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This post summarizes today's challenges in healthcare and shows how an enterprise data management and analytics platform can support healthcare providers to achieve their strategic goals. The blog should be read by persons responsible for data management, business intelligence, and analytics in healthcare providing companies as well as by anyone interested in SAP's business technology platform and its adaption to healthcare.

Healthcare systems around the globe are under stress due to exploding costs and demographic developments, and we can see the following trends:

  • Expensive health systems: Rising healthcare costs foster the discussion about new strategies like value-based medicine and accelerate the shift from illness therapy to prevention and wellbeing. With this in mind, we should introduce a new customer segment beside the patient: the healthcare consumer, a healthy person wanting to stay healthy and therefore ready to consume new health products.

  • Democratization of medicine: Myriads of new health apps for digital prevention, wellness and therapy enable the patients to play an active role.

  • Personalized medicine: Data-driven drug development enables individual medication for individual patients for better outcome and less adverse effects.

The challenge for healthcare providers today is to anticipate the impact of these trends and to get fit for the future. Therefore, they are pursuing these goals:

  • Digital transformation to improve their services and to increase the loyalty of the patients, employees and healthcare consumers.

  • Innovation strategies to develop, test, and implement new business models at the same time as optimizing operations and clinical outcome.

  • Growth strategies through new business networks and partnerships or mergers and acquisitions.

A holistic data management and analytics function is paramount for the organizations to achieve these transformation strategies. Many hospitals still suffer from a disrupted IT landscape with non-integrated data and too many analytical point solutions implemented by the business. In addition to that, the industry recognizes that most expensive EHR systems are not living up to their expectations in the analytics function. Understandably, because the EHR solutions focus on data that is stored within the EHR and medical use cases only. They lack best practices in data integration, meta data management, visualization, and advanced analytics capabilities. Therefore, relying on EHR analytics as an enterprise data platform puts the strategic transformations at risk.

SAP's Comprehensive Architecture for Enterprise Data

SAP delivers a strong data management and analytics solution which covers the requirements of a modern healthcare enterprise data platform and supports the healthcare providers to achieve their strategic goals.

SAPs Comprehensive Architecture for Enterprise Data

Following the data flow from the source systems, we start describing the architecture from the bottom to the top:

Sources, Ingest and Refine

Diagnostic and therapeutical processes generate a big volume of data in various forms. Progress in treatment and diagnostic technologies lead to even more data and even more variation. The healthcare trends described in the introduction section, add additional data categories like experience data, sensor data, patient-generated data, or knowledge data. All of them further increase the data volume and variation.

SAP Data Intelligence is the entry point for the data into the data platform, allowing to expose the application data to the data platform. Data can be cleansed, enriched, or combined to ensure the right data quality at the right place. Its data catalog and data governance capabilities support business semantics, show in a transparent way, where the data is coming from, and allow to define who is allowed to see what data. For data scientists, SAP Data Intelligence is the workbench to access data, understand the data with profiling and even operate their machine learning models. SAP Data Intelligence contains Jupyter Notebooks and can be extended with Python.

Data Store and Compute

Stepping up one layer, we have the data store and compute layer to persist data. Our work horse for this capability is SAP HANA, available as cloud or on-premises edition.

SAP HANA is a powerful database management system providing data modelling and development capabilities for columnar or tabular data, data tiering with hot (=in memory), warm and cold storage, and a fine granular administration and security system. SAP HANA provides data lake capabilities to store large amount of data. With virtual data access, SAP HANA follows the data gravity pattern, allowing to access and compute data without moving it. The data stays in the source application, which saves additional storage costs and time to move the data.

The advanced analytics capabilities of SAP HANA include:

  • Native spatial, graph and JSON document processing engines

  • Trending machine learning algorithms for embedded use and processing with in-memory performance

  • Combine and enrich spatial, search and graph processing with machine learning

  • Building comprehensive multi-model in-database applications

  • Integrates into 3rd party / open source applications

  • Native interface API for data scientists in R and Python

SAP BW/4HANA is the latest and most powerful edition of SAP’s proven Business Warehouse product. It combines enterprise data warehouse capabilities with explorative and interactive real-time analytics using the SAP HANA in-memory database. Its unique selling proposition is the predefined content. We deliver more than 8’000 objects out-of-the-box for Finance, Controlling, Supply Chain Management, Sales and Distribution and many more fields. Healthcare specific content is available as well.

The new kid on the block is called SAP Data Warehouse Cloud. As it is a cloud solution, it is an out-of-the-box enterprise-ready data warehouse which focuses on easy collaboration, business layer, and self-service modelling. The key focus is a well-defined business semantic layer which maps to a robust data layer across a multitude of data sources. With spaces, you can bring data models and connections in one secured and governed place. You can collaborate with your colleagues through clearly defined access authorizations and share findings with other stakeholders. These spaces are for instance particularly well suited to meet the requirements of clinical trials with a group of researchers collaborating in their specific study space. The researchers can define relationships between each element in the data model and enrich the data fields with business information and tags for quick discovery. Without IT or developer support, they can use drag and drop, tables, SQL, Cube Builder, and flat files to get insights from data. They can also connect to data from remote sources and visually build the model with Graphical View to simplify data modelling tasks.

Consume and act

The front-end layer is for the business users to gain insight on data and to immediately act on these insights. Our solution for this self-service analytics layer is called SAP Analytics Cloud. It empowers the business users to safely and securely work with governed data and create interactive dashboards and user stories. With the augmented analytics and integrated planning capabilities, you can predict future scenarios for your hospital based on historic data and your planned data; in one tool, with one single workflow. We have seen hospitals using SAC for the daily top-management meetings, for crisis-management, as well as for long-term business planning.


Healthcare systems are transforming: The pandemic, increasing costs, new value-based reimbursement models, new players, more educated and more demanding patients, much more technology and much more data, etc. All these factors impact the systems in non-foreseeable ways at an increasing speed which leaves policy makers behind.

The here mentioned solutions represent a comprehensive and well-integrated set of solutions to establish an enterprise-wide data platform, and for the healthcare providers to achieve their strategic goals and to make them fit for future challenges.

Please finde additional information at, share your feedback or thoughts in a comment and stay tuned for more posts about data in healthcare.