Key Data Lifecycle Stages (Infographic)
Infographic
With more data being collected and leveraged by organisation’s today, having a data lifecycle management plan in place is top priority for any organisation. Add to that various government regulations and increasing consumer desire for data protection; and now knowing your data’s lifecycle becomes critical.
What is a data lifecycle?
A data lifecycle follows the different stages that your data goes through from the first collection point, through to how it is utilised internally and finally removed from your organisation. Typically, this is governed within an organisation by a set of policies and procedures and will vary slightly dependent on the company, the data collected and any government regulations in that industry.
Why is it critical to know the lifecycle?
To really ensure data quality and proper management of data within your organisation, you first must understand the lifecycle of your data to inform your policies and procedures, and correctly handle it.
Along with focussing on data quality, it’s also imperative that data is effectively managed if it is to be used to help drive business decisions and growth in the company. Poor quality data that is not managed can cost your organisations millions in the long run.
7 Stages of the Data Lifecycle
Here’s the seven stages in the data life cycle that apply to most organisations. Most organisations will go through similar stages in the life cycle for their data, so it is important to ensure data quality is monitored and assessed in the earlier stages of the life cycle (Stages 1 to 3).
STAGE 1. DATA CAPTURE
This is the process of creating a database or data warehouse through data acquisition and collection from a variety of sources, data entry or integrating data captured by other systems. Data capture is a foundational step in the data lifecycle. enabling organisations to derive insights, make informed decisions, and create value from the information they collect.
STAGE 2. DATA MAINTENANCE
Processing data without yet deriving value from it. This includes tasks such as movement, integration, cleansing, changes, and the extract-transform-load (ETL) process.
STAGE 3. DATA SYNTHESIS
Applying data as an input to deduce data values using inductive logic. In other words, existing data sets can be combined to create multiple new data sets. These can then be used for further analysis, training , identify patterns in customer behaviours, reporting and even inform new processes.
STAGE 4. DATA USAGE
The application of quality data to inform decisions and activities performed by the organisation. This includes using data to deliver personalised customer experiences, tapping into new customer segments to increase sales and market share, or improving internal operations for increased productivity.
STAGE 5. DATA PUBLICATION
Data publication specifically fits into the later stages of the data lifecycle and plays a significant role in contributing to the accessibility, usability, and long-term value of data held within an organisation. In this stage, data experts can specify how data can be accessed, used, and stored. They can also define the long-term availability of data and any archival processes that are needed to ensure published data is maintained and is easily retrievable when needed.
STAGE 6. DATA ARCHIVAL
The process of copying data to another environment for storage. This is a crucial stage in the data lifecycle as it ensures valuable data is secure and remains usable over extended periods of time. A big part of this step is the development of a data archival strategy. This will inform the selection of the right data archival systems such as digital archives or data repositories based on the data formats, volume, and access requirements.
STAGE 7. DATA PURGING
This is the process of deleting / removing the data from the organisation. Data purging is essential for maintaining data privacy, security, compliance with regulations, and efficient data management. Systematic data purging is useful in identifying irrelevant or obsolete data such as closed or expired customer accounts. It facilitates secure removal of redundant data and prevents access to unauthorised parties. It also helps optimise storage space.
Conclusion
Understanding the key data lifecycle stages will help you and your organisation know exactly how to implement a data lifecycle management plan and more importantly ensure you have quality data to leverage and use to make informed business decisions.
