As organizations invest more time and energy in data collection and analysis, understanding the data lifecycle becomes critical. In addition to the value, data processing can generate for organizations, data can also pose a significant risk to security.
Understanding how data flows through your organization’s data lifecycle is vital to securing sensitive data from potential attacks or data breaches. Every organization should take data lifecycle management seriously.
This post will take a close look at each step of the data lifecycle so your organization can take an active role in data management and protection.
What Is the Data Lifecycle?
The data lifecycle refers to the stages and processes that data goes through, from its creation or acquisition to its eventual disposal or archival. It encompasses the entire journey of data within an organization or system, from data creation and data sharing through data storage and eventual data deletion or retirement.
Data lifecycle management (DLM) is a detailed approach that organizations use to manage their existing data. DLM ensures that there is structure and organization to a company’s data, whether it is raw data, unstructured data, structured data, or data that needs to be deleted.
In addition, DLM can improve an organization’s data security measures and prepare them for a data breach, data loss, or a system failure that affects data usage.
The Phases of Data Lifecycle Management
Data lifecycle management is a comprehensive approach to data management that begins with data entry and concludes with data deletion. Separating data management into phases helps ensure data quality is being preserved at every stage of the data lifecycle.
Each phase of the data management lifecycle is governed by its own policies to ensure data value and security are maximized at each stage. The primary phases of data lifecycle management include the following:
- Data creation/collection
- Data storage
- Data processing/transformation
- Data analysis
- Data visualization
- Data usage
- Data management
This phase marks the beginning of the data lifecycle. Data can be created organically within an organization through various processes, or it can be collected from external sources such as customers, partners, or sensors.
It is crucial to capture and catalog data accurately from the outset to ensure its integrity and relevance throughout the data lifecycle. Data acquisition can be complicated by the sheer volume of new data generated by every business process and digital interaction.
It is critical to collect data, but if collected data is not aligned with your organization’s goals, it will serve little purpose.
After data is created or collected, it needs a secure and accessible storage environment. The choice of storage technology, whether on-premises or in the cloud, depends on factors like data volume, access requirements, and cost considerations.
Data storage policies dictate how data is stored, including backup, replication, and archival strategies to safeguard against data loss. Storing data collected by your organization also involves security policies.
Ask your team how data is being stored. Data encryption is a popular security measure for data at rest and in motion. Still, it might not be sufficient to guarantee the security of your company’s data.
Raw data typically requires refinement and transformation to be helpful. In this phase, data is cleansed, formatted, and enriched to make it suitable for analysis. Data transformation is vital to extracting value from enterprise data.
Data processing can involve tasks like data normalization, data integration, and the application of business rules to enhance data quality and consistency. Processing typically takes place after transforming data into a usable format.
Data analysis is the core of gleaning insights from your data. In this phase, various analytical techniques and tools are applied to explore, model, and extract meaningful information from the data.
Data analysts and data scientists play a crucial role in this phase, helping organizations make data-driven decisions. Analysis is critical to modern business operations and drives the adoption of big data technologies and techniques.
Data visualization is about presenting the results of data analysis in a clear and understandable manner. Visual representations like charts, graphs, and dashboards help stakeholders grasp insights quickly and make informed decisions.
Effective data visualization enhances communication and aids in identifying trends and patterns. Visualization is a significant part of modern data strategy. It enables non-technical stakeholders to easily interpret collected data, and it makes sharing data simpler.
Data usage encompasses using data for various purposes, including business operations, customer service, research, and reporting. This is also the data-sharing stage.
As a result, it is vital to have access controls and sharing policies.
Well-defined data access controls and usage policies ensure that only authorized individuals or systems can interact with the data, safeguarding its security and privacy. Controlling who can access and use data is an important task that should be given serious consideration.
Data management is an ongoing process that spans the entire data lifecycle. It involves activities such as data governance, data cataloging, metadata management, and data quality monitoring. Data management policies and practices ensure that data remains accurate, relevant, and compliant with regulatory requirements throughout its lifecycle.
However, not all data is actively used, but it may still have value for historical or regulatory purposes. Data archiving and retention policies determine how long data should be retained and where it should be stored during its inactive phase. Compliance with legal and industry-specific regulations is essential and is a part of data management.
Data deletion is also part of this phase in the data lifecycle. When data is no longer needed, it must be securely and permanently removed to mitigate potential risks. Organizations must follow established data deletion procedures and document their actions to demonstrate compliance with data protection regulations.
Some believe that data deletion is a separate phase of data lifecycle management, but deletion is still an essential part of data management.
The data lifecycle is an iterative and dynamic process that never ends. Data collection happens 24/7/265 thanks to digital platforms and tools. Your organization should take data lifecycle management seriously as it navigates the complexities of data in the digital age of business and information.