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In this lesson, we are going to learn

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about the data lifecycle.

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The data lifecycle describes managing data

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through its six stages of life.

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The six stages of the data lifecycle are creation, use,

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sharing, storage, archival, and destruction.

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Staging refers to an intermediate phase

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where data is temporarily stored

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and prepared for further use.

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It typically occurs between the data creation and use phase

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and serves as a space for activities like development,

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testing, and quality assurance.

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These activities are essential for validating

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and ensuring the integrity of data

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before it transitions to production

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where it will be fully utilized.

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While staging is not one of the primary six stages

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of the data lifecycle,

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it plays a critical role in the preparation process,

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facilitating smooth data handling before production

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and eventual use.

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To better understand development, testing,

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quality assurance, and the production environments

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within the data lifecycle

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let's imagine that you want to bring a fancy cake

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to a friend's party,

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but you've never made this particular cake before.

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So to make sure it turns out well,

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you decide to first make a smaller version of the cake

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and have your family try it.

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Development is like gathering all the ingredients

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and following the recipe to mix the batter

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and prepare the cake.

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Testing is when you check the cake

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and it's batter, making sure the measurements

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and consistency look just right.

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Quality assurance happens when you bake that smaller version

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of the cake for your family to taste,

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ensuring that that cake rises properly

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and tastes just like you expect it to.

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Production is when you confidently bake

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the full size cake for the party,

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knowing that it's going to turn out great

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because you've already tested it.

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Each step helps ensure the final product is perfect

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by the time that cake reaches the table.

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Let's explore the data lifecycle management, development,

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testing, quality assurance,

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and production phases in more detail.

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First, we have data lifecycle management.

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All data follows a lifecycle that begins with its creation

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and ends with its destruction.

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The six stages of the data lifecycle are creation, use,

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sharing, storage, archival, and destruction.

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Data is created when it's acquired, entered, or captured.

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For example, data is created when an email is received

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or a device generates logs.

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Data is used when it is accessed, processed, or modified.

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Importantly, this stage also includes an audit trail

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such as logs.

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Next, when data is shared, it is made available to others.

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This could be a financial report being shared

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with external partners via email or shared drive access.

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Next, the storage of data occurs when it is maintained

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for future use.

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For example, a company may save annual financial records

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in their database for annual trend analysis.

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Next, data in the archival stage is moved

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to long-term storage for recovery at a much later time.

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This could mean that customer records

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more than two years old are moved to an archive

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for historical reference.

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And finally, data destruction occurs when the data

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is no longer valuable and is securely destroyed

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to free up storage space.

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For example, financial records greater than seven years old

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may be deleted from the system

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as they reach their data retention policy limit.

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Second, we have development.

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Development is a key activity in creating

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and improving systems, applications,

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and processes that use data.

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Development involves writing code,

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designing system architecture,

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and configuring software to meet specific needs.

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For example, during the development phase

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of a new mobile application, developers write the code

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that defines the application's functionality and features.

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This stage is critical because it lays the groundwork

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for how the system will operate

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with multiple iterations typically being required

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before moving to the next phase of testing.

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Third, we have testing.

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Testing occurs when a system

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or application is evaluated to ensure

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it functions as expected.

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This stage involves running the system

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through various scenarios to identify bugs, errors,

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or inconsistencies.

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For example, during the testing phase

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of a new software update,

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testers may simulate real world conditions

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to verify that the software performs correctly

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under production-level workloads.

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Testing ensures that any issues are identified and addressed

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before the system moves to the production environment,

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ensuring a smooth user experience.

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Fourth, we have quality assurance or QA.

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Quality assurance ensures that the system

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or product meets the required standards

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and functions correctly before it is released.

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This stage involves reviewing

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and validating the entire system to catch any errors

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or issues that might have been missed during testing.

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Quality assurance ensures the final product

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meets quality expectations.

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For example, during the quality assurance phase

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of a new website launch,

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quality assurance specialists might test

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the site's functionality with multiple browsers

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from multiple operating systems

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to ensure it performs consistently for all users.

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In this way, quality assurance ensures the final product

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is reliable, user-friendly, and ready for production.

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Fifth, and finally, we have production.

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Production is the phase in the development lifecycle

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where the system or application is fully deployed

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and used in a live operational environment.

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This is where the system is actively functioning

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to support real users and business processes.

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For example, when a new e-commerce website is launched

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and made accessible to the public for the very first time,

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it enters the production phase.

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In production, customers can browse products,

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make purchases, and interact with the site in real time.

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So remember, the data lifecycle involves managing data

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through six stages, creation, use, sharing, storage,

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archival, and destruction.

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Staging is an intermediate phase

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where data is temporarily stored

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and prepared for activities such as development, testing,

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quality assurance, and production.

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These activities support the preparation

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and validation of data

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before it is fully deployed in the production environment.

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Once in production, data enters the use phase

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where it actively supports business processes.

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Each stage ensures that data is managed efficiently

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and securely throughout its lifecycle,

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from creation to eventual destruction.