Data, a powerful force driving decisions, strategies, and innovations for modern enterprises, also presents a significant challenge. Managing it effectively throughout its lifecycle is no easy task, given the massive volume and diverse variety of data generated daily. On average, we generate 402.74 million terabytes of data every day. The painstaking task of storing and processing the data safely while ensuring it is compliant with evolving legal regulations is a constant struggle. However, mastering the different stages of data lifecycle management can open a wealth of business value, enabling organizations to maximize their potential and ensure security and compliance.
Whether you are an established business leader or a start-up looking to excel in handling data, this blog is for you. We will delve into what data lifecycle management is, the different stages of the data lifecycle, and the benefits it brings. We will also share best practices for handling data, ensuring its seamless flow, and maintaining confidentiality, integrity, and availability.
What is Data Lifecycle Management?
According to IBM, Data lifecycle management (DLM) is an approach “to managing data throughout its lifecycle, from data entry to data destruction. Data is separated into phases based on different criteria, and it moves through these stages as it completes different tasks or meets certain requirements.”
In other words, what is data lifecycle management? DLM defines a set of best practices to oversee the flow of information or data through its lifecycle, from creation to deletion. There are five significant stages of data lifecycle management, just like a software development lifecycle model that decides how data should flow in a system before it brings in any value: generate, storage, usage, archiving, and disposal.
Exploring the Key Stages of Data Lifecycle Management for Businesses
A data lifecycle consists of multiple stages throughout its useful life. Each stage is governed by specific policies aimed at maximizing the value of the data during its lifecycle. Next, we will discuss them in detail and understand how these different stages of data lifecycle management empower businesses to manage data effectively and securely.
Stage 1: Data Creation
The initiation of a new data lifecycle begins with data collection, with diverse sources available. These sources may include web and mobile applications, IoT devices, forms, surveys, and other channels. Although data can be produced in numerous ways, gathering all available data is not essential for business success. The addition of new data should continuously be assessed based on its quality and relevance to the organization.
Stage 2: Data Storage
Data varies in structure, which influences the type of storage a company employs. For instance, structured data typically utilizes relational databases, while unstructured data often relies on NoSQL or non-relational databases. So, determine the appropriate storage for the dataset while keeping in mind factors such as security vulnerabilities in processing methods, including data encryption and transformation. This helps you protect organizations from malicious threats if your data complies with privacy regulations, such as GDPR, assisting businesses to avoid substantial fines associated with non-compliance.
In addition, create a backup of stored data to safeguard against the consequences of accidental deletions or data corruption, protecting against both inadvertent changes and intentional threats like malware attacks.
Stage 3: Data Sharing and Usage
Data is made accessible to business users, and DLM allows organizations to establish who can access the data and for what purposes it may be utilized. Once available, the data can be used for various analyses, ranging from simple exploratory data analysis and visualizations to more complex data mining and machine learning techniques. These approaches contribute to business decision-making and effective communication with different stakeholders.
Moreover, data usage is not confined to internal applications alone. For instance, external service providers may leverage the data for activities such as marketing analytics and advertising. Internal applications could involve daily business processes and workflows, like dashboards and presentations.
Stage 4: Data Archival
Over time, data may cease to be beneficial for regular operations. However, it is crucial to retain copies of the organization’s data that is infrequently accessed for potential legal and investigatory purposes. If necessary, this archived data can be reinstated to an active production environment.
An organization’s DLM strategy should clearly outline when, where, and how long data should be archived. At this stage, data undergoes an archival process to ensure redundancy.
Stage 5: Data Deletion
In this data lifecycle management, data is removed from records and securely destroyed. Organizations will eliminate data that is no longer necessary to free up storage space for active data and also remove ‘dirty data’- data that is inaccurate, incomplete, or inconsistent- that, if ignored, may cost an organization an average of $15 million annually. During this phase, data is taken out of archives once it surpasses the required retention period or no longer serves a meaningful purpose for the organization.
Additionally, data destruction can have profound implications when it is improperly done and can lead to a cybersecurity or privacy risk. Moreover, prematurely destroyed data can create legal complications or compliance violations for your data modernization services. Therefore, thoroughly verify if there is any data or information you would still need in the future and weigh them against external and legal requirements.
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Popular Data Lifecycle Management Frameworks
Presented data lifecycle management frameworks have their own set of guidelines and best practices for managing data. Your organization can select the one that best suits your needs and goals. Here’s a list of the most popular data lifecycle management frameworks, such as AWS data lifecycle management and SAP data lifecycle management, among others, that organizations have been using to manage their data through its entire lifecycle:
- IBM Data Lifecycle Framework: It defines the data lifecycle in six stages: create, store, use, share, archive, and destroy.
- Microsoft Data Lifecycle Framework: This framework has five stages in its data lifecycle, including collect, enhance, organize, analyze, and govern.
- AWS Data Lifecycle Management: It encompasses five stages in its data lifecycle: ingest, store, secure, analyze, and archive.
- SAP Data Lifecycle Management: This framework follows a structured five-stage process: collect, store, process, analyze, and archive.
Also Read: Begineer’s Guide to Data Engineering for AI
What are the Benefits of Data Lifecycle Management
Having an adequate life cycle management process has several benefits for businesses, as it eliminates unwanted data or poor data quality. These two can directly impact a business’s bottom line, with research estimating that insufficient data may cost companies up to 25% of their potential revenue. So, let us explore the most common benefits:
- Ensures Data Security: It follows robust security measures and rigid backup procedures to lower the chance of data theft or breach, especially for businesses that handle sensitive information, like client information and financial data.
- Optimizing IT Operations: From creation to deletion, a systematic approach to handling data is essential in the modern business landscape. It also offers data updation and makes data accessible to users with in-depth insights, innovation, and integrity.
- Tracks Valuable Data: For any data that goes through data lifecycle management, you can trace it to see if it is still meaningful or not in the archives before permanently deleting it. Your business can still keep the information even after replacing it with more current data.
- Remove Outdated Information: Data that is no longer needed and becomes redundant and only creates problems such as unreliable insights, taking up unnecessary space, and exposing the organization to risks. With DLM, it is resolved judiciously.
- Frees Up Storage: When irrelevant data is deleted on a regular basis, you may have room for newer data and updated information. Naturally, having more space for storage leads to your system performing and operating at an optimal level.
5 Best Practices for Efficient Data Lifecycle Management
Data has become one of the most valuable assets for organizations of all sizes. However, managing data throughout its entire lifecycle—from creation to deletion—requires strategic planning and consistent execution. Here are five best practices for an efficient data lifecycle:
1. Implement Data Classification
To start effective data management, you need to have full knowledge of your existing data together with its importance ranking. Automated data grouping through a strong classification method sorts information according to value and regulatory requirements together with its sensitivity level. Set distinct owner responsibilities and create proper treatment procedures that match data classification types. This framework enables data analysts to make practical choices for data about storage, access controls, retention periods, and disposal methods.
2. Optimize Storage Hierarchies
Leveraging a tiered storage system approach lets you maintain fast data retrieval for active data by placing it on quick systems but offset costs while moving outdated data to lower-cost options. Companies should apply cloud storage effectively while using compression techniques to achieve lower costs. Both performance interests and financial commitments are better served when resources get strategically allocated through this method.
3. Set Robust Governance Policies
The effective management of data lifecycle operations depends strictly on having strong governance measures. Ensure your standardized data quality specifications have clear documentation alongside access controls based on roles and mandatory audit trails that follow all applicable regulations. Remember, data integrity remains secure when you have stringent governance policies that have been optimized for proper business utilization.
4. Automate Data Lifecycle Process
Manual data management processes often produce both unstable results and inconsistent outputs. Utilizing automatic scheduling, you can regularly do quality checks, specific archiving procedures, and retention policy execution alongside compliance report creation. The automation of these IT processes not only eliminates human errors but also makes your IT resources available for more strategic business directions.
5. Plan Secure Data Disposal
The end of the data lifecycle requires equal dedication to initial planning processes. Your organization should create standards to detect outdated data together with established methods for secure deletion. In addition, you must create document disposal procedures that satisfy regulatory needs and keep a record of data elimination. Having a proper data disposal method protects business-critical information while reducing storage costs.
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Final Reflections on Stages of Data Lifecycle Management
The significance of data lifecycle management cannot be understated. It helps organizations manage data effectively throughout their lifecycle and empowers them to make informed decisions about business goals and objectives. There is no doubt that as data complexity and volume grow, data lifecycle management will feel like an uphill battle. However, with proper strategies and a transparent vision, businesses can avoid juggling between inefficiencies, risks, and costs. For that to happen, companies must have an in-depth understanding of the Data lifecycle management framework. After all, the possibilities created by scalable, efficient, and secure data practices throughout its lifecycle are worth the effort to develop a data lifecycle management strategy.
In this blog, we discussed what data lifecycle management is, the benefits of the data lifecycle management framework, and the best practices to successfully implement the DLM strategy. Hopefully, this has given you a deeper understanding of the intricacies of the data lifecycle and its management and assists you in developing end-to-end visibility and control over the data flowing through systems and processes.
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