Axle Networks Blog – Data lifecycle management (DLM) is the process of managing data from creation to deletion. It involves a systematic approach to data collection, storage, processing, and disposal, which ensures that data is used efficiently and properly.
Nowadays, where everything is digital, managing data effectively can help businesses make informed decisions, comply with regulations, and ultimately maintain a competitive advantage.
Now, let’s look at data lifecycle management strategies, including the stages of the data lifecycle, how to set it up, and some best practices to follow.
Data Lifecycle Stages
The data lifecycle consists of several stages, each requiring specific management techniques.
Hightouch lays out a solid foundation for data lifecycle management in their blog post, outlining the seven stages of the process, which are: data collection, data ingestion, data storage, data transformation and modelling, analytics, data activation, and data monitoring.
- Data Collection: Gathering data from various sources, such as SaaS tools, advertising platforms, IoT devices, etc., to provide insight into the business.
- Data Ingestion: Moving data from source systems to a centralised repository (data warehouse or data lake) using ETL (extract, transform, load) or ELT (extract, load, transform) processes.
- Storage: Storing collected data in a data warehouse, data lake, or data lakehouse to create a single source of truth for the organisation
- Data Transformation and Modeling: Altering, cleaning, and restructuring data to create data models and define key performance indicators (KPIs) for informed decision-making.
- Analytics: Leveraging insights from data to drive decision-making by visualising and understanding data using reporting tools.
- Data Activation: Syncing insights from the data warehouse to downstream tools of business teams to drive outcomes and personalised experiences for customers.
- Data Monitoring: Ongoing tracking of data health and state throughout the data lifecycle stages to prevent data downtime and resolve issues as they occur.
The data lifecycle management process may differ depending on the scenario. However, the seven stages of data lifecycle management establish a solid foundation for ensuring that data is effectively managed and utilised throughout its lifecycle to support business objectives and decision-making processes.
Sometimes you need to archive your data as part of your DLM process. How should you archive your data effectively? Read more about it in our previous article. To access it, click the link provided below.
Learn More: Data Archiving Strategies: Importance, Methods, and Best Practices
Setting Up a Data Lifecycle Management Strategy
To implement an effective data lifecycle management strategy, organisations should follow these steps:
- Identify data types and sources: Determine the types of data generated and collected by the organisation and their respective sources.
- Define data retention policies: Establish clear guidelines for how long data should be retained based on legal, regulatory, and business requirements.
- Implement data classification: Classify data based on its sensitivity, criticality, and business value to prioritise management efforts.
- Establish data storage and archiving solutions: Select appropriate storage and archiving solutions based on data classification and retention policies.
- Develop data deletion procedures: Define secure data deletion processes to ensure that data is properly disposed of when no longer needed.
Considerations in Data Lifecycle Management
When implementing a data lifecycle management strategy, organisations should consider the following factors: compliance, security, quality, and scalability.
Regulatory compliance ensures that data management practices comply with applicable data privacy and protection regulations.
Data security is an important consideration since breaches might mean exposing sensitive information to unauthorised individuals. So, you need to consider the security measures to protect data from unauthorized access, breaches, and data loss, in your DLM.
Data quality ensures that accurate and reliable information is maintained throughout the data lifecycle. Scalability allows for flexibility in managing growing volumes of data as the organisation expands.
Lastly, scalability is crucial for ensuring that the data management system can handle increasing amounts of data without compromising performance. It is important to regularly review and update data management practices to ensure they continue to meet the needs of the organisation.
Best Practices for Data Lifecycle Management
Aside from the 7 stages of data lifecycle management, there are also several best practices that you need to follow for your data workflow. One of the most important is a seemingly simple process, such as defining your data types and naming your files.
However, these practices are essential for maintaining the integrity and reliability of your data throughout its lifecycle.
Some of the best practices that you need to follow in data lifecycle management are:
- Defining Your Data Types: Understand the different types of data your organisation handles. This could include structured data (like databases), unstructured data (like emails, documents), semi-structured data (like XML files), and more.
- Creating a File Naming Process: Establish a standardised naming convention for your files. This can help in organising data and making it easier to locate and access.
- Implementing a Data Backup Plan and Creating a Data Archive Policy: Regular data backups are crucial for data recovery in case of data loss. An archive policy can help manage older data that might not be needed for day-to-day operations but is important to keep for other reasons.
- Archiving Hardly Used Data and Setting Data Deletion Guidelines: Not all data is accessed frequently. Archiving such data can free up valuable resources. Also, not all data needs to be kept indefinitely. Setting guidelines for when data should be deleted can help manage data effectively.
- Creating a Data Management Policy: This policy should outline how data is collected, processed, stored, shared, and deleted. It helps ensure everyone in the organisation understands their responsibilities when it comes to data management.
In addition to these, implementing best practices such as data classification, data storage, data backup and recovery, data archiving, and data disposal can effectively manage data and achieve long-term success.
Remember, a good DLM strategy prioritises data protection and disaster recovery. It provides structure and organisation to a business’s data, which in turn enables key goals within the process, such as data security and data availability.
Another aspect of the DLM strategy is selecting the right cloud service provider. Learn how to do it by visiting our previous article via the link below.
Learn More: Choosing the Right Cloud Service Provider: 5 Key Considerations
Conclusion
Data lifecycle management is an essential component of modern business operations, allowing organisations to use data efficiently, comply with regulations, and make informed decisions.
Businesses can optimize data management processes, reduce costs, and realise the full potential of their data assets by understanding the stages of the data lifecycle, implementing a comprehensive DLM strategy, and adhering to best practices.
So, do you want to increase the effectiveness and optimisation of your cloud data management?
Axle Networks IT Managed Services offers robust cloud management solutions tailored to your specific needs. With our team of experienced professionals, you can rest assured that your cloud-based resources and data will be protected from unauthorised access and potential security threats.