human issues to imagine the possibilities

issues to imagine the possibilities

issues to imagine the possibilities to create an articulate solutions to

to create an articulate solutions to

to create an articulate solutions to convey the message and insights with the

convey the message and insights with the

convey the message and insights with the right visuals to make them actionable

right visuals to make them actionable

right visuals to make them actionable those are the key elements that turn

those are the key elements that turn

those are the key elements that turn data into something meaningful it’s a

data into something meaningful it’s a

data into something meaningful it’s a set of skills that no artificial

set of skills that no artificial

set of skills that no artificial intelligence can match yet it’s it is at

intelligence can match yet it’s it is at

intelligence can match yet it’s it is at the intersection of Technology and

the intersection of Technology and

the intersection of Technology and liberal arts where magic happens if the

liberal arts where magic happens if the

liberal arts where magic happens if the Renaissance of the 14th and 17th century

Renaissance of the 14th and 17th century

Renaissance of the 14th and 17th century was the cultural force that drove

was the cultural force that drove

was the cultural force that drove humanity out of the Middle Ages and into

humanity out of the Middle Ages and into

humanity out of the Middle Ages and into the modern era the Big Data Renaissance

the modern era the Big Data Renaissance

the modern era the Big Data Renaissance is already unveiled as possibilities to

is already unveiled as possibilities to

is already unveiled as possibilities to push human the human race forward with

push human the human race forward with

push human the human race forward with the power of data it is up to us to

the power of data it is up to us to

the power of data it is up to us to imagine what data and machines will do

imagine what data and machines will do

imagine what data and machines will do for us remember that a fool with a tool

for us remember that a fool with a tool

for us remember that a fool with a tool is still a fool

big data and it’s big brains big data

big data and it’s big brains big data needs the curious brain of an artist to

needs the curious brain of an artist to

needs the curious brain of an artist to make a difference thank you

[Applause]

[Applause] [Music]