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Next-Generation Analytics with SAP Datasphere and SAP Analytics Cloud

Ysaline de Wouters
25 Apr 2023

Over the last few years, there seems to be a trend toward the increasing democratization of data modeling and analytics. In this spirit, SAP has introduced SAP Datasphere (formerly named SAP Data Warehouse Cloud) as its new cloud data warehouse solution. Brought together, SAP Datasphere and SAP Analytics Cloud (SAC) provide a complete data warehouse in the cloud including integration, modeling, and consumption. It thereby sets the pace for next generation analytics.

From IT to Business: A distribution of responsibilities

Last years have been highlighted by a fast popularization of data analysis, which lead to the question of making it easier for non-technical users to access and process data. Until recently, analytics were driven by IT, and users had limited autonomy. The focus was on large-scale reporting and the tools offered were aimed at data engineers. Gradually, the trend has shifted to self-service, allowing increased autonomy for business users through more user-friendly interfaces. Little by little, responsibilities are moving away from the IT department, leading to a true autonomy of the end users.

This trend is also visible in the so-called data mesh. A decentralized data architecture that structures data by specific business domain. Spaces in SAP Datasphere perfectly support this decentralized architecture. For business users, spaces can be considered as dedicated working environments to model and analyse data according to logical areas or business lines. For IT, those spaces consist of compute and storage resources that can be allocated to a specific project or team. Data mesh architectures facilitate self-service applications from multiple data sources, extending access to data beyond the more technical resources, namely data scientists, data engineers and developers. This creates teams that work independently and take full end-to-end ownership of their domain data. They are responsible for both the operational source data and the analytical endpoints. By making data more accessible through this domain-centric design, the data mesh reduces data silos and operational bottlenecks.

The end goal for these trends is similar, getting more people involved in the analysis process and thus unlocking the full value of the data. As a result, decisions can be made faster and more informed. There is no doubt that SAP Datasphere is designed for both business users and developers. It has a semantic layer that facilitates data analysis, and it allows everyone to speak the same language in terms of metadata, dimensions, and metrics, thus avoiding confusion over technical terms.

SAP Datasphere – A 3-layer architecture

SAP Datasphere seems to be breaking new ground in the world of data warehousing due to its ease of configuration and operation. It is an out-of-the-box solution that comes with standard, pre-installed business content, offering a fast time to market. Powered by SAP HANA, this tool provides end-to-end processes that span three different layers: data integration, data modeling and data consumption. Hence, SAP Datasphere comes as a link between the sourcing and the consumption of data.

Data Integration layer

SAP Datasphere facilitates the integration of data from different landscapes. The data can be integrated virtually or via replication. Both Smart Data Integration (SDI) and Smart Data Access (SDA) are supported. Integration possibilities are extensive, giving this tool a large degree of flexibility. It allows data from multi-cloud, hybrid and on-premise environments to be combined. And depending on changing requirements, the spaces can easily be expanded, reduced, or even put on hold. This offers some elasticity when it comes to managing computing resources and storage.

Besides, SAP Datasphere offers rich metadata management tools to ensure data quality and comes with data lineage capabilities. Using the lineage features, users get a clear overview of objects and spaces and can quickly retrieve data dependencies and navigate to the relevant objects, while understanding where data is coming from and where it will land.

Brought together with SAP Data Intelligence you can cover way more scenarios. Users can harness the power of heterogeneous data across multiple systems, integrating structured and unstructured data.

Data modeling layer

SAP Datasphere comes with two modeling layers, the data layer and the business layer.

The data layer, referring to the data builder, targets data engineers that model with a more technical approach. Models are created and maintained here. Python scripts can be used to apply custom transformations that are not supported out of the box. What’s more, the data builder will allow users to acquire and combine data, create tables/views on various data sources and build data flows for reporting purposes. One of the modeling options offered by SAP Datasphere is modeling using Graphical views. This option is very similar to the Calculations views feature available in previous toolsets. The main idea is to combine different tables or views into a single output in a graphical manner. These graphical views seem more intuitive compared to SQL views, which require some more technical knowledge. Once done, these views can then be consumed directly by SAP Analytics Cloud.

The business layer, i.e., business builder is meant for business users that want to semantically enrich those models. Business users can, amongst others, create business entities such as dimensions and analytics datasets in the business builder. What’s more, SAP Datasphere includes a new data catalog that acts as a centralized repository.

Next, the Datasphere incorporates Artificial Intelligence and predictive intelligence into the data warehouse. Since it has SAP HANA Cloud embedded into it, once data has been curated in SAP Datasphere, it can be enriched by various ML algorithms. For instance, as it is tightly integrated with SAP Data Intelligence cloud, users can benefit from certain extra features. Amongst others, the predictive capabilities that come with SAP HANA APL and PAL libraries. Users can write python to train a predictive model and integrate such models in the data flows. Both R and Python operators are available in the Data Intelligence data flows.

In most cases, the data integration will be handled by the IT team. They will manage data replication and federation and create a baseline model for each business unit in the Data Builder. Then, business users get access to add their own virtual models to the existing foundation and enrich the semantic layer.

Data consumption layer

SAP Datasphere is tightly integrated with SAP’s analytical platform: SAP Analytics Cloud.  Consuming SAP Datasphere content is done through the optimized story experience. Unified stories enable interactive exploration of data and help users to find insights and visualize information with charts and tables. Findings and presentations can be commented, shared, and presented to people from within and outside of the organization.

With the integration of SAC and it’s planning capabilities, businesses can easily adopt the hassle-free planning and data simulation across KPIs, and publish the planned data into the system with less involvement of IT.

Finally, advanced analytics is also possible via machine learning and R integration. Augmented analytics help organizations to gain insights from their data faster than ever before. Instead of spending hours or even days screening data and creating complex reports, augmented analytics can quickly identify patterns and relationships in the data, enabling users to derive key insights in real time. The smart features from SAC help users to automate some data wrangling steps, identify best influences, or even perform analyses using natural language capabilities.

Customers are not limited to SAC as a reporting solution as SAP Datasphere can easily connect with 3rd party tools such as Microsoft Power BI, Tableau or Microsoft Excel.

Target audience of SAP Datasphere

SAP Datasphere is aimed at both small and larger companies. Small companies find benefit in lowering the initial costs that can be contracted by implementing a data warehouse. Larger companies are given the benefit of modernizing their data warehouse without having to completely re-build their flows. Besides, it allows anyone to connect with applications and data sources, without the need for technical expertise.

SAP Datasphere is based on the decentralization of responsibilities and thus contributes to a real convergence between IT and business, reassigning responsibilities. In that sense, it provides business users with a highly abstract infrastructure, removing the complexity of managing the lifecycle of the product and enabling domain autonomy. SAP Datasphere offers seamless integration and radically simplifies the data warehousing landscape, providing graphical low-code modeling tools, a rich semantic layer, and self-service visualization capabilities. Together, they form the next-generation analytics platform.

Capgemini is already helping customers around the globe in setting up their next-generation analytics solutions with SAP Datasphere and SAP Analytics Cloud. For instance at one of Belgium’s leading Water distribution companies we set up a next generation analytics platform bringing together master data, transactional data, and streaming data to enable real time tracking of water flow and water consumption throughout the network. It unlocks intelligence, simplifies innovation, and lowers the total cost of ownership compared to peers.

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Author

Ysaline de Wouters

SAP Analytics Consultant
Ysaline has been a valuable member of the Capgemini team for nearly 5 years. During this time, she has developed a keen interest in data engineering using SAP technologies, and now assists multiple clients in their transition to BW/4HANA and the cloud. Ysaline is dedicated to keeping up with evolving technologies and frequently researches the latest modeling and reporting tools and methodologies to ensure her clients receive the best solutions possible.