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Author's profile photo Wolfgang Albert Epting

Data Mesh with SAP Business Technology Platform Part 5 – SAP Analytics Cloud

Summary: In my first blog with the title: More than just a hype: Data Mesh as a new approach to increase agility in value creation from data I explained the four principles of Data Mesh at a high level and mapped some helpful capabilities of the SAP Unified Data & Analytics Portfolio. In the past and  current episodes, I will look at individual SAP products and explain in more detail which of their technical capabilities can support the introduction of a Data Mesh.

Having started the series with SAP Data Warehouse Cloud, SAP HANA Cloud, SAP Master Data Governance and SAP Data Intelligence Cloud in the previous blogs, this time I will talk about SAP Analytics Cloud.

To fully understand this blog, it is at least necessary to have read my first blog or another respective publication to be familiar with the four principles of Data Mesh and to understand why centralized  monolithic data architectures suffer from some inherent problems. Recognize the mention of Data Mesh principles by the fact that they are written in bold

Decisions without doubt

As the analytics and planning solution within SAP Business Technology Platform, SAP Analytics Cloud supports trusted insights and integrated planning processes enterprise-wide to help you make decisions without doubt.

Data Mesh builds on a foundation for visualizations and reports that provide insights into business:

In a Data Mesh, the people that only consume data products are typically users who are outside of the data product domain. These consumers use a central data catalog to find data products that are relevant to their needs. They are looking for data that help them achieve various use cases such as persistent analytics dashboards and reports, individual performance reports, and other business performance metrics. Humans understand data, trends, and anomalies much easier when they perceive them visually. Providing data product consumers with valid, accurate datasets, reports and dashboards is a critical part of every Data Mesh implementation. At the same time, the business requirements must be met. These are vital prerequisites in order to be able to implement certain properties required by Data Mesh for data products – understandable, accessible and usable.

Analogous to the real economy, acceptance must be won and the consumption of data products must be promoted, as this is ideally measured. If data products do not meet consumer needs, they must be withdrawn from the market, again analogous to the real economy. In this respect, every data product owner has a great interest in an appropriate form of visualization and presentation.

Technical jargon and data terminology must be translated into information that can be understood by business stakeholders. Annual reports must be fed from the content of data products. Reports describing strategic business objectives must be generated and monitored. New data products must be created by aggregating and combining existing data products. Another Data Mesh characteristic says that every new data product must provide added value (“Valuable on its own”).

How is this supposed to succeed if even the basic data products don’t meet this requirement because they don’t demonstrate sufficient excellence in human-machine communication.

 

In order to be able to successfully implement all these requirements, a complete solution like SAP Analytics Cloud that uniquely offers business intelligence (BI), augmented and predictive analytics as well as business planning in one modern solution is required. The multiple demands of all kinds of roles and decisions must be met, and business people must be allowed to analyze past and present while planning for the future. They make better, more informed decisions and act faster and more confidently.

The ability to use a common data consumption tool across the organization, for both central teams and domain experts, is an important enabler for Data Mesh. And efficiently manage and monitor the activities of the self-serve data platform.

 

One service for all users that enables intelligent ways to analyze and plan for 360° insights:

Complete solution for BI, augmented/predictive analytics, and planning

Data Mesh drives Data Democracy – everybody learns about data analytics and visualization:

“Data Meshstrategy requires the diffusion of minimum skill sets required for everyone involved in digital solution – development, applications, services, products – to appreciate, understand, and use data. Removing the walls of organizational silos between the data specialists and everyone else is a catalyst for an organic cross-pollination of skills, language, and understanding.[1]

 

The term data democratization stands for the greater autonomy of departments when working with data, while at the same time more and more extensive support of business processes with data. It’s about easy and comprehensive access for employees to the data relevant to them or their role. For this purpose, tools for accessing, processing and analyzing data are provided in order to improve or automate decisions.

Part of data democratization is to understand how business factors impact enterprise performance. It’s about knowing about the causal relationships behind core KPIs. Then even the effects of strategic business decisions can be simulated using machine learning (AI/ML) technology. AI/ML helps to answer questions about the future with predictive models by learning from historical data. The probability of the occurrence of a particular situation can be calculated with reasonable accuracy. Patented classification, regression and time-series forecasting algorithms provide the basis for powerful and robust models that can be used to optimize operations and make strategic growth decisions.

SAP Analytics Cloud offers data exploration capabilities for both technical users and citizen analysts. And accordingly is suited to be positioned as common data consumption tool across the company. SAP Analytics Cloud is perfect to provide large scale training and enablement programs  and  to facilitate the exchange of know-how and talent across data domains.

SAP Analytics Cloud conversational artificial intelligence makes querying data as easy as asking a colleague. Natural language queries generate visualizations in real-time to get the information you need in the shortest amount of time. Users always want to understand and know more about their data. It’s easy to see more information about a specific data point in your visualization or table, as well as an anomaly in your collected data.

Making data more accessible to all even not technical business users who aren’t familiar with the ins and outs of data science is one of the greatest benefits of Augmented Analytics.It leverages Natural Language Processing and Explainable AI to enable users with no data science experience to analyze and query data. This democratizes data literacy, extending analysis from expert data scientists to other professionals, like “citizen data scientists.”

Data Driven insights drive data democratization:

The right interface for every data product consumer – including the board

Becoming data-driven quickly by Data Mesh is wishful thinking. As we all know, the hardest part is establishing a data culture. Data Mesh can accelerate the journey provided organizations have reached a certain level of maturity in understanding the data value creation process.

In addition to an organizationally clear assignment of responsibilities, tools with the appropriate level of convenience and the right functionality are required. Only then can Data Mesh also help to integrate data product consumers sufficiently and bring them back from “shadow IT” to the realm of light. Suddenly, siloed departments become allied distributed domain teams, working with and for each other rather than against each other to extract the inherent value from data that really drives the business forward.

With this blog, I conclude the first season of the blog series about the Unified Data and Analytics technology components of our SAP Business Technology Platform that can help our customers to implement Data Mesh.

I will continue to write about other exciting aspects of Data Mesh. Stay tuned.

[1] Deghani, Z.(2022): Delivering Data Value at Scale, O’Reilly 2022, p. 327 f.

The author would like to thank Pinar Dolen for the collaboration on this topic and her contributions to this article.

If you have any further questions about SAP Business Technology Platform or specific elements of our platform, leave a question in SAP Community Q&A or visit our SAP Community topic page.

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