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10+ ways to reshape your SAP landscape with SAP Business Technology Platform – Blog 8

Wolfgang_Epting
Product and Topic Expert
Product and Topic Expert
‎2024 Jun 05 9:23 PM
1,857

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 Blog 8: The role of Data Products in the digital transformation 

Summary: In my last blog in this series with the title “Mastering the Data Dance: The Potential of Clean Core and AI”, I outlined the role data plays in digital transformation for individual economic entities, and why it's crucial to maintain innovation by adhering to a Clean Core strategy. In this blog, I will expand the perspective to consider the transformation towards digital ecosystems and the importance and role of data products.

In our increasingly fragile world, characterized by perpetual crises and multifaceted challenges, the ability to respond quickly and act resiliently is more crucial than ever. Companies can't accomplish this in isolation, but instead, form partnerships to launch new business models, make supply chains more resilient, weather inflation or meet the requirements of Corporate Sustainability Reporting Directive Reporting Guidelines, just to name a few. Partnership denotes more than the simply interlocking processes; it includes the plethora of data exchange. This raises a range of intriguing questions related to digital sovereignty including data protection and privacy of the players involved. Naturally, it also brings into focus a suitable data management architecture and an overarching data strategy, a very crucial aspect to consider.

To understand the role of data better, we need to look beyond single businesses and consider digital ecosystems across different industries. Manufacturing-X data ecosystems revolve around operational and supply chain data in manufacturing and automotive industries, respectively. Whereas, a Sustainability data ecosystem emphasizes environmental, social, and governance metrics, guiding firms towards responsible business practices.

Artificial intelligence will become a success factor only if we achieve acceptance of people. In this respect, the data used to train models plays a central role when we apply ethical criteria. Artificial intelligence represents one of the most significant competitive arenas of our world, which will determine the progress and prosperity of entire economic zones like for example the European Union The importance we attribute to the role of data in the European economic area can be gleaned from the multitude of regulatory orders, such as the Data Act, the Data Governance Act, and the AI Act.

At the core of relevant, responsible, and reliable Business AI, is the high trustworthiness of insights data produced by SAP’s business applications. Only with input data of high quality, can we expect a relevant output. SAP's data governance and standards ensure that the data applied is of high quality and semantically rich.

The concept of “Data as a Product” has emerged to facilitate, among many other benefits, the usage and interchange of data.

Data Product:

Traditional data management catered primarily to the operational prerequisites of single departments, with data stored in siloed systems. Nowadays, data catalogs help to achieve self-service, promoting efficient data democratization and simplifies utilization by making data assets transparent. Managing data as a product shifts the focus further towards delivering tangible value to internal and external stakeholders.

What exactly is a data product and how does it differ from a dashboard, a file, an API, or an analytical model? One of the four principles of Data Mesh is in fact “Data as a Product”. Thus, it would be straightforward to adopt the characteristics listed there. I have authored an extensive blog series on “Data Mesh' and its implementation with the SAP Business Technology Platform”, providing a comprehensive resource for further reading on the subject.

Given that visual representations often surpass theoretical definitions in fostering human comprehension, I will utilize an example for clarity. I do this cognizant of the fact that, at times, examples might not adequately encapsulate the complexity of reality:

Imagine you've invited your friends and you wish to prepare a delightful meal. You head to the supermarket and purchase all the raw materials and ingredients you need, hoping for a successful outcome. Alternatively, you could go to a vendor who already provides all the ingredients and spices in the correct quantities, with instructions and a recipe tailored to the number of your guests. What do you believe is the value of this product, which includes everything required to impress your friends? I would argue that its value is substantial, as it caters not only to satisfying culinary requirements but also to enhancing your image. This simple analogy illustrates how our thinking in real-world economics can be transferred to the realm of analytical data provisioning and data products.

At SAP, we have concurred on the following definition: A data product is a managed artifact which satisfies recurring information needs and creates value through transforming and packaging relevant data elements into consumable form.

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SAP Datasphere empowers business and technical users by leveraging the concept of data products to facilitate integration between SAP and non-SAP data. Each business line within SAP can create a semantically enriched data product, such as for example an invoice, that encapsulates multiple pieces of related data. SAP Datasphere is designed to consume these data products that can be created by different business lines within the company. Examples include the “onboarding experience” data product in SAP SuccessFactors or the ”supplier spend analysis” in SAP Ariba. Traditional methods required IT to integrate all data and build a new data model, with SAP Datasphere and its data products, it becomes easier to combine data from different SAP properties like ERP from SAP S/4HANA and use it seamlessly. This integrated data strategy minimizes the need for complex IT projects, giving autonomy to the business lines to interrogate and use data.

Furthermore, data products are crucial for generative AI use cases as they supply the essential inputs leveraged by generative algorithms. This is accomplished by processing exorbitant volumes of high-quality data, enabling AI systems to classify patterns and relationships, assimilate this knowledge, and apply it to produce innovative and distinctive outputs.

Additionally, data products frequently encompass associated metadata, which imparts supplementary contextual details capable of informing the AI’s generative operations. Access to robustly structured and comprehensive data products can drastically enhance the efficacy and precision of generative AI systems. The utilization of data products from a wide array of sources assists in the development of AI models characterized by the ability to generate more varied and inventive outputs.

Data Catalog and Data Marketplace:

Analogous to the real economy where we refer to a marketplace as a location where demand and supply meet, a similar context also exists for data products. It is desirable to have an Amazon-like user experience to find and consume data products. Search and discovery are features of a data catalog. To adequately prepare the purchase decision, all relevant information must be available to be viewed. This includes metadata, reviews, comments, ratings, references to the business glossary, lineage information about the origin and composition, data currency and much more.

The SAP Datasphere catalog will allow users to discover valuable data products from various sources, including SAP applications, metadata, and marketplace third parties, through a unified experience. This streamlines data discovery and enhances data insights, improving operational efficiency, streamlining workflow, and reducing time spent searching for crucial data. Users can make more informed decisions by utilizing accurate and relevant data for modeling and business use cases, with comprehensive access to SAP data products.

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The SAP Datasphere catalog helps you bring together all the data used by your organization into a governed, one-stop-shop experience for anyone working with data in your organization. Once found, data products can be made available for both internal and external consumption in the SAP Datasphere data marketplace.

An example of an external use case would be the provisioning of data products for Catena-X. SAP Datasphere's data marketplace will allow external consumers to access data products through the Catena-X Eclipse Data Space Connector (EDC). This enhances synchronization of data products and Catena-X access policies with the EDC catalog. It incorporates license management with EDC contracts and aligns data offerings with the Catena-X format. It empowers Catena-X members to consume marketplace products via the EDC and enables users to restrict visibility of EDC catalog assets. Lastly, it verifies access to data against licenses for the SAP Datasphere's data marketplace.

Catena-X is an automotive network that enables secure and standardized data and information exchange among manufacturers, suppliers, and other automotive industry partners and it shows the importance of exchanging data with industry specific semantic.

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SAP Datasphere data marketplace facilitates self-service data exchange within and between enterprises to be a driving force for data democratization.

Knowledge Graph:

Data products contain collected and processed data, presented in a structure that can provide insights, drive decisions, or feed machine learning algorithms. This includes databases, reports, predictive models, data streams, APIs, and more.

Knowledge Graphs, on the other hand, are a powerful tool for representing the relationships between different data points in a highly contextual and visual manner. They add a layer of semantic context to data, linking related concepts in a web of information.

When used together, they can significantly improve the accessibility, utility and understanding of data. Knowledge graphs can be used to organize, index, and make sense of data products. They can show how different data products are related to each other, provide additional metadata or context about each data product, and allow for more sophisticated querying and analysis of data products.

For example, a business could use a knowledge graph to link customer data, purchasing behavior, and product data from various data products, creating a 360-degree view of customer behavior. Hence, knowledge graphs could enrich the depth and breadth of insights derived from data products, enhancing their usability, and driving more informed decision-making.

During the SAP Data Unleashed Event 2024 we unveiled that SAP Datasphere will allow organizations to better represent their real-world use of data through a knowledge graph, providing more complete context from disparate data sources to large language models (LLMs) while inhibiting AI-generated model hallucinations.

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Auto-generated knowledge graphs enable AI to answer complex, open-ended questions

The Blog Series 10 + ways to reshape your SAP Landscape with SAP BTP will be published regularly and the latest blogs can be found here:

 

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