Key Takeaways
- Once a healthcare organization reaches a certain scale, having a sound data lifecycle management strategy is essential.
- DLM guarantees not only the safety, correctness, and accessibility of an organization’s data, but also that it complies with all applicable data laws.
- To gain even more insight and control over the data consumption in your organization, you can take the next step after developing a data lifecycle management system and put it into practice.
An organization can prevent data risks with the help of data lifecycle management, which also facilitates the identification and implementation of necessary data quality enhancements. It is a crucial subject to discuss when talking about interconnected business operations that share or modify data. The generation of data at its place of origin, its usage in the business operations that depend on it, and its final retirement, destruction, or archiving are all parts of the data lifecycle. For data that is either necessary for many business processes or essential for key business operations, an organization benefits from specifying data consumption and the accompanying dependencies across business processes.
According to research, it’s beneficial for people who work with data to be involved throughout the data life cycle. 78% of executives who participated in our 2021 data health study said it was difficult to use business data to make decisions. But something interesting came out of the study. Executives who primarily deliver or consume data report having low levels of confidence in their data and have a weak sense of how strongly their decisions are influenced by it. Executives who work with data on both sides, however, claim to have a better knowledge of it and to make more data-driven decisions.
What is the data management lifecycle in healthcare?
Data lifecycle management in healthcare is a policy-based method for controlling the data flow in a data system from the point of formation and initially stored to the point when it is no longer required and is erased.
Processes for lifecycle management are automated by DLM products. Typically, they segregate data into distinct levels in accordance with predetermined policies. In accordance with those requirements, they also automated data transfer from one layer to another. Newer and more often accessed data is typically typically kept on quicker and more costly storage media, while less important data is typically stored on slower, cheap media.
What are the data lifecycle management’s three key objectives?
More data than ever is handled by organizations, and that data may be kept on-site, at colocation sites, in edge settings, on cloud platforms, or on any combo of these platforms. There has never been a greater need for an efficient DLM strategy, but for that plan to work, it needs to be comprehensive.
Numerous sources list the following three objectives, or closely related ones, as the most crucial ones to fulfil in a successful DLM strategy:
1. Confidentiality and data security.
To ensure that private, confidential, and other sensitive information is continuously safeguarded against potential compromise, data must be stored securely at all times.
2. Data reliability.
No matter where it is stored, how many people are using it or working with it, or how many copies are kept, the data must be accurate and trustworthy.
3. Data accessibility
When and where they need access to the data, authorized users should be able to do so without interfering with their regular workflows or business as usual.
As organizations contend with an expanding body of compliance regulations, including the Sarbanes-Oxley Act (SOX), General Data Protection Regulation (GDPR), Health Insurance Portability and Accountability Act (HIPAA), and California Consumer Privacy Act, data security and confidentiality have become more crucial (CCPA).
Data lifecycle management, according to professionals in the field, is a holistic strategy to managing the data within an organization that includes both procedures and practices and application development.
Data retention laws and requirements vary by industry sector, and a strong DLM strategy aids firms in staying compliant. While ensuring compliance with data protection rules involving personal data and organizational records, DLM enables enterprises to handle data more efficiently and securely. Partnering with healthcare software developers can help you get it customized as per you organization’s standards.
5 stages of the data lifecycle management process
Data Lifecycle Management is a method that aids businesses in controlling the flow of data from the point of creation to destruction. Although the many stages of a typical data lifecycle have many interpretations, they can be summed up as follows:
1. Data Generation
The production or capture of data is the initial stage of the data lifecycle. The formats of this data include PDF, picture, Word document, and SQL database data. An organization often produces data in one of three ways:
- Data acquisition: is the process of getting already-produced information from sources outside the organization.
- Data entry: the manual entering of fresh data by organization staff
- Data capture: the collection of data produced by equipment used in a variety of organizational procedures.
2. Keeping
As soon as new data is produced within the organization, it needs to be stored, safeguarded, and given the proper level of security. To guarantee data retention throughout the lifecycle, a strong backup and recovery procedure should also be put in place.
3. Usage
Data are used to support organizational operations throughout the consumption phase of the data lifecycle. It is possible to examine, process, modify, and save data. To ensure that all data updates are fully traceable, an audit trail should be kept for all critical data. Additionally, data may be made accessible for sharing with others outside the organization.
4. Historical Data
Data is archived when it is copied to a location where it is kept in case it is ever needed again in a live production environment and then deleted from all live production environments.
Simply said, a data archive is a location where data is kept but isn’t used frequently. The data can be restored if necessary to a working environment.
5. Devastation
Even though you may want to save all of your data indefinitely, that is not possible due to the inevitable growth in the volume of archived data. Data destruction is under pressure due to storage costs and compliance concerns. The removal of all copies of a data item from an organization is known as data destruction or purging. Usually, it is carried out from a storage location for archives. During this stage of the lifecycle, the hardest part is making sure the data has been properly destroyed. It is crucial to confirm that data items have outlived their required regulatory retention period before destroying them.
A vital component of ensuring that Data Governance can be implemented successfully within your organization is having a clearly defined and documented data lifecycle management process.
Data Integrity assessments, remediation software, and validation services are just a few of the many services that Data works’ highly qualified CSV & Software Engineers offer as part of our offering.
Closing Thoughts
Organizations may secure information, reduce costs, and spot weaknesses in their data technology ecosystem by implementing DLM. Implementing DLM and supplementary software that alerts and detects compromises in real-time should be seriously considered by virtually any organization that handles sensitive or confidential data that needs to be safeguarded. You can effectively manage information and guarantee compliance from the outset by working with an experienced partner like Arkenea to develop the suitable DLM strategy.