- So the cloud gives us almost unlimited space, right?
We've got the ability to spin up
virtual servers in a flash, and storage is cheap.
I mean, Google gives away 15 gigabytes,
and you can get a terabyte for $50 a year.
A terabyte, do you know how much data that is?
To some of us old-timers, it's almost inconceivably big,
but figure roughly 500 hours of video,
or hundreds of thousands of documents.
Anyway you look at it, it's a lot.
So what does this mean?
It means that the amount of data available is now enormous,
which brings a new problem.
How on Earth do you possibly use it?
How do you find what you're looking for?
How do you even know what to look for?
If you've ever wondered what people meant
when they talked about big data, this is it.
The absolute quantity of information available
means that it's impossible to work with it manually.
And here's another place
where digital transformation plays a role.
As we move away from systems of record
towards systems of engagement, we start acquiring
more interactivity to go with our information.
I've already talked about tools
like PowerPivot and Power BI.
I'm going to call out Tableau too, since that's widely used.
Now these are great for allowing you
to take your information from multiple sources,
and create graphs, charts, and other visualizations
to show you the data in various configurations.
What's better is that they let you change variables
on the fly, and see immediate results in the output.
Now this gives you the chance to model
how different factors can change your outcome.
But you know what's even better than that?
When you get into serious data mining these days,
you're starting to look at tools
that can do much more than rehash your data displays.
It can actually analyze your data for you,
and make recommendations.
Actually, let me back up a step.
The first concept we need to deal with is machine learning.
Machine learning gives computers the ability
to look at results of types of problems
and use those to make predictions for similar problems.
Then, continue to learn and refine
as they experience more variables.
A simple example of this would be how some websites
give you recommendations based on your declared likes
and dislikes, or even your viewing history.
Aha, says the computer.
She liked this and this.
They have these characteristics in common,
therefore, she will probably also like that.
And it's suggested to you.
And then, it takes your next response into account,
and your next, and next, and so on.
Another level of this is artificial intelligence.
I'm going to go with a very narrow definition here.
For me, artificial intelligence takes
the insights gathered through machine learning,
and instead of making recommendations,
actually makes business decisions based on them.
A possible example might be a supply system
for a restaurant chain.
Looking at past performance for various locations
during a range of seasons, and taking into account
fluctuations in weather, the economy,
and the price of tea in China,
the system deduces that there will be a spike in people
ordering drinks to go in South Carolina.
And so it increases the number of paper cups
shipping to the affected locations
without involving any human intervention at all.
This isn't science fiction, by the way.
This type of functionality actually exists today.
And it gets better!
Let's talk about cognitive computing.
Cognitive computing takes machine learning
and adds in all the new information we've learned lately
about how the human brain works!
Computers are programmed to collect and process data
the same way people process and think about data,
and interact with people using natural language.
Once again, this is not a fantasy.
This is real!
IBM's Watson is a great example.
One of the things it's being used for right now
is to improve medical care.
Watson Health Cloud looks at patient history
from current and previous doctors,
current condition, input from fitness trackers,
medical journals, clinical trials, and on, and on, and on,
and it puts it all together in a meaningful way
for doctors to suggest specific treatment
and achieve better outcomes.
Having ways to effectively use all the data available to us
means making better decisions, and getting better results.
In business, this means less cost and greater profits.
And if that's not an argument for digital transformation,
I don't know what is!
If you'd like to learn more about big data,
check out Learning Digital Business Analysis
in the course library.