What is NLP (natural language processing)?
NLP is a field of artificial intelligence that enables computers to understand, analyze, and generate human language—providing the foundation for voice-based search, automated text generation, and AI assistants. NLP combines linguistics, computer science, and machine learning and provides the technological basis for language-based interaction with data and systems.
| Feature | Details |
| Category | AI technology / machine learning |
| Application | Business intelligence, controlling, customer service, text analysis, search systems |
| Typical use cases | Natural language data queries, automated report commentary, chatbots, text analysis |
| Related terms | Large language models (LLMs), artificial intelligence, machine learning, generative AI, decision intelligence |
| Benefits | Lower barriers to data analysis, greater efficiency in reporting, analysis of unstructured data |
At a glance
combines linguistics, computer science, and machine learning
modern NLP systems are based on large language models (LLMs)
enables natural language data queries, automated report commentary, and analysis of unstructured text in controlling
provides the foundation for AI assistants, chatbots, and natural language querying in BI systems
NLP definition
The abbreviation NLP stands for natural language processing and refers to a field of artificial intelligence (AI) concerned with the machine-based analysis, understanding, and generation of human language. NLP provides the technological foundation for computer systems to not only process written or spoken language but also interpret its meaning and context.
How does NLP work?
NLP combines methods from linguistics, computer science, and machine learning. Modern NLP systems are predominantly based on large language models (LLMs), which are trained on extensive volumes of text. They identify patterns in language, derive meaning, and generate contextually appropriate responses.
Key processing steps include:
Tokenization: breaking text down into individual words or sentence components
Syntax analysis: identifying grammatical structures
Semantic analysis: determining meaning and context
Named entity recognition (NER): identifying names, places, KPIs, and concepts
Sentiment analysis: classifying statements as positive, negative, or neutral
What does NLP mean for controlling and business intelligence?
In controlling and BI systems, natural language processing opens up new possibilities for human-machine interaction and automated data analysis:
Natural language querying (NLQ): Users ask questions in everyday language—for example, “How did revenue develop last quarter?”—and receive directly prepared answers from the data system without requiring SQL skills or in-depth system knowledge.
Automated report commentary: NLP systems analyze KPIs and automatically turn them into written explanations. What previously had to be written manually can thus be generated consistently and at scale—providing a significant efficiency gain in reporting.
Text analysis as a data source: Unstructured data from customer feedback, contracts, emails, and press releases can be analyzed using NLP. Controlling and management therefore gain access to information that is not captured in traditional databases.
Chatbots and AI assistants: NLP provides the foundation for conversational assistance systems that answer controlling questions, explain variances, or provide analyses on demand.
What are the opportunities and limitations of natural language processing?
NLP makes data more accessible and significantly lowers the barriers to using analytical systems. At the same time, the principle remains: language models interpret—they do not perform calculations. Incorrect or ambiguous inputs can lead to incorrect conclusions. For use in controlling, it is therefore essential that NLP components are closely linked to verified data sources and defined KPI models.
Quality assurance, transparency regarding the sources used, and a clear understanding of system limitations are prerequisites for responsible use in a business context.
Conclusion
NLP gives data and systems the ability to communicate through language—in both directions. It makes information easier to query and enables insights to be formulated automatically. For controlling and business intelligence, this means shorter paths to analysis, broader usability of reporting systems, and new ways of systematically incorporating unstructured information.
Bissantz and natural language processing
For Bissantz, language in controlling is not an end in itself but a means of communicating figures and relationships more effectively.
In DeltaMaster, Bissantz’s BI and analytics platform, KPIs and variances can be used to automatically generate written explanations, interpretations, and recommendations for action—precisely, consistently, and transparently. What controllers previously had to formulate manually is thus generated automatically in the appropriate context.
Bissantz reliably implements the NLP use cases described above—from natural language data queries and automated report commentary to AI-supported analysis assistants—as part of its decision intelligence approach. This approach combines analytical precision with linguistic accessibility while ensuring that NLP functions are always based on verified data and defined KPI models.
Practical example: A sales controller prepares the monthly sales report and finds that revenue in one region is significantly below forecast. Instead of merely displaying the variance as a number, DeltaMaster can turn the relevant KPIs and analysis results into an understandable text—for example, highlighting the affected region, the size of the variance, and the key influencing factors. The controller therefore receives not only the information that a variance exists but also a concise written interpretation that can be used directly in a management meeting.
FAQ – frequently asked questions
Natural language processing means that a computer can understand human language—much like a person reads and interprets a text. When you ask an AI assistant, “Why did our revenue decline in March?” and receive a well-founded answer, NLP is behind it: the system has understood your question, retrieved the relevant data, and formulated an understandable response.
NLP is the broader term for all methods of machine-based language processing. A large language model (LLM) such as GPT-4 is a specific technology within NLP—a model trained on enormous volumes of text that can process and generate language with a particularly high level of capability. LLMs currently represent the most powerful form of NLP technology.
Yes—in combination with speech-to-text technologies, NLP can also process spoken language. Speech is first converted into text, which NLP systems then analyze and interpret. Voice assistants such as Siri and Alexa use precisely this combination.
No—NLP is a field of artificial intelligence. AI is the broader term for methods that enable machines to exhibit intelligent behavior. NLP is a specialized discipline within AI that focuses on processing and understanding human language.
Bissantz consistently links NLP functions in DeltaMaster to verified business data and defined KPI models. Natural language queries and automated commentary are based on the company’s actual, validated figures—transparently, traceably, and in accordance with the principles of explainable AI and human-in-the-loop.
Summary
Natural language processing gives data and systems the ability to communicate through language—in both directions: it makes information easier to query and enables insights to be formulated automatically. For controlling and business intelligence, this means shorter paths to analysis, broader usability of reporting systems, and new ways of systematically incorporating unstructured information. Bissantz uses NLP responsibly in DeltaMaster, closely linking it to verified data, defined KPI models, and the principle of human oversight.
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