Most IT professionals realize that there is such a thing as a data lifecycle, but there’s no common rule on what it is. Lifecycle may be a misleading term, since most lifecycle lead to reproduction or recycling, and data doesn’t. But at least we can agree that the data lifecycle has some distinct phases during which it needs managed.
We’ve identified 6 phases of the data lifecycle that most data processes through, and good data management is one of the foundations on which rests the lifeblood of every company—its data.
Here are the 6 lifecycle phases in order.
How does the data enter your enterprise? When an employee creates a file, design research compiles results in a spreadsheet, data comes in through capture forms on your website, or any other form of data creation, that information automatically becomes part of your company’s data. This active data is stored locally on servers, in the cloud, or a host data center.
This is when data gets processed and synthesized in a variety of tasks. This is a fairly broad range of management actions, such as how data is supplied to the end users and how analytics such as modeling are performed.
Now is when the data is used and moved around your enterprise. Maybe it’s being transformed and enhanced by end users. Data usage can even be a product or service that your enterprise offers. This is where governance and compliance challenges arise.
This is one way that data can leave your enterprise. Say you publish a white paper that is downloaded by multiple companies, or you use data you’ve collected to send out invoices or investment statements to customers.
At some point in time, the data in your system will have no immediate use, and it’s time to archive it in case it might be needed in the future. This removes the data from your active environment and moves it off to storage.
When you no longer need data, it must be destroyed. This is another point in the data lifecycle where a governance and compliance issue might be raised. It’s important to ensure that the data has actually been destroyed properly.
Exceptions to the stages
Of course, there are exceptions to these lifecycle stages. It’s not to say that data must flow through these phases in order because sometimes data is used over and over through some of the stages while skipping others.
It also doesn’t describe the environments that exist for data. Data can exist in information silos where some of these stages don’t apply.
The main point to the data lifecycle is that data management and its distinct governance and compliance issues have phases that must be managed appropriately, which is an often cumbersome task for enterprises with large amounts of data flowing through.
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