updated date: 12.Jul.2023

Business data has become a strategically crucial asset for enterprises after they have digitalized their business management with enterprise software. According to HBR insights, companies can derive value from data. For RISE with SAP customers, they are running their business transactions in SAP system for their corporate finance, supply chain, production, warehousing, sales and distribution, human experience management, customer relationships, business planning, etc. With a massive amount of data being generated, how to manage their business data, and further how to cultivate insights and derive foresight out of it have become vital.

Using SAP data management solution on top of business data generated in SAP landscape has its unique value, especially in cases with regard to currency conversion, hierarchy, derivation, time dependency master data, and so on. For instance, corporate-finance-related planning, analytics, visualization, and machine learning.

In this article, we will follow the flow of how business data is generated in SAP landscape, then how it is been stored. Based on that, for analytical purposes, how could ETL jobs been done, and what is the approach to do BI, ML, and AI. We will give a review of SAP offerings for corresponding demands.

Fig.1 system landscape, deployment

Business Data and Storage

Business Data Classification

Business Data Storage

Database

A database is typically used to store operational/transactional data (eg. business data generated from ERP or CRM). In other words, a database is usually used as an OLTP system (though some very high-performance databases like HANA can also be used as an OLAP system), which entails the optimization for effectively handling small, distinct transactions, such as real-time insertion, modification, and removal of records. OLTP systems are commonly associated with databases, as they are designed to handle data that is subject to frequent changes. Consequently, the emphasis is placed on speed, concurrency, and consistency.

Data warehouse

A data warehouse is normally associated with OLAP. Typically, instead of real-time, data flows into a data warehouse usually on a regular cadence, from operational systems (like ERP and CRM), databases, and external sources such as partner systems, Internet of Things (IoT) devices, and social media. In modern data warehouses (like SAP Datasohere), real-time access to source data without replication is also provided. The data stored in a data warehouse is typically used for batch reporting, BI, and visualizations.

Data Lake

A data lake stores vast amounts of raw data in its native/original format, unlike a data warehouse, whose stored data has already been formatted to some extent.

Transactional Data Processing – OLTP vs. Analytical Data Processing – OLAP

Transactional data is generated to trace specific events. OLTP is referring to the work been done by the system that processes transactional data. An example would be, SAP RISE customers run transactions in S/4HANA Finance system to post open invoices for billing purposes, and these transactions will be processed into table entries and stored in the underlying HANA table.

Analytical data is usually been centrally gathered into one system, and is used for analytics purposes. Typical OLAP tasks could be business planning, producing reports, and generating visualizations or dashboards.

To transfer OLTP data and files into an OLAP system, an ETL (Extract, Transform, and Load) process is essential. One of the essential tasks of ETL tasks is data modeling. Below, SAP offers ETL capabilities in lots of products, either where the transactional data been produced (eg. S/4HANA), or in databases (eg. HANA), or in where the analytics data is been stored (eg. BW/4HANA, Datasphere). Below, we provide a review of commonly used SAP offerings with ETL capabilities.

Data Insight and Foresight

Business Planning

Business Intelligence

Typical tasks include data visualization, reporting, and dashboarding.

Machine Learning & Artificial Intelligence

Some use case examples

We will start a side blog series just to reflect on how SAP runs SAP (SAP’s own IT uses SAP data management solutions). Each blog in this series will be based on real business scenarios, and we will elaborate how the problem been tackled with SAP data management solutions. A list of blog review will be updated in this section.

Expanded Capability with Multi-Cloud

Nowadays, RISE with SAP customers run their business in multi-cloud environments. In addition to their SAP RISE Private Cloud subscriptions, BTP subscriptions, and so on, they normally also have their own hyperscaler subscription from Azure, AWS, and GCP. Also, SAP has reached some collaboration and partnerships with hyperscalers.

As a follow-up blog series, for each hyperscaler, we will expand and dive deep into the landscape of customers’ own hyperscaler data management services. We will talk about how to use hyperscaler data management services to complement SAP data management landscape and empower RISE customers’ intelligent enterprise.

Link for AWS: Extend the Power of Data for SAP RISE Customers: data federation with SAP in multi-cloud AWS

Link for GCP: Extend the Power of Data for SAP RISE Customers: data federation with SAP in multi-cloud GCP

Link for Azure: Extend the Power of Data for SAP RISE Customers: data federation with SAP in multi-cloud Azure

Acknowledgment to contributors/reviewers/advisors:

Ke Ma (a.k.a. Mark), co-author, Senior Consultant, SAP IES AI CoE / RISE Cloud Advisory RA group

Michael Truong Ngoc, co-author, Machine Learning Engineer, SAP IES AI CoE

Nikola Cornelia Braukmüller, Senior Product Manager, SAP HANA Database & Analytics

Zili Zhou, Strategic Project Manager, SAP HANA Database & Analytics

Dr. Markus Kohler, Development Manager, SAP AI CTO Office

Darwin Wijaya Tonny, IT manager, SAP IES AI CoE

Luc DUCOIN, Cloud Architect & Advisor, RISE Cloud Advisory

Richard Traut, Cloud Architect & Advisor, RISE Cloud Advisory

Murad Mursalov, Cloud Architect & Advisor, RISE Cloud Advisory

Kevin Flanagan, Head of Cloud Architecture & Advisory, RISE Cloud Advisory, EMEA North

Daniel Temming, Co-head of Cloud Architecture & Advisory, RISE Cloud Advisory, MEE

Sven Bedorf, Co-head of Cloud Architecture & Advisory, RISE Cloud Advisory, MEE

Extended Reading:
Data Architecture with SAP – Data Warehouse
Data Architecture with SAP – Data Lake
Data Architecture with SAP – Data Fabric
SAP Data Warehouse Cloud + Data Mesh
SAP and Google Cloud Expand Partnership to Build the Future of Open Data and AI for Enterprises