Hybrid, agile and resilient: SAP data management

Data, data storage and data management

Digitization of processes needs a stable data architecture and infrastructure for data storage. "A stable data architecture is imperative," explains Glenn Fitzgerald, CDO Product Business at Fujitsu EMEA, at the beginning of the exclusive E-3 conversation. "Especially when both SAP Hana data and non-SAP data must be available on a universal platform for all business needs. This is where the system inspection service would be fundamentally helpful." Crucial for users are two key points: data protection and data security in the context of overall IT security. Using artificial intelligence methods, the solution supports data analysis to detect anomalies. "In the specific case of cooperation with our partners, a corresponding IT platform was defined and installed on the basis of Kubernetes, Ceph Cluster, Fujitsu Primergy servers and NetApp NFS servers," Glenn Fitzgerald describes the symbiosis in the SAP community.

“Intelligent Enterprise, in which a holistic view of S/4 and non-SAP data is achieved.“

Glenn Fitzgerald, CDO, Fujitsu PBL Europe

The success of a company's digital transformation depends above all on how well or poorly it handles data. After all, progress can only be made with a consistent data and analytics strategy. Many companies are therefore currently making structural changes in order to become a so-called data-driven company - in other words, to consistently use their own data resources as a company in order to open up new opportunities and possibilities for their business processes.

"In the course of digital transformation, data must be prepared, enriched with information, consolidated, and related to each other," defines Thomas Herrmann, Manager Business Development SAP at NetApp for EMEA. In September of this year, he organized a conference for existing SAP customers with the participation of SAP itself, Cisco, Fujitsu, Red Hat and Amazon/AWS. On display was the interplay of complementary IT vendors for data management in SAP systems. One focus was naturally on the topic of cloud computing, with special attention to hybrid cloud and ultimately the challenge of S/4 conversion.

“The flood of data that accompanies digitization requires the implementation of an archiving concept.“

Thomas Herrmann, Manager Business Development SAP, NetApp

Thomas Herrmann thus explained logically at the Net App event: "So data management plays a decisive and important role in all digitization initiatives in the company. Data management is the sum of all measures necessary to collect, store and provide data in such a way. Once digitization is complete, all key business processes are based on data, which must be managed as optimally as possible in order to be used to the fullest." His colleague Robert Madl from Cisco adds, "In any case, data management is an important criterion for success. Digital transformation in the context of SAP is, after all, the digitization, optimization and automation of business processes."

Data structures

Data management is currently in demand everywhere, as Cisco manager Robert Madl describes: "If, for example, I automate processes in production or warehouse logistics with the help of sensors - keyword IoT - a lot of new data is generated that has to be handled in a completely different way than traditional ERP data. For example, if machines are controlled on the basis of this sensor data, reliable transmission with correspondingly low latency is critical. If you collect sensor data for Big Data analysis in connection with data from the ERP system, it is also unproblematic to store this data hybrid distributed in a data lake in the cloud and to keep the ERP databases on-prem or in a colocation. However, making the right data management decisions here is critical to the success of digital transformation projects."

“Hybrid data architectures can be implemented. The challenge lies in the interdependencies between the systems.“

Robert Madl, Global Strategic Partner Executive, Cisco

By 2024, 93 percent of companies in Germany will increasingly use their data for revenue growth. 42 percent even see data as a significant source of revenue. This is according to the new study, "The Multi-Cloud Maturity Index," conducted among some 3,000 business and IT decision makers in the EMEA region. "The past decade has shown that almost everything around us is data-driven. More than that, data has become a core business asset and, when used correctly, can contribute significantly to business success," emphasizes Glenn Fitzgerald of Fujitsu in a discussion with E-3 Editor-in-Chief Peter Färbinger. And Fitzgerald adds: "Currently, we live in a world with unstructured data, data silos, exorbitant data growth and increasing data complexity. This makes how that data is managed all the more important to business success. How solid is a company's data management strategy so that it can respond quickly to market demands at all times?"

Intelligent Enterprise

The following question is crucial: What methods and tools are needed to successfully use data of any kind? Technologies such as artificial intelligence and machine learning offer solutions, says Glenn Fitzgerald, and he explains: "On the one hand, the quality of the data can be significantly improved, and on the other hand, errors can already be detected during data acquisition. This can be supported by automated machine learning. The decisive factor for a company's success is the optimal supply of business processes with the best possible qualified data - and at the right time. A primary goal here is to pick up the customer, identify the customer challenge and deliver a solution to it. There are numerous techniques, methods and tools for this. This is how we support the customer and jointly develop their Intelligent Enterprise."

What are the criteria for data storage in the Intelligent Enterprise? The ERP system is on-prem, so should the data also be on-prem? The ERP system is in the cloud, so should the data be there too? Is that right? Thomas Herrmann: "That is not so easy to answer, because several factors play a role, network speed, i.e. sufficient bandwidth to the cloud, location, distance to the next backbone. Real time access or batch processing, so what are my SLAs in terms of response times, etc. If it's real-time processing of data, the data should of course be where it's processed, with Hana that would be in-memory computing, whether that's in the cloud or on-prem is then secondary."

And Robert Madl from Cisco specifies: "Of course, hybrid data architectures can be implemented. The challenge lies in understanding the interdependencies between systems. SAP landscapes have often grown organically over decades, and individual code has been implemented everywhere - the creators of which may no longer even be in the company. As a result, there are often dependencies between systems. For example, one system may access the database of another system directly - or make a call to the other system, which in turn triggers an access to the data layer. Here, it's important to understand which systems depend on each other and how - i.e., what bandwidths are needed and how time-critical this communication is, i.e., what are the maximum latencies allowed to provide the necessary data in a timely and complete manner."

Processes and algorithms

At the end of the day, it's about ensuring that the business process mapped on the SAP systems - no matter where they are running - works with high performance. "You just have to be aware when you're distributing SAP systems across multiple sites that you're going to have higher latencies and lower bandwidth between sites, and you have to take that into account when you're planning before you migrate. This is where AppDynamics can be immensely useful, as it automatically analyzes and visualizes these dependencies between systems and makes them available for planning," explains Robert Madl in the discussion.

What does the Cisco manager say about optimal data storage? "It depends on the type of data and how it is used. With databases like SAP Hana, it makes sense to have the data close to the computing resources," explains Robert Madl. "While Hana is an in-memory database - meaning that data is held in the server's memory - that only helps with read transactions. Write transactions are only confirmed when the data has been written to the so-called persistence layer, vulgo the data storage system. So here it's critical to have fast IO between server and storage for application performance."

In-memory databases

OLTP applications have experienced the biggest performance boost with the introduction of flash memory. In analytics scenarios (typically OLAP), in-memory technology would have a smaller performance impact at runtime because the data is already stored - but it would take a very long time to boot these systems if the data could not be loaded from a local data store into RAM. Decentralized data storage can really make sense for Big Data analyses, says Robert Madl, explaining, "For example, if you have several data lakes based on Hadoop close to the data source or sensors, you can - for example, with the MapReduce algorithm - iteratively pre-aggregate data for analysis in a decentralized manner and then transfer only the necessary information to a central system for further processing."

What are the advantages and disadvantages of hybrid data management? Robert Madl again: "The three factors of time, cost and complexity must be optimized here. The place where the data originates does not necessarily have to be the place where the data is used. Transferring data over long distances causes costs and takes time. However, having many different places where data is stored increases complexity. It often proves to be a sensible strategy in digital transformation projects to define minimum requirements for the time factor, a maximum framework for costs, and then optimize in the direction of complexity first. For example, in a smart factory project, the time factor often dictates how much edge computing is necessary, the cost of transmission dictates how far sensor data must be pre-aggregated, and complexity is ultimately the decisive factor for feasibility and overall success."

S/4 and data conversion

Data management and data storage become a success and cost factor in S/4 conversion. How do you guarantee high success and low data costs for the existing SAP customer? "Together with our customer, we create true Intelligent Enterprise, in which a holistic view of SAP S/4 Hana and non-SAP data comes about," says Fujitsu manager Glenn Fitzgerald, describing the challenge. How the data is managed and stored in detail depends on the company's own business processes. "Here, Fujitsu supports its customers with the so-called co-creation approach. At its core, this is a workshop based on the specifications of Fujitsu Human Centric Experience Design. In close cooperation with customers, technology partners and our experts, we develop an optimal approach, accompanied by proof of concept and a long-term plan for overcoming the specific challenges as well as continuous optimization of IT," Glenn Fitzgerald knows from many successful projects.

Data flood and digitization

The volume of data among SAP's existing customers will continue to grow, and with it, the cost of data management is likely to increase. "The flood of data that comes with digitization requires the implementation of an archiving concept," explains NetApp manager Thomas Herrmann at the end of the E-3 conversation. "First of all, it must be determined which data must be archived in compliance with the law, which data wants to be archived, and which data should be retained for a certain period of time. Modern data archiving uses the cloud. All major cloud providers offer an archiving tier for object stores.

These tiers are increasingly becoming the preferred destination for backup data with long-term retention requirements. This includes all the major archiving offerings from AWS, Azure, and GCP. Cloud archive solutions are the most cost-effective object storage tiers available today and can scale to petabytes of storage as the size of archived data increases. NetApp Cloud Backup, for example, provides a comprehensive service for long-term protection of your data in heterogeneous environments, whether in the cloud, on-prem, or a hybrid combination of these platforms. NetApp Cloud Backup supports the archive storage tiers from the above cloud providers as targets for your long-term backup and archival data."

Data and workloads

Cisco manager Robert Madl has another tip for existing SAP customers: "SAP workloads do not usually live in a vacuum. An optimal infrastructure for SAP Hana should therefore not only be optimal for Hana itself, but also optimal for all other workloads, so that you don't have to build an additional management silo in IT for this one workload. There are around 200 reference architectures for Cisco Flexpod on how to reliably run workloads on it - not only SAP workloads like Hana, but also, for example, web services, which are often part of the mapped business process supported by the SAP system. With Flexpod XCS, there is now the multicloud-optimized version of Flexpod, which extends exactly these reference architectures with scenarios where you can outsource and connect services to the cloud without additional management effort."