Analytics

Compliance in the area of SAP Analytics is essential to ensure data security and integrity. The targeted assignment of analysis authorizations ensures that only authorized users can access critical data. In addition, individual documents with users and time stamps ensure complete transparency and traceability of data analyses. The change log in the backend enables complete documentation of all system changes, which is crucial in order to meet regulatory requirements. Through these measures, companies minimize risks and ensure that their analysis practices meet the highest compliance standards.

The efficient system setup for SAP Analytics is crucial for the success of your analytics projects. The initial setup creates the basis for a functional system landscape, which usually consists of three systems: development, test and production. Regular maintenance ensures the performance and stability of the system. Changes and developments are securely transferred between the systems as part of the transport system. A well thought-out test concept, including a dedicated test system, enables comprehensive quality assurance and minimizes risks before productive use so that analysis projects can be implemented smoothly.

A well thought-out data model is the key to the success of SAP Analytics projects. Various model types, such as the account model and the key figure model, enable a structured and targeted analysis of company data. Dimensions offer comprehensive flexibility in terms of setting options and allow individual adaptation to the specific requirements of your company.

Master data plays a central role in the data model and consists of various elements such as texts, attributes and hierarchies. A clear definition of whether maintenance is carried out by business users or exclusively by IT ensures that the data is always up-to-date and consistent.

In addition, the type of data storage is crucial for the performance and efficiency of analytics solutions. The data flow, including the harmonization of data, ensures a consistent and reliable database for well-founded decisions. A harmonized data landscape makes it possible to integrate and analyse data from different sources, which significantly increases the quality of the analyses. The data model thus forms a solid basis for all analysis activities and supports companies in achieving their strategic goals.

The effective connection of interfaces is crucial for the success of SAP Analytics projects. Importing files enables the uncomplicated integration of external data sources. With Smart Data Access (SDA), analyses can be performed in real time without physically moving the data. The SAP BW Bridge provides a powerful connection to existing SAP BW data models, while extractors for ERP ensure seamless data transfer. The export for third-party systems (3rd party tools) in turn enables the use of data in various applications and supports comprehensive analysis. These interfaces ensure flexible and efficient data processing and analysis.

Automation plays a central role in SAP Analytics projects and helps to make processes more efficient and error-free. By defining process chains, recurring workflows can be automated, saving time and increasing productivity. Scheduled background jobs ensure that important data analyses and reports are carried out regularly and reliably without manual intervention.
In addition, targeted notifications, such as email updates or integrated messages (for example from the BW system or via Datasphere), enable information to be proactively forwarded to the relevant users. This communication ensures that users are always informed about the status of processes, analyses or possible errors. The combination of these automation measures significantly increases the efficiency of SAP Analytics projects while minimizing the susceptibility to errors, allowing companies to make optimal use of their analytical capacities.

Archiving is an essential aspect of SAP Analytics projects to optimize data management and ensure system performance. With nearline storage, companies can efficiently store large amounts of data that are not actively needed at the moment, but still need to be accessible for future analyses or compliance requirements. This enables cost-effective long-term archiving.

The distinction between hot data and cold data plays a central role here. Hot data includes frequently required information that needs to be available quickly and easily, while cold data can be stored for longer periods and is accessed less frequently. Through targeted archiving, companies can optimize their data resources and improve the performance of their analysis systems. This ensures that relevant data is available at all times, while less-used information is stored in nearline storage to save space and improve efficiency. These strategies help to maximize the efficiency and cost structure of SAP Analytics projects.

The reporting tool is a central component of SAP Analytics projects and enables flexible and user-friendly data analysis. The Excel plug-in offers the possibility to perform multidimensional analyses and use familiar Excel functions to gain deeper insights into the data. This integration ensures that users can seamlessly access their data and process it in their familiar working environments.

In addition, a web application for multi-device access offers comprehensive options for dashboards and self-service reporting. Users can access intuitive dashboards from different devices, providing a visual representation of the data and supporting real-time decision making. The combination of Excel plug-in and web-based solution ensures that users have the tools they need to perform complex analysis and make quick, informed decisions. This increases user satisfaction and significantly promotes data analysis in companies.

The reporting type in SAP Analytics projects is crucial for the performance and timeliness of data analyses. High performance is achieved by implementing persistence, as relevant data is stored for quick access. This enables queries to be processed efficiently and ensures that extensive data analyses can be carried out in real time without placing an excessive burden on system resources.

In addition, the concept of live reporting through virtualization offers an innovative solution for data analysis depth. Data is retrieved in real time without the need to keep a physical copy in the system. This method not only ensures that the information is up-to-date, but also enables flexible analyses that meet rapidly changing business requirements. By combining persistence and virtualization, Reporting Type optimizes access to data and ensures that companies can make timely and informed decisions based on the latest available information.