Technologies and markets, which are currently dealing with data storage and management, are finding themselves in a dramatic change due to the continuously growing amount of data and information gathered. The policy-based approach which manages the path of data in a certain Information system throughout its lifecycle, from creation, storage, and deletion after the data is outdated, is called “Data Lifecycle Management.” Internet sales, digital contracts, CRM data, operational systems, marketing all of which are nowadays the base on which every modern enterprise functions. Not to mention wireless management, remote devices, and multimedia usage, all of these producing huge amounts of data. Heavy amount of data to store, monitor, audit, and destroy, not only to do business with but also to comply with data legislation.
Data Security
Security of the stored data has become the main topic not only towards IT management. Due to Data-Protection policies and other law enforcement, security issues nowadays involve board management. So besides treating IT departments as business departments, it is also important to secure IT infrastructure. The data lifecycle management’s role is to ensure the cost-effective protection of data. This means that important data are given a high level of protection, and otherwise unimportant data are given an adequate and cost-effective level of protection.
Availability of Data
Gathered data has to be stored and accessed at different frequencies depending on their importance. Availability of data is regulated with DLM (Data Lifecycle Management) by storing important data in primary storage devices (CPU Cache, RAM) with short access time. And data, which is less important, is stored in secondary and tertiary storage devices like Hard Drive or even in offline devices (USB, CD, Tape Drive) always in accordance to the data importance and frequency of usage. To regulate this DLM uses technology based on HSM (Hierarchical System Management).
Data Integrity
Data integrity has become a serious issue due to regulations regarding data usage. The integrity of data means that data, which is stored in enterprise databases, must be accessible, reliable, and authentic over its lifecycle. According to FDA and their guide on “Data integrity and compliance with CGMP” data is expected to be:
- Attributable – Collected data has to be attributable to the person who generated that data.
- Legible – Data gathered has to readable, and easy to understand for humans. Ensured records have to be accessible during the data lifecycle.
- Contemporaneous – Every time, data is executed, records have to be made. Results and measurements have to be recorded at the time data are executed.
- Original – Original form or source data have always to be preserved.
- Accurate – Data has to be free from errors, truthful, and reflective of the perceptive.
Data lifecycle management has to give solutions in order to keep data hierarchically tiered in-store technologies, that differ in price and performance. While all these data have to be accessible to users and admins every time.
HSM (Hierarchical Storage Management)
Hierarchical Storage Management is a Data Lifecycle Management concept. HSM operates like a backup software, a low frequency used files and data are stored in a cheaper backup storage. Data is sorted and administered according to specific parameters such as last access, the frequency of use, file size etc. The storage media used to store the data also relies on these parameters. So often data is stored in hard disks, and later when the frequency of usage is higher, data is swapped out to tape drives, or tape libraries. Inactive files and data are stored and entered in the local file system and can be recalled anytime. The more active data is stored in more expensive mass storage systems and can, therefore, be recalled anytime but faster.
HSM system as part of the Data Lifecycle Management process offers a longer access time to stored data, that’s why they are more practical for DLM.
One way to keep costs under control while still providing for the unexpected need for specific data is to visualize and act upon the five phases of the data lifecycle:
Phase 1
Fresh data is a “ Source of Power” for companies. Employees, create new data and files, which flow into the company’s daily operations. Companies store this data into local and network servers. In addition, these data is also backed up in the cloud or locally. So in case of data loss, they can be restored quickly.
Phase 2
Data loss can be prevented through data protection. When data is aged they can be removed from primary servers, and transferred into more cost-effective locations like external backup tapes, or cloud servers. In case of a major disruption, or a catastrophic event the stored data can be completely restored.
A balanced data backup and recovery strategy combine external storage tape libraries with cloud backup and data recovery. In such a hybrid system, the most suitable storage medium is selected for each file.
Phase 3
Another way to keep the data storage costs within limits is archiving. For some data, there is a retention period of up to ten years. So even inactive data must be stored and be accessible during this period. For long-term data storage requirements, external backup archives provide a high level of security with fast access and low storage costs.
This kind of storage is especially well suited for unstructured data such as emails. Companies also prefer these archives because the large amounts of data needed for big data analysis can be stored there cost-effectively.
Phase 3
Another way to keep the data storage costs within limits is archiving. For some data, there is a retention period of up to ten years. So even inactive data must be stored and be accessible during this period. For long-term data storage requirements, external backup archives provide a high level of security with fast access and low storage costs.
This kind of storage is especially well suited for unstructured data such as emails. Companies also prefer these archives because the large amounts of data needed for big data analysis can be stored there cost-effectively.
Phase 4
Old and inactive data has to be destroyed, so controlled data destruction has to be ensured. Backup tapes are cheap but most companies can not afford unlimited storage. The last phase of Data Lifecycle Management is a reliable destruction of no longer needed data. This is usually regulated and recorded according to a fixed timetable, which is based on the regulatory and legal requirements as well as the requirements of the company.
Phase 5
Finally, a secure deletion must take place from all IT systems. The data storage lifecycle ends only when the last traces of data are erased – even from decommissioned computers and peripheral devices. As with the destruction of data media, the gapless chain of evidence must be maintained when old computers and office equipment are removed.
If your organization only retains out-of-date storage hardware and storage to access legacy data when needed, it may be a good idea to hire a contractor to handle the data migration and ensure data recoverability. With special technology, such a service provider provides access to archived data and saves your company the expense associated with maintaining legacy systems and legacy software.Get access to insights, research documents, and updates.
