What the value of company data means to the success of the company cannot be overstated. Managing that data effectively is critical. Regular cleanup and maintenance ensure that data stays consistent and up-to-date, databases operate at their best, and systems will not cause disruption or even fail. Also widely referred to as housekeeping, such activities are a necessity.

Definition

SAP Housekeeping refers to the management and cleansing of no longer required data in SAP systems. The goal is to improve system performance, reduce the risk of system disruption, and minimize system storage space. Specific data analysis leads to the elimination of data inconsistencies and duplicates, mitigating compliance risks. Data that is no longer needed is either deleted or archived in a separate system or in the cloud.

SAP systems typically generate large amounts of data on a daily basis, such as application and change logs, administration information, and communication tracking. SAP Housekeeping addresses that kind of temporary data on a regular basis.

Data Housekeeping is the process of validating, cleansing, and reorganizing data in various systems. Most transformation projects plan for those activities in a preparation phase to facilitate the actual data transfer to the target systems. Data is checked for accuracy, consistency, and quality, outdated data is deleted, duplicates corrected, and data structures are restructured. Data Housekeeping is an important part of Information Lifecycle Management (ILM) and is used to manage and optimize the quality of your data.

Both SAP Housekeeping and Data Housekeeping are not only relevant as preparation for a transformation project but should ideally be used systematically and continuously. This way, data quality and efficiency get the attention independently of transformation projects.

An efficient housekeeping project follows four key phases to ensure optimal database and system management.

1.  Planning phase

In the planning phase, all roles and responsibilities are assigned while systems in scope are being identified. Furthermore, the implementation plan is created with quality checks and specific measures included.

2. Implementation phase

During the implementation phase, quality assurance checks are coordinated with the business. Data analysis tools such as DCS Analyze are installed, and parameters are set. Benchmarking is introduced to measure performance.

3. Execution phase

The execution phase includes activities for system validation and cleanup measures. Change Management and knowledge transfer are important areas which require a dedicated plan to ensure a smooth process.

4. Optimization phase

The optimization phase focuses on documenting process issues, weak points, and the resolution of those with the goal of increasing efficiency. The phase concludes with the handover to the operations team which monitors and proactively manages the growing data volume.