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mark_ma
Product and Topic Expert
Product and Topic Expert
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

























Type of Data Remarks Example
Structured data

  • entries with fixed schema

  • same fields or properties

  • stored in relational databases




  • accounting table (ACDOCA) in SAP S/4HANA Finance product


Semi-structured data

  • entries with the same structure

  • schema is not fixed and could have nested information

  • stored in




  • JSON, XML file


Unstructured data

  • does not have a specific structure




  • images, audio, and video



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.























Offerings Remarks
SAP Sybase ASE

  • Relational database

  • Typically used as the underlying database for JAVA-based applications (eg. NetWeaver)

  • SAP RISE managed VM, subscribe through SAP


SAP HANA

  • Relational, in-memory, with collum-storage enabled database

  • Typically used as the underlying database for */4HANA applications (eg. S/4HANA, BW/4HANA)

  • support real-time analysis on data

  • SAP RISE managed VM, subscribe through SAP


SAP Sybase IQ

SAP HANA Cloud Database

  • Relational Database

  • SAP managed services, subscribe through SAP



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.















Offerings Remarks
BW/4HANA

  • typically used as the main data warehouse solution for large enterprises.

  • a typical scenario would be, there are scheduled data replications from S/4HANA (ERP) systems to BW/4HANA

  • SAP RISE managed VM, subscribe through SAP


SAP Datasphere
(previously, Data Warehouse Cloud)


  • cloud data services (including warehouse)

  • supports the business data fabric which is a data management architecture focusing on delivering an integrated, semantically-rich data layer over underlying data landscapes to provide seamless and scalable access to data

  • typically complements with BW/4HANA or BW through SAP BW Bridge

  • offer data exchange for data providers & consumers with Data Marketplace

  • Enriched connection options to multi-cloud environment

  • SAP managed services, subscribe through SAP



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.











Offerings Remarks
SAP HANA Cloud Data Lake

  • both structured data and unstructured data

  • support file storage

  • cloud data lake solution

  • SAP managed services, subscribe through SAP



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.































Offerings Remarks
CDS View in S/4HANA

  • data modeling within the transactional system

  • SAP RISE managed VM, subscribe through SAP


SAP HANA Smart Data Integration

  • data modeling within HANA database

  • SAP RISE managed VM, subscribe through SAP


SAP Data Services

  • ETL within data integrator tool

  • SAP RISE managed VM, subscribe through SAP


SAP BW/4HANA

  • data acquisition, and data modeling within data warehouse solution

  • SAP RISE managed VM, subscribe through SAP


SAP Datasphere

  • acquiring, preparing, and modeling data within cloud data warehouse solution

  • SAP managed services, subscribe through BTP


SAP Analytics Cloud

  • simple data modeling is possible, but it's more common to do ETL / data modeling in CDS view or  Datasphere, and use SAC as a data visualization tool

  • SAP managed services, subscribe through SAP



Data Insight and Foresight


Business Planning

















Offerings Remarks
SAP Analytics Cloud (SAC)

  • SAC planning can both be used as a stand-alone solution, or can also be used as embedded applications within SAP ERP solutions, such as S/4, Digital Supply Chain, etc.

  • SAP managed services, subscribe through SAP


SAP Business Planning and Consolidation (SAP BPC)


Business Intelligence


Typical tasks include data visualization, reporting, and dashboarding.











Offerings Remarks
SAP Analytics Cloud

  • SAP managed services, subscribe through SAP



Machine Learning & Artificial Intelligence

































Offerings Remarks
HANA PAL/APL

  • built-in machine learning (PAL) and automated machine learning (APL) library within the HANA database

  • typically are consumed by applications

  • PAL and APL in SAP HANA, SAP RISE managed VM, subscribe through SAP

  • PAL and APL in SAP HANA Cloud, SAP managed services, subscribe through SAP


SAP Analytics Cloud

  • predictive analytics within BI tool

  • typically scenarios include: predictive planning, simple predictive usage for data analysts

  • SAP managed services, subscribe through SAP


BTP AI Business Service

  • tailored for SAP business processes, pre-built reusable and generic machine-learning APIs on BTP

  • SAP managed services, subscribe through BTP


BTP AI Core

  • PaaS service on BTP for bringing your own heave-load machine-learning model, with seamless integration with BTP services

  • SAP managed services, subscribe through BTP





  • all-in-one entry for all AI content within the SAP landscape

  • SAP managed services, subscribe through BTP








 





  • machine learning model management application (a Fiori app) built within S/4HANA

  • SAP RISE managed VM, subscribe through SAP



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