The world of data is changing and growing at an unprecedented rate. Data is a driving force in our society. The ever-expanding volumes of data from many sources that we now have access to have led organizations worldwide to rely on their data more than ever before.
One trend fueling this approach is the increasing availability and affordability of technology, which facilitates greater use for analytics purposes with precision accuracy and scalability, allowing businesses to grow exponentially.
Nowadays, organizations are in a constant state of flux and need to be agile enough to keep up with the fast-paced world. With this rise comes an increase in digital data, including emails, IoT devices that send text messages or collect information from sensors on your mobile phone.
Organizations can harness these sources of raw data for analysis, so they stay ahead of their competitors. One example is IBM Watson Analytics which provides companies with insights and analytics.
Data management is a complex process, and there are many steps to take to manage it properly. This post will discuss what data lifecycle management is, six phases of the lifecycle of data, the three main goals of data lifecycle management, and more!
Data Lifecycle Management Definition
Data Lifecycle Management (DLM) is the different stages that data goes through during its life, from when it’s created to when it’s deleted. Data lifecycle stages include creation, utilization, sharing, storage, and deletion. Each stage of the data lifecycle will be controlled by different policies that control protection, resiliency, and regulatory compliance.
What is Data Lifecycle Management?
Data lifecycle management is a comprehensive approach to managing an organization’s data. It operates according to policy-based systems that manage the flow of information across different applications, systems, databases, and storage media during its life cycle, from creation through retirement. Data Lifecycle Management is a way to make your data most valuable. There are many definitions for it, but one thing remains true: Data Lifecycle Management is not a specific product but rather an effective way for organizations from all industries to leverage their most valuable asset – DATA!
The data lifecycle management process ensures that the organization can understand inventories, maps and controls its data throughout as it’s created, modified, and stored. It will ensure that the most helpful and most recent records are accessible with speed and ease because much of this process can be automated into repeatable steps.
Data is a precious resource that needs to be understood and managed from the moment it’s created. Data lifecycle management (DLM) helps organizations do this by moving data through different lifecycle stages, adapting its usage at each stage following current business requirements.
Understanding how information moves between these phases allows you to better manage your company’s finances and understand what type of Big Data investments may make sense for your organization today and tomorrow.
The six phases of the lifecycle of data
The first step in the data lifecycle is creating it and then capturing it. There are several ways to do this, but we focus on three of them: getting existing data that outside entities have created; using human-driven input and other devices from your own business; or collecting information through sensors, Internet of Things elements (IoT), external databases, etc.
Data maintenance or storage
This is the organization, cleansing, and synthesis of entry signals. Data maintenance means that you are processing data to make it usable. The data has to be stored in places that fit them. This is important because the organization, access, and data control are essential for a company’s success.
Data is a raw material that can be used for various activities such as alterations, analysis, storage, and decision-making. Once data has been adequately maintained, it becomes information or knowledge to achieve your needs.
Data publication or release is a process of making data available to a broader audience. Data publishing often refers to sharing outside your enterprise and releasing it into the public domain with few restrictions when all appropriate authorities have approved them.
Data that have either not been used recently or need to be archived and indexed for future use are stored differently during this phase. Data archival means storing those data in a different location that is considered not required anymore by the organization. It is kept here in case it is needed any further to avoid facing any problem.
This consists of deleting duplicated data, which are eliminated to prevent the generation of statistical noise or the appearance of errors. It is the last stage in the data lifecycle. Data deletion refers to permanently destroying unnecessary data from storage and archives, meaning that it is deleted from the system entirely and removed for good.
What are the three main goals of Data Lifecycle Management (DLM)?
One of the top challenges companies faces when they grow and accumulate data is a data breach, which means that the data must be managed effectively throughout its lifecycle.
Managing data is challenging. Data lifecycle management has goals that need to be considered. These goals are essential for an unhindered and streamlined flow of information.
Three main goals that need to be considered when managing the database throughout its lifecycle can be categorized as follows:
The availability of data is imperative for the success of any project. Data being unavailable when it needs to be accessed can lead to cascading failures in other processes that depend on this data from a previous process. In order words, making sure your database has all available and accessible information at an instant will make management easier while preventing devastating errors later down the line as well.
Avoiding these failures requires careful consideration of all available options. One such option includes cloud-based solutions, which have shown themselves capable enough for meeting your needs while also being more accessible than local databases with limited bandwidth capabilities.
Data often drive businesses, so it’s crucial to ensure that the data is available and processed correctly.
Data is very versatile and can be used in many different areas. It’s essential to keep it organized so that you can use the data effectively when needed. You want your organization’s data consistent with all of its users, which will help them access accurate information whenever they need it!
Data can change over time, whether through alterations or modifications. This can result in data sprawl, where the same information is present on different systems and devices in slightly different forms. Therefore, it’s necessary to put an infrastructure in place to ensure the integrity and performance of data.
Data integrity is put at risk if various users allow simultaneous access to a database or the other backups are not adequately controlled in secure environments.
Data Security and Confidentiality
Once you have acquired the data, it needs to be stored safely. Structured data is typically held in an on-site or clouded database, while unstructured data is usually stored on file servers and/or in the cloud. Regardless of where it’s being stored, this data should be securely protected against access by unauthorized users.
Data is the new currency, and many risks come with it. Organizations should take risks seriously and keep their data secure by deploying a firewall system with antivirus software to prevent intrusion or danger from potential malware infections.
The potential value, both on regulated markets and black ones, makes maintaining appropriate access crucial, especially given recent GDPR or personal data regulations.
What is Hot, Warm, and Cold Data?
In its life, data is frequently classified according to a multi-temperature scale. The hot data is the most accessed, which needs to be replaced occasionally for decreased performance and business productivity. Warm data is still important but not as frequently accessible. Cooling data has ephemeral importance with little necessary use or access.
- Data that is heavily accessed and used every day for business activities are known as hot data. This type of data typically remains on Tier 1 storage, which is the most expensive form of storage because it is optimized to deliver fast access times.
- However, data used infrequently must be available online to satisfy specific business rules and regulatory requirements. This data is referred to as warm data.
- Data that no longer has an intrinsic value to the business is considered cold data. Cold data is usually archived off or deleted.
Data management is an integral part of any business and a necessary component to maintaining the flow of information. Three main goals should be considered when managing data throughout its lifecycle: ensuring that it can reach your customers in their preferred format for easy access, finding ways to make sure you have control over all collected customer information so there are no hiccups along the way, making sure everything flows smoothly from one phase or system into another seamlessly with no obstacles marking its path.
Managing data has become an essential aspect of businesses, and some opt for services when storing their data on the cloud. This article provides a brief overview of what is meant by data lifecycle management and the three main goals of data lifecycle management. We hope you found it informative! Check out our blog for more information about managing your company.