SAP Information
Lifecycle Management (ILM):
This is how GDPR-compliant data management works!

Definition

SAP Information Lifecycle Management (ILM) describes a comprehensive approach to automated data management. Rules are used to determine how data is handled throughout its lifecycle - from creation and storage to decommissioning and final deletion. The aim is to optimize data flows, reduce IT system costs and minimize legal compliance risks associated with the collection of certain data.

The background for Information Lifecycle Management approaches are growing amounts of data, more complex system landscapes and continuously increasing requirements from legal regulations such as the GDPR or the revised Swiss Data Protection Act (DSG). These require improved data governance methods and data protection measures from companies - especially for personal data. And this is exactly where ILM comes in.

Lifecycle Management & Storage

SAP ILM supplements the standard SAP data archiving and enables companies to automatically block, archive, or delete personal data. Corresponding ILM rules are defined for this. These determine, for example, the minimum and maximum retention periods for productive and archived data, thus ensuring that they are removed from the system and destroyed at the right time. If necessary, legal blocks can also be placed on data to prevent premature deletion. In practice, the deletion process is usually preceded by a final check. This means that the data to be deleted is first collected in an archive, then checked by the responsible information owner, and only then released for final deletion.

In addition, data that has already been archived with SAP ILM can also be stored on an ILM-certified WebDAV server. This guarantees that the data cannot be changed and protects against premature deletion, while at the same time relieving the active IT system and saving costs. Overall, powerful information lifecycle management in companies ensures consistent data management and disposal in accordance with data protection and retention regulations. You can find out more about this in our ILM whitepaper.

Data archiving, retention management and retention warehouse

1. Data archiving

The core element of SAP Information Lifecycle Management is data archiving, which focuses on data volume management. That means: data that is no longer required in day-to-day business is outsourced from the operative IT system. SAP ILM expands the archiving functions of the SAP standard to enable interaction with the modules Retention Management and Retention Warehouse. In addition to archiving, SAP ILM also allows data to be destroyed and snapshots to be created.

2. Retention Management

The second element, retention management, controls the lifecycle of data up to its End-of-Life. Data creation, storage, and destruction are managed using different methods and tools. Defined sets of rules and deletion concepts provide the guidelines for the different data types. Data related to legal cases can also be temporarily frozen using the lock feature.

3. Retention Warehouse

The third element, Retention Warehouse, is aimed at decommissioning entire system landscapes or legacy systems. It provides a standardized procedure for this:

• Archiving of the entire database from the legacy system
• Rule-based storage of data in the ILM Retention Warehouse
• Ensuring that the data can be retrieved for tax or auditing purposes until the retention period has expired

There are good reasons to consider introducing SAP ILM as an active data volume management (DVM) solution while transforming your SAP landscape to SAP S/4HANA. The following sections outline why this approach offers benefits especially during the preparation phase and post go-live.

Prepare for SAP S/4HANA: Data Volume Management with SAP ILM

Introducing SAP ILM Data Volume Management or expanding on an already implemented solution can positively impact your move to SAP S/4HANA in many ways and bring you closer to achieving your transformation goals.

• Reduce total data volume before the transformation
  Archiving data that is not required anymore reduces the total amount of data to be considered as relevant for the transformation activities. This is not only advantageous for the transformation project itself but also when subsequently operating the SAP S/4HANA system. Less data helps with managing the project complexity and improves performance and efficiency of the running system while keeping data storage cost at bay. If limiting the window of allowed downtime for your transformation is an important factor, lower data volume could make your job easier to meet the expectation.
  
  
• Archive data and thereby detect data inconsistencies at an early stage
  Standard checks for data archiving can contribute to data quality validation. While sorting out data that is no longer required, these checks identify inconsistencies that could lead to further issues during a transformation project. By identifying any inconsistencies that early, the team can already decide whether to proceed with data cleansing activities during the DVM preparation project. Natuvion recommends adding data quality management (DQM) to the mix. With this best practice, you leverage gained data insight, integrate data cleansing into the pre-project, and benefit from a much leaner transformation road map.
  
  
• Initialize compliance processes through archiving historic, inactive data
  The meaning of SAP ILM goes beyond data archiving. The solution also serves as the central tool for data protection and compliance for your SAP landscape. If you have not yet thought about the data protection aspect, now is the time to catch up by integrating it into an SAP ILM pre-project and your overall SAP S/4HANA transformation plan. That approach only slightly expands the SAP ILM scope that typically focuses on data volume and data quality. However, you will be able to take advantage of data that meets data protection requirements from the beginning, ultimately reducing your efforts during and post transformation. If the legacy system is kept in the landscape, it must be compliant. This is another reason to consider a corresponding SAP ILM preparation project.
  
  
• Implement an active data life cycle and adapt processes
  After considering DVM and DQM as a pre-project, the next step would be to create an active data life cycle concept for your company. Assuming that each data set must be archived after reaching the end of its business purpose, it seems obvious that the introduction of a company-wide data life cycle has to happen rather early. However, experience shows that it is an often neglected topic. With cost going up for today‘s in-memory data storage technology, data life cycle management has become more relevant. Thus companies started planning for data volume management measures for their SAP S/4HANA systems using SAP ILM.
  
  Special hint: The move to SAP S/4HANA already requires very effective change management from a user’s point of view. If the implementation of an active data life cycle is planned as a parallel workstream, it will add to the amount of changes and further increase the change management effort. If the transformation started with the SAP ILM introduction, that headache would go away. By adopting processes and managing changes early, the complexity of the SAP S/4HANA transformation is being significantly reduced.
  
  

SAP ILM Benefits for SAP S/4HANA Operations

• Active data volume management
  Control database growth with active data life cycles when data storage with the SAP HANA database is costly. This is an essential measure for any transformation approach, but particularly for brownfield scenarios where systems are purely technically migrated to SAP S/4HANA. In case of Selective Data Transition or greenfield scenarios where initially lower data volume is being transferred, processes are needed that support keeping that status.
  
  
• Operating cost reduction
  Reducing the amount of data in a live database is mandatory considering the increased cost per storage unit.
  
  
• System performance stabilization
  Keep runtime of reports and background jobs down.
  
  
• Maintainability and recovery scenarios
  Reduce complexity by managing the data situation. Uncontrolled data growth negatively impacts activities such as taking system copies or backing up systems.
  
  
• Compliance and revisions
  Meet regulatory compliance requirements for data minimization and data processing restrictions of personal data.
  
  
• Business integration of historical data
  Retrieve and display data that had been previously archived through specific transactions.

Data protection-compliant handling of personal data

In SAP Human Capital Management (HCM) systems, in particular, a large number of sensitive, personal data are processed. For these, the GDPR prescribes specific deletion periods that must be observed. As described above, SAP ILM can be used to define an individual set of rules that can also be linked to the HR data or processes in SAP HCM. These blocking and deletion processes ensure that personnel data is not processed in your SAP HCM systems for longer than permitted. This gives those responsible the security and control that the GDPR specifications are bindingly complied with for all personal data in system landscapes of any complexity. The revised Swiss Data Protection Act (DSG) also responds to the current requirements for data security.

In addition to the security and control that SAP ILM enables in SAP HCM, it also ensures performance and cost gain in the system. Data that is no longer required is regularly deleted and does not take up unnecessary storage space while existing data can be processed more efficiently. Would you like to learn more about the data protection-compliant handling of personnel data in SAP HCM? Then take a look at our whitepaper.

The introduction of SAP Information Lifecycle Management can be complex and extensive depending on the individual requirements of the project and the nature of the systems. Because the blocking and deletion of personal data affect numerous processes and objects. Standardized implementation approaches, best practices, and predefined templates like those developed by Natuvion can be real game-changers here. And at the same time ensure increased data quality and lower implementation costs.