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Here's a question for you.

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When you think about a life cycle,
what's the first thing that comes to mind?

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Now I'm not a mind reader, but
I know whatever you're thinking is right.

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There's actually no wrong answer
because everything has a life cycle.

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One of the most well known examples
of a life cycle is a butterfly.

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Butterflies begin as eggs, hatch into
caterpillars and then become a chrysalis.

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That's where the real magic happens.

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Data has a life cycle of its own too.

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In this video, we're going to talk about
each of the stages in that life cycle

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to help you understand the individual
phases data goes through.

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The life cycle of data is plan,
capture, manage,

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analyze, archive and destroy.

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Let's start with the first phase,
planning.

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This actually happens well before
starting an analysis project.

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During planning, a business decides
what kind of data it needs,

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how it will be managed
throughout its life cycle,

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who will be responsible for
it, and the optimal outcomes.

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For example, let's say an electricity
provider wanted to gain insights

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into how to save people energy.

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In the planning phase, they might decide
to capture information on how much

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electricity its customers use each year,

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what types of buildings are being powered,
and

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what types of devices are being powered
inside of them. The electricity company

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would also decide which team members will
be responsible for collecting, storing,

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and sharing that data.
All of this happens during planning, and

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it helps set up the rest of the project.
The next phase is when you capture data.

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This is where data is collected from
a variety of different sources and

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brought into the organization.

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With so much data being created everyday,

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the ways to collect
it are truly endless.

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One common method is getting data
from outside resources. For example,

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if you were doing data analysis on weather
patterns, you'd probably get data from

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a publicly available dataset like
the National Climatic Data Center.

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Another way to get data is from
a company's own documents and files,

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which are usually stored inside a
database. While we've mentioned databases

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before, we haven't gone into too
much detail about what they are.

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A database is a collection of
data stored in a computer system.

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In the case of our electricity provider,
the business would probably measure data

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usage among its customers within
a database that it owns. As a quick note,

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when you maintain a database of customer
information, ensuring data integrity,

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credibility, and
privacy are all important concerns.

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You'll learn a lot more
about that later on.

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Now that we've captured our data,

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we'll move on to the next phase of
the data life cycle, manage.

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Here we're talking about how we care for
our data,

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how and where it's stored,

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the tools used to keep it safe and
secure, and

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the actions taken to make sure
that it's maintained properly.

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This phase is very important to data
cleansing, which we'll cover later on.

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Next it's time to analyze your data.
This is where data analysts really shine.

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In this phase, the data is used to solve
problems, make great decisions, and

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support business goals.

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For example, one of our electricity
company's goals might be to find ways

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to help customers save energy.

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Moving along the data life cycle
now evolves to the archive phase.

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Archiving means storing data in a place
where it's still available, but

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may not be used again.

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During analysis,
analysts handle huge amounts of data.

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Can you imagine if we had to sort through
all of the available data that's out

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there, even if it was no longer useful and
relevant to our work?

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It makes way more sense to archive
it than to keep it around.

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And finally, the last step of the data
life cycle, the destroy phase.

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Yes, it sounds sad, but when you
destroy data, it won't hurt a bit.

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So let's get back to our
electricity provider example.

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They would have data stored
on multiple hard drives.

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To destroy it, the company would
use a secure data erasure software.

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If there were any paper files,
they would be shredded too.

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This is important for protecting
a company's private information, as well

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as private data about its customers.

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And there you have it,
the data life cycle.

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And now that you understand the different
phases data goes through during its life

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cycle, you can better understand how
to approach the data analysis process,

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which we'll talk about soon.