Data Lifecycle Management

What Do You Mean By Data Lifecycle Management?

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Every piece of information in this digital age is precious. For strategists, it’s a driving force, which torches them to the innovation and final decisions. But, handling critical details and putting them together for creating something exceptional is not a cakewalk. This is simply because of the rapidly increasing volume and complexity of datasets. To counter these problems, Data Lifecycle Management can prove a milestone. 

The global Enterprise Data Management market size was valued at USD 147840.15 million in 2022 and is expected to expand at a CAGR of 13.66% during the forecast period, reaching USD 318814.03 million by 2028, as per a report.

So, what is it? Let’s find out. 

Data Lifecycle Management: An Introduction

This is about the whole life journey of datasets. This kind of management is a strategic approach to keep the data circle going on and on. It encompasses from its creation to archiving, making all arrangements to store, organize, and maintain every piece of information in a way that resonates with the objective of an organization, aligns with regulations and costing. 

It represents a lifecycle of data, which indicates various phases being involved. The data strategists plan for every phase as to how to manage data in it. 

So, how it is planned in every stage – let’s find out here.  

Step 1. Data Creation/Acquisition

Data creation refers to pooling datasets from internal and external sources. Internal sources can be business processes or backend operational data. And for external sources, the details relating to customers, partners, and third-party providers can help. 

So, this gives a rise to a data repository, which has tons of details. It’s like an ocean, where finding a few datasets can be like an uphill battle. This can interrupt drawing value out of it. So, analysts and managers properly tag and create meta-data tagging so that reaching out to desirable information and its sources no longer seem a hassle.

Step 2 .Data Storage

After creation, your records require a safe IT infrastructure to stay secure and scale easily at any point of time. This can be the cloud support, or on-premises servers. If the data count exceeds millions of records, cloud, or server can be the best alternative. Or, it can be a hybrid solution. 

Actually, there are certain factors, such as accessibility, performance, cost, volume, and redundancies that act as indicators to finalize storage for your records. 

Step 3. Data Processing and Analysis

Data processing refers to employing various methods and tools to refine the structure and make them all ready to draw intelligence. This process helps you to reach out to meaningful insights for business intelligence or machine learning, which becomes easy if the database is clean and transformed. 

The processes like typos removal, enrichment, normalization, validation, verification, and integrity are executed to filter and place valuable, reliable, and useful details in corporate records. 

Step 4. Data Usage

This very next phase is associated with utilizing insights from the processed data. This can be for decision-making to improve customer engagement and experience, or reporting to analyze various associated business operations in the backend. The reason can be any of these, but the aim is to make things better for attracting overwhelming revenue and profit. 

While doing so, security is a must-have. It is critical to secure information that is particularly sensitive or of clients.  

Step 5. Data Archiving

Archiving of data is all about storing obsolete data or the records that are less useful. It is obvious that fresh pieces of information replace old data, making them less relevant. So, those details can be shifted to secondary storage or server. All in all, archiving can help you to minimize data management costs. The fresh data can be restructured and managed regularly, but archiving needs one-time efforts. 

Step 6. Data Retention and Compliance

The next phase defines how to retain and regulate databases of an organization. Instead of randomly managing, prepare a proper strategy while abiding by the regulatory requirements, such as General Data Protection Regulation (GDPR). Always follow these two things to safely manage your precious corporate records without compromising on hacking. Data Lifecycle management is incomplete without it. To make this cycle agile, always define a period of time when you refine your databases and minimize penalties.

Step 8. Data Deletion

The longevity of your data depends on how effectively you manage them. Corporate records require security and prevention from unauthorized access.  A well-defined privacy policy can help in doing this.  Although it’s critical and indeed difficult, data compliance like CCPA and GDPR can never be ignored, especially when penalties for non-compliance are severe. 

From time to time, deleting unnecessary or obsolete datasets is a must. So, schedule its deletion prior to carrying it out at the end.

Best Practices in Data Lifecycle Management

Now that you know the significance and the entire lifecycle of data management, let’s explore a few best practices that can strengthen your data lifecycle. 

  • Define Clear Policies

Here, the policies are concerned with the entire management of the data process. Like privacy policies, establish a step-by-step procedure to-do and not-to-do in a draft of data management policies. Outline how every step will go on, like its creation, storage, processing, archiving, and retention. Also, align the role to specific specialists who are concerned with them. Also, look into whether these policies resonate with business goals and regulatory requirements.

  • Metadata Management

Metadata is a small description that represents what the data are all about. It helps in tagging, and hence, eases searching and navigation. These all things are indeed crucial because the whole categorization of your database is based on metadata. So, implement it carefully as per category and attributes. 

  • Automate Where Possible

Digitization makes things faster. To follow it, you should focus on automating the whole data cycle. So, think of various tools and techniques that can streamline the integrated subsets, such as data ingestion, archiving, and processing. This can make things error-free and also, contribute to overwhelming production in a short span.  

  • Regular Auditing

Auditing defines how to make your records error free and resonating with goals. This is an important part, which should be strictly strategized to be carried out periodically. Examine IT infrastructure to ensure that datasets are all aligned as per data management companies’ policies and regulatory requirements. Proactively check data access controls, encryption, and retention policies.

  • Data Encryption

Encryption is the way of securing data during their transfer. It protects vital records. Therefore, place it in your data security policy’s components. 

  • User Access Control

Access control defines who is likely to see, edit, or update datasets. Since these records are  luxuries, you should restrict their access to a competent person who owns the right.  

  • Backup and Disaster Recovery

Backup and disaster recovery is a proactive strategy to prevent unexpected attempts, such as hardware setback, cyberattacks, or pandemics. It should be in place to deal with these unsolicited situations. 

  • Data Training and Awareness

Proper training is compulsory for ensuring the best practices of data management in place. Make all stakeholders aware of how to prevent and deal with cyberattacks and maintain data integrity. 

  • Regular Data Cleanup

Cleanliness is also necessary for your data repository. Ensure that you remove redundant and obsolete datasets from time-to-time. This can significantly reduce your storage costs and improve data quality. 

  • Stay Informed About Regulations

Obtaining updates regarding data protection laws corresponding to your company is compulsory. You should adjust your strategy according to laws or regulations. 

Conclusion

Data Lifecycle Management is a technical and strategic approach to manage data effectively. An effective DLM strategy involves optimizing budget, data security, and complying with precise regulations while maintaining data quality.

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