In this lesson, we are going to learn
about the data lifecycle.
The data lifecycle describes managing data
through its six stages of life.
The six stages of the data lifecycle are creation, use,
sharing, storage, archival, and destruction.
Staging refers to an intermediate phase
where data is temporarily stored
and prepared for further use.
It typically occurs between the data creation and use phase
and serves as a space for activities like development,
testing, and quality assurance.
These activities are essential for validating
and ensuring the integrity of data
before it transitions to production
where it will be fully utilized.
While staging is not one of the primary six stages
of the data lifecycle,
it plays a critical role in the preparation process,
facilitating smooth data handling before production
and eventual use.
To better understand development, testing,
quality assurance, and the production environments
within the data lifecycle
let's imagine that you want to bring a fancy cake
to a friend's party,
but you've never made this particular cake before.
So to make sure it turns out well,
you decide to first make a smaller version of the cake
and have your family try it.
Development is like gathering all the ingredients
and following the recipe to mix the batter
and prepare the cake.
Testing is when you check the cake
and it's batter, making sure the measurements
and consistency look just right.
Quality assurance happens when you bake that smaller version
of the cake for your family to taste,
ensuring that that cake rises properly
and tastes just like you expect it to.
Production is when you confidently bake
the full size cake for the party,
knowing that it's going to turn out great
because you've already tested it.
Each step helps ensure the final product is perfect
by the time that cake reaches the table.
Let's explore the data lifecycle management, development,
testing, quality assurance,
and production phases in more detail.
First, we have data lifecycle management.
All data follows a lifecycle that begins with its creation
and ends with its destruction.
The six stages of the data lifecycle are creation, use,
sharing, storage, archival, and destruction.
Data is created when it's acquired, entered, or captured.
For example, data is created when an email is received
or a device generates logs.
Data is used when it is accessed, processed, or modified.
Importantly, this stage also includes an audit trail
such as logs.
Next, when data is shared, it is made available to others.
This could be a financial report being shared
with external partners via email or shared drive access.
Next, the storage of data occurs when it is maintained
for future use.
For example, a company may save annual financial records
in their database for annual trend analysis.
Next, data in the archival stage is moved
to long-term storage for recovery at a much later time.
This could mean that customer records
more than two years old are moved to an archive
for historical reference.
And finally, data destruction occurs when the data
is no longer valuable and is securely destroyed
to free up storage space.
For example, financial records greater than seven years old
may be deleted from the system
as they reach their data retention policy limit.
Second, we have development.
Development is a key activity in creating
and improving systems, applications,
and processes that use data.
Development involves writing code,
designing system architecture,
and configuring software to meet specific needs.
For example, during the development phase
of a new mobile application, developers write the code
that defines the application's functionality and features.
This stage is critical because it lays the groundwork
for how the system will operate
with multiple iterations typically being required
before moving to the next phase of testing.
Third, we have testing.
Testing occurs when a system
or application is evaluated to ensure
it functions as expected.
This stage involves running the system
through various scenarios to identify bugs, errors,
or inconsistencies.
For example, during the testing phase
of a new software update,
testers may simulate real world conditions
to verify that the software performs correctly
under production-level workloads.
Testing ensures that any issues are identified and addressed
before the system moves to the production environment,
ensuring a smooth user experience.
Fourth, we have quality assurance or QA.
Quality assurance ensures that the system
or product meets the required standards
and functions correctly before it is released.
This stage involves reviewing
and validating the entire system to catch any errors
or issues that might have been missed during testing.
Quality assurance ensures the final product
meets quality expectations.
For example, during the quality assurance phase
of a new website launch,
quality assurance specialists might test
the site's functionality with multiple browsers
from multiple operating systems
to ensure it performs consistently for all users.
In this way, quality assurance ensures the final product
is reliable, user-friendly, and ready for production.
Fifth, and finally, we have production.
Production is the phase in the development lifecycle
where the system or application is fully deployed
and used in a live operational environment.
This is where the system is actively functioning
to support real users and business processes.
For example, when a new e-commerce website is launched
and made accessible to the public for the very first time,
it enters the production phase.
In production, customers can browse products,
make purchases, and interact with the site in real time.
So remember, the data lifecycle involves managing data
through six stages, creation, use, sharing, storage,
archival, and destruction.
Staging is an intermediate phase
where data is temporarily stored
and prepared for activities such as development, testing,
quality assurance, and production.
These activities support the preparation
and validation of data
before it is fully deployed in the production environment.
Once in production, data enters the use phase
where it actively supports business processes.
Each stage ensures that data is managed efficiently
and securely throughout its lifecycle,
from creation to eventual destruction.