October 02, 2023
No matter what industry you work in, data management is increasingly important for your career and performance. Data is everywhere and it's crucial for businesses all over the world.
Information is no longer separate bits of data—the internet of things (IoT) and big data mean that every piece of data is interconnected.
But, to keep your data healthy and secure, you need to be aware of the data life cycle and how it influences the quality of your organization's data.
What is the data life cycle?
As the term may indicate, the data life cycle is a concept that encompasses the whole “life” of it—from the moment of data creation, data updating, until the moment it's deleted. In this process, your data goes through numerous stages that make up the concept of data life cycle.
In life, every being goes through different phases: birth, childhood, adulthood, and old age. Similar to different living beings, not all data is the same, so not all data has the same life cycle.
A butterfly, for instance, has a shorter life span than an elephant. Similarly, different types of data have different life spans.
Data types and life spans
Knowing how different data types affect the data life cycle is important. Some data is more relevant for your business, so you need to allocate more resources to ensure its quality, security, and accuracy.
Such frequently required data is also known as hot data. This information is required for your daily business activities. It is also the most expensive type of data because you depend on it to make accurate daily business decisions; hence, you need to implement sophisticated data management to optimize it, clean it, and keep it safe at all times.
Warm data refers to information that is not as frequently accessed. It may be monthly or quarterly data that helps you make predictions or forecasts. Its security and health are also important, but it may be easier to maintain since you use it less frequently.
Finally, any information that you no longer use is known as cold data. For instance, you don’t use your sales data collected ten years ago. This information has already served its purpose and you no longer access it to make current business decisions. In general, cold data is typically stored in a data archive, or is deleted.
The life cycle of hot data may also be quite short compared to warm data. In other words, hot data turns to cold data quicker than warm data, since you need a continuous flow of hot data to make timely decisions.
Applying appropriate data management techniques regarding relevance is important to maintaining data quality and data integrity.
What is data lifecycle management (DLM)?
Data lifecycle management refers to everything an organization does to manage the data throughout its life cycle. As mentioned above, the life cycle is a sequence of stages your data goes through from its creation to its destruction. Depending on the type of business and data, the life cycle may be slightly different. In general, there are a few main stages.
What is the correct sequence of the data life cycle?
Creation of data
First, you create or capture unstructured data. Given the endless data sources nowadays, this data may be in a virtually unlimited number of formats: images, texts, videos, and others.
At this stage, businesses often obtain access to data by acquiring them from a data provider (external or public web data), manual entry of data in your organization (by your staff), and capturing data from device use.
Storing your data
Once you have access to more data, the next stage is to store it. This phase is also crucial to data security. Concepts like data backup and recovery process must be implemented to ensure data safety.
Data prep and analysis
In this stage, data is used within your organization to support daily activities or to inform decisions. At this point, after processing data, you can perform data analysis. Some types of data may also be shared with parties outside the organization.
Once data is used or no longer relevant, you can opt to archive or delete it. Archived data allows you to access the data in case you need it at a later time for certain purposes, such as verification. In general, archived data no longer requires any input from you—there is no maintenance or usage at this stage.
Data removal simply means removing the data from your system. Companies that archive data may run into high storage costs as more data comes in. Data destruction usually occurs within the archival system, but it is important to make sure that data destruction occurs according to the regulatory retention period and is compliant with all the current regulations.
Data lifecycle management objectives
Data lifecycle management is compulsory for different reasons. You need to manage the life cycle of your information because:
- You need to limit the misuse of data. Keeping track of your data during the life cycle ensures that it’s stored securely; limiting misuse or data breaches. Regardless of the type of database you have, you need to keep track of all of your data throughout each stage.
- It increases data availability. Once you know where your data is, you can access it timely.
- It increases data integrity. Entire data lifecycle management means that you have specific processes in place to manage your existing data. In turn, this decreases the chance of duplicated and/or irrelevant data.
What are the phases in the data security life cycle?
The data security life cycle is slightly different from lifecycle management. This concept refers to keeping your data safe. Cybersecurity is a pressing issue for many organizations around the world—data security life cycle improves visibility or, in other words, you know at all times where your data is.
In practice, data does not have a linear progress throughout the five life cycle stages discussed above. Data may not go through all the same stages. For instance, you may decide to skip archiving data and simply remove it from the system. In contrast, you may go back and forth several times before you destroy the data.
The data security life cycle follows the stages outlined below with a focus on how you can secure and protect your data.
In this stage, raw data is captured or created. In other words, it’s when data acquisition happens. To protect sensitive data, companies implement data classification and assign rights to the information.
Some data, like customer personal information, may be extremely delicate. You should control who has access to it to protect data integrity. You also need to make sure you comply with the data regulations in your region or country.
For example, many companies store email addresses since email marketing is a powerful tool nowadays. However, email is also a popular means for hackers and phishers to find their new victims.
On one hand, you need to make sure that the email addresses that come into your company are legitimate. On the other hand, you need to have data encryption systems in place to prevent email attacks. Furthermore, you need to control which employees have access to your customers’ emails and ensure the data generated is not leaked.
In the creation stage there are many security threats. You or your partners may use different web solutions that could be the entryway for other cybersecurity threats. This is why it’s important to establish clear and safe ways of capturing or creating new data—such as opting for trustworthy data providers for your external data needs.
In this life cycle stage, there are also numerous security threats for your business processes. For instance, you should establish access controls, so you limit the access to data, especially regarding sensitive information.
However, there are numerous ways to restrict access to data storage, such as rights management and data encryption. These are crucial to prevent data breaches or, in case of an unfortunate hack happening, executing effective disaster recovery solutions
Not all data has the same purpose or meaning. You need to find out which data is sensitive, as well as the right security protocols. In other words, which data requires protecting and why?
Companies can do that using classification, whether manually or automatically. Once you sort and label your data, you need to implement different security levels that regulate user access and control. It sorts your data, differentiating between shareable, mundane information, and confidential data.
Also, when you share the data collection with your stakeholders, you need to make sure the data item is suitable for public consumption. Data sharing employs the risk of leaking unwanted data, so all the information must be accurately classified and labeled.
Archive or delete data
It’s important to keep in mind that there are regulations regarding stored data and data deletion. You need to follow the local data privacy policies if you want to archive or dispose of your data.
All in all, the data life cycle and data governance is an extremely important concept for any business. It is crucial to conducting your daily activities. Your company can easily become the target of cyberattacks if you are unaware of data protection methods and practices throughout different stages.
It is of utmost importance to prevent unauthorized access. Data lifecycle management allows you to establish clear procedures and data processing actions that make the structured and unstructured data available when needed. It also helps to dispose of data when it becomes redundant.
This article was originally published on DATAVERSITY.
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