Recently, SAP announced the expansion of SAP S/4HANA Cloud with vector databases managing unstructured data.
But what does this mean in practice? First, we need to understand what unstructured data is. These are pieces of information that lack a predefined format or specific organization, such as texts, images, emails, PDF documents, etc.
Unlike structured data, which is organized in tables and databases with clear fields and defined relationships, unstructured data does not follow a rigid format and can exist more freely. The analysis of unstructured data poses a challenge because traditional relational database techniques are not as effective in this context.
However, with the advancement of artificial intelligence, machine learning, and natural language processing, organizations are developing more sophisticated methods to extract valuable information from such data types, allowing for a more comprehensive understanding of the informational environment they are in.
Vector databases, on the other hand, are systems that store and manipulate data represented in the form of vectors. In contrast to traditional databases dealing with structured data in tables, vector databases are designed to store multidimensional and complex information, often used in areas such as machine learning, natural language processing, and computer vision.
Expansion of SAP S/4HANA Cloud: Practical Gains
The recent announcement regarding the expansion of SAP S/4HANA Cloud is a significant advancement for the SAP community. By now encompassing vector databases managing unstructured data, it will facilitate the rapid identification and retrieval of similar objects.
Vendor or customer searches can be conducted based on contract language, for instance, easing the examination of payment histories or individualized order tracking.
These new features of vector databases will enhance the interaction between the so-called Large Language Models (LLMs) and the critical data of companies.
Large Language Models refer to natural language processing algorithms trained on extensive datasets to understand and generate text more sophisticatedly. These models use deep learning techniques, such as neural networks, to grasp complex patterns and semantic representations of language.
LLMs have a variety of applications, from virtual assistants and chatbots to automatic text generation in translation, summarization, and content creation. They play a significant role in the evolution of artificial intelligence and the ability of machines to handle the complexity of human language more naturally and contextually.
A vector engine is very important in the context of LLMs because they all have a cut-off date. They don’t have the latest data and don’t know anything more specific about the company. The vector database can provide more context about, for example, financial data, supply chain, quality, logistics, HR, procurement, customer experience. With this announcement, we combine the strength of LLMs with real-time company data,”
explains Juergen Mueller, CTO of SAP.
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