- One of the great paradoxes of big data is that
sometimes all of the technological wizardry,
all the equipment, all the huge amount
of effort that goes into it,
is put into motion to help with something
as theoretically simple as choosing whether
to go left or right at the fork in the road.
The idea here is that big data is
frequently used to create small data.
The small amount of information, the direction,
the couple of restaurant recommendations
that the end user wants.
It's really another example of the general principal
that all analysis, all data analysis,
is an act of simplification.
You are taking something very complex,
something potentially huge and overwhelming,
and reducing it with hopefully
still maintaining the general form and feel
to something that is more usable and actionable.
Now, in terms of applications based on big data
some of the really common ones are things like
directions from point A to point B,
or maybe something like finding jobs online,
or getting health feedback.
By the way, I intentionally use cell phones
in all three of these because mobile devices
are going to be one of the greatest ways
that people interact with big data.
You've got the huge server farms on your end.
They've got their phone in their hand
at the other end and that's how you're trying
to meet with the end user.
Now, think about directions, just for example.
In terms of big data what you're doing is
you're bringing things in like
GPS coordinates for where the person is,
where they want to go.
You're bringing in map data,
so information about the roads,
the shape files that show the map.
You're bringing in traffic conditions,
historical patterns and real time estimates.
You're getting user preferences.
Do they like to be on the freeways?
Do they want the scenic route?
Do they want to avoid tolls?
And you can also bring in data
from other travelers to identify potential
slow downs or detours in the road.
This is a very complex set of data
and that's why it is big data.
It's the varietyness, the volume,
and things can be coming in quickly
with the velocity.
But you're boiling it down to,
"Go straight, turn left, turn right here."
That is big data being put into
the best possible use for an application
with consumer orientation.
Or take something like a job search
which can be a very time consuming, stressful,
unpleasant experience.
But with big data applications
you can take, for instance, a person's
physical location, where they work,
where they say they want to work.
You can match their skills,
you might even be able to assess
those skills directly.
You can look at the endorsements that people give them
and match those skills and endorsements
to the requirements of the jobs
that you have available.
And then, probably one of the most important things is,
you can connect that person
through a professional network
to other people who have similar jobs,
maybe even at the same company.
It tremendously facilitates the job search process.
And again, these are many different kinds of data.
That's how big data is used.
To compress it down to a small number
of jobs and recommendations for the end user.
And then as a final example, there are
health applications where you're doing things like
connecting with medical history.
Usually self reported, where the person
who owns the phone, they put the information
into their account, possibly collecting data
from their medical provider's records.
That's another big obstacle.
But you can also get sensor data
from their watch and their phone.
How much are they moving around?
What's their heart rate?
How did they sleep last night?
What's the temperature around them?
All of that information, again,
very dramatically different kinds,
can be integrated into the health app.
And you can even use that to connect with
emergency help.
Again, think about something that a person
needs all this information to put together
so the watch can say, "maybe you fell,"
and, "maybe you need emergency help."
That's taking big data and distilling it down
to the essence of the small data to get
the customer, or the client,
the thing that they want.
All three of these are fabulous examples
of how the big data revolution
has made it possible for companies
to provide unprecedented service,
both in terms of it's kind of service,
and in it's personalization, when that fulfills
the promise of big data in the consumer realm.