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Data & AI

Data Streaming - the smart way to artificial intelligence

Artificial intelligence (AI) is the hot topic of the moment. No wonder many companies want to incorporate AI into their digitalization strategy. However, very few are aware of the requirements and possibilities of this technology. The result: despite high investments, the AI gained is not as intelligent as one would like it to be. The problem is usually not with the AI itself, but with the data available to it.

Data streaming - the key to AI

The key to data-driven decisions and the use of AI lies in the availability of up-to-date and consistent data. This is possible thanks to data streaming, a technology that collects, processes and analyzes continuously generated data in real time. In contrast to traditional batch processing models, in which data is collected and processed at fixed intervals, data streaming enables data to be processed continuously and almost immediately, providing AI, for example, with up-to-date and relevant information.

But how are such data flows created from convoluted data distributed across various systems? The implementation of data streaming requires not only a careful selection of the right technologies and tools, but also a clear strategy. Only with the right data sources can AI provide useful answers. Therefore, connectivity to the relevant systems or data sources must first be ensured. Events occurring in the sources can then be fed into data streaming platforms such as Apache Kafka in real time. However, platforms such as Kafka do not just serve as data collection basins. Rather, they form a kind of central nervous system by processing and analyzing the events with low latency.

Data streaming + AI = infinite possibilities

Some may wonder whether this effort is worthwhile at all or whether an enterprise license from ChatGPT would not be enough. In this context, it is important to note that not all AI is the same. ChatGPT will never be able to provide as specific information as an AI that can access company data in real time. In a business context in particular, AI in combination with data streaming opens up completely new possibilities, especially when Retrieval Augmented Generation (RAG) comes into play.

RAG is a technology that combines Generative Artificial Intelligence (GenAI) with a retrieval model to retrieve relevant information from various company databases. This is done in two steps: First, the retrieval model searches a large amount of documents, databases or knowledge bases using a vector database to find relevant information to a specific query. This data is retrieved and passed on to the GenAI, which then uses the information to generate a detailed and precise answer. This allows an AI with RAG to respond to specific questions in a more precise and contextualized way. The internal chatbot thus becomes an indispensable know-it-all.

This approach is also - or even especially - worthwhile for SMEs. For them, training their own AI models is often not affordable. RAG offers a very efficient alternative for accessing company data without having to train your own AIs. For this to succeed, data consistency is required. For relevant business data in particular, it is important that it is in a correct and consistent state across the various applications throughout its entire lifecycle - from collection and processing to storage and analysis. After all, no one likes an AI that constantly changes its mind.

Looking to the future with AI

Thanks to RAG, however, an AI can do much more than just display the right information at the right time. With data streaming and RAG, AI becomes a modern oracle. For example, companies in the industrial sector can use data streaming and AI to carry out predictive maintenance. By monitoring machine data in real time, anomalies can be detected and maintenance work can be planned before actual breakdowns occur. But other industries also benefit from this technology. In the financial sector, real-time data analysis and AI models can be used to detect fraud. By continuously monitoring transactions, unusual patterns can be recognized immediately and fraudulent activities can be prevented.

There are numerous other examples. One thing is certain: If companies want to use the full potential of AI, the integration of company data and therefore the introduction of data streaming is mandatory. Thanks to real-time processing, scalability, improved data quality and reduced latency times, data streaming creates the necessary infrastructure to successfully implement advanced AI applications. And in a world that is increasingly characterized by data and digital technologies, the integration of data streaming and AI represents a significant competitive advantage.

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