Improving Data Value with Data Lifecycle Management

As organizations continue to collect and analyze larger volumes of data, it’s essential that they have strong data management practices in place to ensure that their data remains a valuable asset and doesn’t become a liability. This has led to a growing emphasis on data lifecycle management.

What is data lifecycle management (DLM)?

Data lifecycle management refers to the process of managing data throughout its entire lifespan, from creation to deletion. As it relates to data governance, data lifecycle management involves ensuring that data is used and managed in a way that meets legal, regulatory, and ethical requirements, while also fulfilling the needs of the organization.

How it works

The process of data lifecycle management typically begins with data creation. This involves defining the data that needs to be collected and determining how it will be collected. Once the data is collected, it needs to be stored and organized in a way that makes it easy to access and analyze.

Data is also subject to change over time, so data lifecycle management involves implementing processes for data updates and modifications. It’s important to ensure that any changes to the data are properly tracked and documented, so that the data remains accurate and reliable.

As data ages, it may become less useful or even obsolete. In such cases, data lifecycle management involves determining when the data is no longer needed and should be deleted. This requires taking into account legal requirements, such as data retention laws, as well as the organization’s own policies and procedures.

Why is data lifecycle management important?

Data lifecycle management is critically important in relation to data governance because it ensures that data is properly managed throughout its entire lifespan. By implementing a comprehensive data lifecycle management strategy, organizations can maintain the accuracy, reliability, and usefulness of their data, while also complying with legal and regulatory requirements.

The benefits

One of the main benefits of data lifecycle management is that it helps organizations to identify and eliminate redundant or outdated data. This reduces the risk of using inaccurate or unreliable data, which can lead to poor decision-making and other negative outcomes.

Data lifecycle management also ensures that data is stored and managed securely, which is particularly important in today’s era of increasing cyber threats. By implementing strong security measures at every stage of the data lifecycle, organizations can minimize the risk of data breaches and other security incidents.

Moreover, data lifecycle management can help organizations to maximize the value of their data. By implementing processes for data analysis and data-driven decision-making, organizations can leverage their data assets to gain insights and make more informed business decisions.

Data lifecycle management goals

The goals of data lifecycle management for governance are aimed at ensuring that data is managed effectively throughout its entire lifespan. Here are some key goals:

1. To ensure data accuracy and reliability: By implementing strong data lifecycle management practices, organizations can ensure that their data is accurate, reliable, and consistent. This helps to minimize errors and improve decision-making based on the data.
2. To ensure data security: Strong data lifecycle management practices can help to ensure that data is stored, processed, and accessed securely. This helps to minimize the risk of data breaches and other security incidents that could compromise sensitive data.
3. To ensure compliance with legal and regulatory requirements: Data lifecycle management helps organizations to comply with legal and regulatory requirements related to data management, such as data privacy and retention laws. This helps to minimize the risk of legal penalties and reputational damage.
4. To improve data accessibility: Data lifecycle management practices help to ensure that data is organized in a way that makes it easy to access and analyze. This helps organizations to leverage their data assets to drive better business outcomes.
5. To optimize data storage and management costs: By implementing data lifecycle management practices, organizations can minimize data storage and management costs by eliminating redundant or outdated data.
6. To enable effective decision-making: Data lifecycle management helps to ensure that data is properly managed and organized, enabling organizations to make more informed and effective decisions based on their data assets.

DLM statistics to consider

Recent research and statistics show that data governance is increasingly emphasizing the importance of data lifecycle management. Here are some key findings:

• According to a study by Gartner, by 2022, 90% of corporate strategies will explicitly mention information as a critical enterprise asset and data lifecycle management as a critical competency.
• A survey by Deloitte found that 65% of executives believe that data management is a critical challenge for their organization, and 59% believe that data governance is a high priority.
• Another study by Experian found that data quality and data governance were among the top challenges facing organizations when it comes to data management.
• The COVID-19 pandemic has also highlighted the importance of data lifecycle management, as organizations have had to rapidly adapt to changing business conditions and manage large volumes of data related to health and safety protocols.

In summary, the rise of big data, artificial intelligence, and other advanced technologies has made data lifecycle management even more critical. As organizations increasingly rely on data to make strategic decisions, it’s essential that they have strong data governance and data lifecycle management practices in place to ensure that their data is accurate, reliable, and secure.

Data Lifecycle Management (DLM) steps

Data lifecycle management involves several components and steps to ensure that data is properly managed throughout its lifespan. These components and steps include:

1. Data collection: This involves defining the data that needs to be collected and determining how it will be collected. This includes identifying data sources and establishing data collection protocols.
2. Data storage: Once data is collected, it needs to be stored securely and organized in a way that makes it easy to access and analyze. This involves implementing data storage infrastructure and processes, including data backup and recovery procedures.
3. Data processing: Data needs to be processed and transformed into a usable format for analysis and decision-making. This includes data cleaning, transformation, and integration processes.
4. Data analysis: Data analysis involves using various tools and techniques to analyze and derive insights from the data. This includes data visualization, statistical analysis, and machine learning algorithms.
5. Data archiving: As data ages, it may become less useful or even obsolete. In such cases, data lifecycle management involves determining when the data is no longer needed and should be archived or deleted.
6. Data disposal: Once data is no longer needed, it must be disposed of properly. This involves implementing secure data disposal methods that comply with legal and regulatory requirements.
7. Data governance: Data governance is the overall management framework that guides the data lifecycle management process. It involves establishing policies, procedures, and best practices for data management, including data security, privacy, and compliance.

What’s ahead for DLM

As the volume of data increases, the future of data lifecycle management will likely involve the adoption of more advanced technologies and strategies to manage and leverage data effectively throughout its entire lifecycle.

Here are some potential developments that may occur:

• Increased emphasis on automation: With larger volumes of data, it becomes more difficult for humans to manually manage and process everything. As a result, there may be greater reliance on automated tools and workflows to help streamline data management tasks.
• Greater use of machine learning and AI: Machine learning and AI technologies can be used to analyze large datasets and extract insights more efficiently. As the volume of data grows, the use of these technologies is likely to become more widespread.
• Enhanced data governance: With more data comes greater risk, including the risk of data breaches or compliance violations. To mitigate these risks, there may be greater emphasis on data governance and security measures to ensure that data is properly protected and used in compliance with relevant regulations.
• Expansion of cloud-based solutions: As data volumes grow, companies may increasingly turn to cloud-based solutions for data storage, processing, and analysis. This allows organizations to scale their infrastructure more easily and cost-effectively than traditional on-premise solutions.

Overall, the future of data lifecycle management will likely involve a combination of advanced technologies and strategies to effectively manage and leverage data at scale.

Streamline Data Lifecycle Management with BigID

BigID’s approach to data lifecycle management is designed to provide organizations with a scalable, efficient, and secure way to manage their data throughout its lifecycle.This includes solutions for data discovery, classification, cataloging, and protection, as well as tools for compliance and risk management.

BigID’s data intelligence platform leverages advanced technologies such as machine learning and AI to automate many data management tasks, helping organizations to more efficiently and effectively manage large volumes of data. The platform also includes robust data governance and security features to help organizations comply with relevant regulations and protect sensitive data throughout its entire lifecycle.

To ensure compliance and streamline your data lifecycle management— get a 1:1 demo with BigID today.