Data Lifecycle Management (DLM) is a critical part of any organization’s goals. It provides the framework for how data is created, stored, accessed and disposed of. DLM has three main goals:
Data quality management ensures that there are no errors in your data to give you false information
Data governance ensures that all managers within an organization have access to the same information and follow the same guidelines for storing and disposing of it
Data protection measures ensure that only authorized personnel have access to sensitive data.
The first goal is what most people think about when they hear ‘data lifecycle’. The second two goals encompass long term planning, as well as short term maintenance. For instance, storage might be a problem from time to time depending on how much space you currently use versus your projected growth rate over the next five years; making sure everyone uses the same standards will help alleviate this issue down the line by avoiding pitfalls like customers not being able to access the server when a technician is building it.
Data Structure Management ensures that data can be easily structured, according to what type of information needs to be stored and where
Process Development Planning establishes clear procedures for storing new data as well as disposing of old ones.
Process Development Planning: what are the steps for process development planning?
Understand Data Types
Determine Appropriate Storage Locations (i.e., on a server, in a cloud)
Create Procedures and Policies to Follow When Storing or Destroying Data (examples might include data retention periods, who should have access to which types of data, etc.)
Data Structure Management: what is it good for? why do organizations need this type of management?
Ensures that data can be easily structured according to what needs to be stored where – example would show structuring information such as names into lastname__firstname format versus just firstnames alphabetically; also includes subcategorizing all data with metadata
Ensures the accuracy and reliability of data, ensuring that all information being stored or accessed can be easily verified – examples might include measuring how often a data set needs to be refreshed in order to keep it up-to-date while also determining what types of errors are most commonly present in said dataset (ex. spelling mistakes)
Data Protection Strategies: what are some steps an organization should take towards protecting their data?
Create appropriate procedures for handling confidential/sensitive customer Data (i.e., encrypting personal identifiers such as social security numbers)
Develop safeguards against natural disasters by either storing backup copies of information on other storage media or in the cloud
Data Storage Strategies: what are some of the considerations one should keep in mind when storing large quantities of data?
Decide whether it is more important to store a lot of small amounts of Data or fewer, larger backups. Large backup files take up less space on disks and use less energy than many smaller ones would. However, if you only have room for one copy then that means you will be unable to access your stored data unless all copies become unusable (i.e., lost due to fire) at once – which is unlikely but not impossible because natural disasters such as hurricanes can cause widespread damage. Therefore most organizations usually prefer having multiple copies located in different places so that at least some of the backups are available.
Evaluate cloud storage solutions and choose one that is compliant with your organization’s security requirements, regulations, and standards such as HIPAA. For example, if you store PHI data on a public cloud server then someone could access it without authorization or detect information about other clients by analyzing metadata associated with their account; so in this case you would need to use a secure private/hybrid option instead.
Ensure strong backup procedures – for instance, be sure to create multiple copies at regular intervals rather than just backing up once per month because if disaster strikes before the next monthly copy has been made then all of those days’ worth of Data will be lost forever.
Ensure that data is backed up locally in addition to any cloud backup solution you may be using. This will help protect against power outages and hardware failure (such as a hard drive crash).
Create an inventory of what Data your organization has, where it’s stored, who can access the data, how long it needs to be retained for legal or regulatory purposes – then establish rules about what can happen with each type of Data and set appropriate retention limits so there are no surprises when it comes time to delete old records.
Keep metadata on what changes have been made such as adding comments or changing tags which could make tracking back through older versions of data much easier if necessary; also keep track of software patches applied since these patches may have unintended consequences.
Establish protocols for data sharing and ensure that appropriate approvals are in place to allow any Data outside of your organization’s control, such as personal information you receive from a source or third party vendor, to be shared with the right people inside or outside of your organization.
Data Lifecycle Management: what are the three main goals of data lifecycle management (dlm)?
Keep metadata on what changes have been made such as adding comments or changing tags which could make tracking back through older versions of data much easier if necessary; also keep track of software patches applied since these patches may have unintended consequences. – Establish protocols for data sharing and ensure that appropriate approvals are in place to allow any Data outside of your organization’s control, such as personal information you receive from a source or third party vendor, to be shared with the right people inside or outside of your organization.
Develop standards for data storage and protection including what encryption methods are acceptable and when backups should take place.
A successful Data Lifecycle Management plan includes three core tenets: Keep metadata on what changes have been made; establish protocols for data sharing that include appropriate approvals in order to share any Data outside of an organization’s control like personal information received from a source or third party vendor; and develop standards for data storage & protection by developing policies around how often backup copies should be taken and what types of encryption are permissible among other practices.