What is the data life cycle?
The data life cycle, also known as the information life cycle, relates to the duration of data storage in your system. This life cycle describes all of the processes that your data goes through, starting with its first acquisition.
Every living organism goes through a succession of stages in life science: infancy, growth and development, productive maturity, and old age. These stages differ depending on where you are on the evolutionary tree. Salmon die soon after spawning, whereas whales can live to be grandmothers. A mouse, fox, and butterfly all have completely different life cycles, even though they reside in the same field.
Similarly, various data items will experience different stages of life at varying rates. As an example of a data lifecycle framework, consider the following:
1. Data creation, ingestion, or capture
You gather information in some way, whether you produce it via data entry, acquire it from other sources, or get signals from equipment. This step specifies when data values enter your system's firewalls.
2. Data processing
Cleaning and processing raw data for further analysis involves a number of steps. Data preparation often comprises integrating data from many sources, verifying data, and executing the transformation, however the order of processes may vary. As part of the data processing pipeline, data is frequently reformatted, summarized, subset, standardized, and enhanced.
3. Data analysis
This is where the magic happens, regardless of how you analyze and interpret your data. A number of analysis may be required to explore and interpret your data. This might imply data visualization and statistical analysis. It might also refer to the use of classical data modeling or artificial intelligence (AI).
4. Data sharing or publication
Forecasts and insights become choices and direction at this level. When you publish the results of your data analysis, your data becomes fully operational.
5. Archiving
Data is often saved for future reference once it has been gathered, processed, analyzed, and disseminated. It's critical to retain information about each item in your records, especially concerning data provenance, if you want your archives to have any future value.
In a never-ending circle, the data life cycle goes from the final step back to the start. Of course, one element has significantly complicated the way we deal with data in the twenty-first century.
Big data life cycle
It's no secret that data volumes have exploded in recent years and are only expected to continue to rise. More and more SaaS and online apps are being used by businesses, and more data is being collected from them. At the same time, more people throughout the world are using the internet, clicking links, capturing photos, and filling out web forms. Smart gadgets and the Internet of Things (IoT) are constantly discovering new methods to quantify everything in the world.
You don't have to (or want to) acquire all of the information in the cosmos. While having every scrap of data at your fingertips may sound appealing, data management issues rise with data volume. More data implies more money spent on data storage. The more data you have, the more data preparation and analysis resources you'll require. Companies that merely collect more data without a proper digital transformation plan rapidly end up with a digital landfill on their hands. Sure, they have a lot of information. But no one can locate what they need, and what they can discover makes no sense, so they can't rely on it to make business choices.
Build certain protections into your big data life cycle to help you scale up for big data without going crazy. Over-collecting data, poor data management, and hoarding obsolete data are three common causes of difficulties. Instead, try the following:
• Improve your data collecting method.
Don't gather all created data to avoid data overcollection. Create a strategy for defining and capturing just the data that is important to your project.
• Effective data management should be implemented.
Create an architecture that combines manual and automatic monitoring and maintenance to preserve the health of your data, and catalog it so it's easy to discover and utilize. (Find out what makes data healthy.)
• Remove any unneeded data.
Consider removing data or purging outdated records if they've outlived their usefulness. You'll want to bear in mind any legal duties to retain or destroy old documents, and create a clear data deletion timeline.
Note that recommendations to delete outdated data might be contentious. Some people adhere to the "never erase anything" mentality. They feel that saving all data for as long as feasible is useful in the long term. However, holding data that is no longer useful not only costs more, but it can also expose you to liability, putting you at danger. This is especially true when dealing with sensitive personal information.
We think that the value of data is determined by its use to the company. That is why it is critical to properly manage your data lifecycle.
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