Transcriber: Lisa Rodriguez Reviewer: Rhonda Jacobs
One of the most famous Paleolithic caves is in Lascaux, France.
It's estimated to be nearly 15,000 years old.
The cave contains elaborate depictions, nearly 2,000 different abstractions
from human figures, animals, and abstract signs.
Now, the question is,
why did our earliest ancestors in the first place,
have these abstract drawings?
Simply stated, whether it was a ritual, a hunt,
they were trying to tell a story.
Let's fast forward to the 19th century.
Florence Nightingale, in 1855, had to illustrate to the army at the time
why they needed to invest in a health care system,
rather than guns and ammo.
She has a graph, as you can see behind me, that showed each month
and the mortalities by disease, like typhus and cholera,
rather than on the battlefield.
And as you can see, it's a starking example.
Now, what this graph did is it didn't just show information,
but it told a story with data.
Now, this graphic is particularly powerful
because it gave rise to the modern nursing system.
That leads us to where we are now.
We're facing another revolution of information.
Our social media accounts, our credit scores,
our finances, our search history,
the food we eat, and even the way we walk has been affected by data,
in particular, digital data around us.
I don't know if anybody has the walker pedometers,
but if you lay down at the end of the day, and you have 100 steps left,
you're like "Oh, crap, I've just got to get that last 100 steps in,
and I'm going to be done for the day."
But that's digital data that's affecting your life for the better.
We're currently facing a big data revolution.
Now, what's happening is that we're procuring so much information
that we can't reliably access it or analyze it with humankind.
So we have to write programs and applications to access this.
By the year 2020 alone, it's predicted by experts
that from the beginning of time - let's start right over here -
we're going to go all the way from Lascaux
into the year 2020, until the year 2015,
we'll have as much digital data in that one year alone, 2020,
as that entire process before that.
That's nearly 40,000 exabytes.
But to give context to what an exabyte is, it's 43 million Blu-ray movies.
So if you don't have a lot of time on your hands, get started now.
Putting this into more context,
if you were to read just the tweets alone today,
it would take you nearly two decades to read every one.
And on YouTube, we upload 72 hours every minute.
And if you're going to watch every YouTube video,
you better set aside 11 years to get that done.
I've been a data nerd my entire life.
(Laughter)
(Whoo!)
Yes!
Every kid has to have a laser photo.
(Laughter)
I was like, "This is too cool. I'm doing it."
My undergraduate and graduate work was in statistics,
and in particular, predictive analytics.
I graduated in 2007, in the midst of a recession,
and job opportunities were very scarce.
Data has always been an essential part of my fiber;
I consider it the red blood cells to my body.
So I thought, let's start a consulting business around analytics.
I remember calling people trying to explain to them,
"Yeah, this is Justin Richie. I'm helping you be data-driven."
They're like, "Where am I driving to? This doesn't make any sense."
One of my first engagements - I'm trying to explain to a client,
and I found writing some of the deep technical analysis -
I found millions of dollars of value just sitting there.
And all they had to do was change a little tiny process in their workflow -
no loss in jobs, and they don't even notice it.
Instead of delight and amazement, I was met with blank stares
and a team that was unwilling to change their ways.
Now, was it my inexperience, my age that invalidated my delivery?
Maybe.
But what dawned on me at that point
is that I wanted to make it my career mission
to not only communicate complex data,
but to tell a story with analytics.
Albert Einstein said,
"If you can't explain it simply, you don't understand it well enough."
Let's fast forward three short years.
The term "data science" all of the sudden came out of nowhere.
Analytic-minded individuals had an intense job demand in the market.
Data science was here to stay.
For those of you who don't know it,
you don't go to school to be a data scientist.
Look on U.S. Bureau and Labor Statistics, there's no "data scientist" anywhere.
It's a mishmash of computer science, software engineering,
subject-matter expertise, and my argument, design.
Now, data science is known as a job right now,
but soon it will understood to be known as a field.
Now, there is a spectrum of this talent
that falls under the quantitative and qualitative scale.
We're looking at the quantitative first.
First, we're going to talk about a data engineer. This is the "what."
This is people that write complex algorithms, machine learning
that powers your applications like Facebook and Instagram today.
In between the quantitative and qualitative,
we have the data analysts. They're the "how."
These are the people that use existing applications
and try to find trends in information.
That leads us to the qualitative side.
What I'm introducing in this talk today is called a data designer. It's the "why."
The most important piece of this entire process,
and we'll talk about this in a second.
But first, why do we need a data designer?
We've talked about data science, data engineer, data analysts.
So I want everybody to look to their left.
I want everybody look to their right.
That's a human being sitting next to you.
Now, what design teaches us is that design is creating experiences
and interfaces for human consumption.
Now, the difficulty in what we do is we design computers and applications
to be used and interpreted by machines, not by humans.
And it falls short of that process.
Applications now are growing at such a rapid pace
that they're now starting to tap into this experience
and this interface of this process.
But that's going to call on more qualitative and creative individuals
to help synthesize that information to give it meaning.
Let's talk a little bit more about what a data designer is.
We're looking at the infrastructure.
It's the "what."
They're taking the databases and the information
that's been procured by a data scientist, data engineer,
then they're designing the interface, the "how."
But more importantly, it's the experience, the "why," that we need to focus on.
Dieter Rams is one of my favorite industrial designers of all time;
he has a good design manifesto that I use as a data scientist
every single day.
He cites that, "It's unobtrusive, honest,
thorough to the last detail, long lasting,"
but more importantly, he stresses
that "good design is as little design as possible."
Now, breaking things down to the most essential elements
is it helps your story resonate more.
I've had the pleasure of my career being the bulk in health care,
and it was difficult enough to communicate to a board room
how to change certain little processes, but to communicate to an actual person,
a patient in a health care system, is the most challenging.
So imagine, if you will, you go to the doctor for your annual exam.
You eat okay, you like bacon, you don't run as much as you should,
you drink a little bit here and there.
Doctor comes in: "David, I want to tell you,
you're doing okay,
but you have a 1 in 8 chance of getting diabetes."
What are you going to say to yourself?
"I'm going to be one of the seven people that don't get it.
Check. See you next year."
(Laughter)
Now, your mind in your neocortex is using an emotional reaction
and blocking out that risk.
So, what do we do?
We need to tell a compelling story with this information.
So, let's flip this around; let's tell this story.
"David, the path you are on, you won't just kind of get it,
you are absolutely going to get Type 2 diabetes.
Your out-of-pocket health care expense
and combined with unregulated health care costs,
in three years, it's going to go to $17,000.
You're spending about $3,000 right now.
Not to mention, you're going to live to be 71, far below the average age.
But, with 30 minutes of regular diet, exercise, maintain a healthy weight,
you can even walk if you want to,
your cost goes down to $4,600. Twelve years longer to live.
That's a savings of almost $13,000 a year."
Now, what do you want to tell somebody?
$13,000 per year, for 40 years!
That's almost $1 million if you put that into an investment account.
More importantly, what are you going to spend that time with your family?
I'm making my mission, your mission to live a healthier lifestyle.
The "what" is I wrote a very complex regression model
and extrapolated their claims and clinical data,
but the "why" is I want you to live longer.
I love technology, and what I love about it is how it creates connections
that we never knew existed before.
We're writing applications that are changing the human condition.
But what we struggle right now
is we do not see the forest through the trees.
We're constantly creating technology and we can't keep up.
We're so focused on the infrastructure, we don't focus on the value, the "why."
How are we going to design an application to be used by people?
How are we going to create that new experience
that hasn't existed before?
We have extremely humble beginnings, like Lascaux
into the Florence Nightingale example, but our story remains true,
that we're born storytellers and our new medium is data.
And that leads us to where we are now.
Life is incredibly complex.
The graph behind me, as you see, is looking your actual human genomic data,
looking for markers in cancer and disease.
We're accepting incredible amounts of data that we can't reliably analyze
just with one person to do.
We have to write hoards of applications just to ingest this information.
But ultimately, we need humans and data designers
to ingest this information
for the experience of how do we extrapolate that.
My question to everybody and my goal
is that we want to make sure that you guys start with the experience.
We can build the infrastructure and the interface,
but we really want to tell a story with data.
Dare to be original, and always, always tell a story with data.
Thank you.
(Applause) (Cheers)