Primarily because you're able to take the,

or you take your data, take your information

and put it in the cloud,

put it in the central storage system.

It allows you to bypass the physical limitations

of the computers and the systems you're using

and it allows you to deploy the analytics

and storage capacities of advanced machines

that do not necessarily have to be your machine

or your company's machine.

Cloud allows you not just to store large amounts of data

on servers somewhere in California or in Nevada,

but it also allows you to deploy very advanced

computing algorithms and the ability to do

high performance computing

using machines that are not yours.

So, think of it as you have some information,

you can't store it, so you send it to storage space,

let's call it cloud, and the algorithms that you need to use

you don't have them with you, but then on the cloud

you have those algorithms available.

So, what you do, is you deploy those algorithms

on very large data sets and you're able to do it

even though your own systems, your own machines,

your own computing environments

were not allowing you to do so.

So, cloud is beautiful.

And, the other thing that cloud is beautiful for

is that it allows multiple entities

to work with same data at the same time.

So, you can be working with the same data

that your colleagues in, say, Germany,

and another team in India, and another team in Ghana,

they are collectively working

and they are able to do so because the information

and the algorithms and the tools and the answers

and the results, whatever they needed

is available at a central place.

Which we call cloud, so cloud is beautiful.

At the Big Data University which is an IBM initiative,

we have these courses people can take

and learn about data science,

but at the same time we provide this cloud based environment

for not only analytics,

but also for working with big and small data.

So one of the products that is integrated

with Big Data University is Data Scientist Workbench.

Data Scientist Workbench is an internet based solution,

you log in and the moment you log in,

you now have access to some

very advanced computing environment.

As simple as R and Rstudio and data

and algorithms to define the data set using OpenRefine,

but also the ability to work with very large data sets

using technologies like Spark.

So, the advantage of working with Data Scientist Workbench

is not only that you have the ability to work with

these advanced algorithms and two computing platforms,

but you also have the ability to work with

very large data sets because Spark

is integrated and it's all in the cloud,

you don't have to maintain it,

you don't have to download it,

you don't have to worry about updating it.

All is being done for you in the cloud

by the Data Scientist Workbench.