AI Glossary

An AI agent is a software system that carries out tasks independently: it takes a goal, plans the steps, uses tools (search, databases, APIs) and acts to reach the goal – without every single step being pre-defined.

How it works: An agent combines a language model (LLM) with memory and tools. It breaks a task into sub-steps, decides at each step which tool to call, evaluates the result and plans the next step until the goal is met.

Business example: A mid-sized company uses an agent in procurement: it reads quotation emails, compares prices against the ERP, builds a comparison table and prepares a purchase proposal for approval.

Applications:

• Automating recurring office and administrative processes
• Researching and preparing information
• Customer service with access to internal systems
• Data maintenance and reconciliation across applications

Distinction: Unlike a bot, which follows fixed rules, an agent makes its own decisions and uses tools flexibly.

Related terms: Agentic AI, Bot

Agentic AI refers to AI systems made up of several cooperating agents that carry out complex undertakings largely on their own. Instead of a single request-and-response, they pursue an overarching goal across many steps.

How it works: Several specialised agents take on sub-roles (e.g. planner, researcher, reviewer) and coordinate with one another. They plan, execute, check their intermediate results and correct themselves until the overall result is ready.

Business example: For preparing quotes, three agents work together: one gathers requirements, one calculates based on the ERP, one drafts the quote – the employee only reviews the final result.

Applications:

• End-to-end automation of multi-step business processes
• Creating and reviewing documents and reports
• Data analysis followed by a recommended action
• Orchestrating several specialist systems

Distinction: While a single agent handles one task, agentic AI coordinates multiple agents for larger, multi-step undertakings.

Related terms: Agent, Bot

An API (Application Programming Interface) is a clearly defined interface through which different software applications communicate and exchange data. The API specifies which requests can be made, the format in which data is transferred and how errors are handled.

How it works: An API works on a request-and-response principle. An application sends a structured request to the API, which processes it and returns a response, often in JSON or XML format.

Business example: An online shop integrates its shipping provider via an API: as soon as an order comes in, the API automatically transmits the delivery data, with no manual step required.

Applications:

• Connecting business intelligence tools to different data sources
• Integrating payment providers, shipping services or CRM systems
• Using external AI models (e.g. OpenAI, Google) inside your own applications
• Automating data pipelines between internal systems

How it differs: Unlike a direct database connection, an API abstracts the technical infrastructure; the caller does not need to know how the data is stored internally.

Related terms: Microservices, Data Engineer, ETL

Artificial intelligence refers to computer systems that perform tasks which previously required human intelligence, such as language understanding, visual perception, decision-making or solving complex problems.

How it works: Modern AI is based almost entirely on data-driven machine learning: instead of explicitly programmed rules, models learn patterns from large datasets. The most capable current systems are large language models and multimodal models.

Business example: AI systems automate tasks across every industry today, from automated document review in insurance and AI-assisted diagnostics in medicine to automated quality control in manufacturing.

Subfields of AI:

• Machine learning and deep learning
• Natural language processing (NLP)
• Computer vision / image recognition
• Robotics and autonomous systems
• Generative AI

How it differs: "AI" is often an umbrella term. Narrow AI can only handle one specific task (e.g. playing chess). Broad or general AI (AGI), which transfers human intelligence to arbitrary tasks, does not yet exist.

Related terms: Machine Learning, Large Language Model, Generative AI

A blockchain is a decentralised, tamper-resistant data structure that stores transactions or information in a chronological chain of blocks. No central actor controls the data; instead, the chain is stored on many computers at once and validated jointly by all participants.

How it works: Each block contains a cryptographic hash of the previous block, a timestamp and the actual transaction data. If a block is altered after the fact, its hash changes, which invalidates all subsequent blocks. This makes manipulation practically impossible.

Business example: A food producer uses blockchain to document its entire supply chain from farm to supermarket shelf. Every step is recorded immutably; in the event of a recall, a product's origin can be traced in seconds.

Applications:

• Cryptocurrencies (Bitcoin, Ethereum)
• Supply-chain transparency
• Digital contracts (smart contracts)
• Certification of documents and credentials
• Healthcare (secure patient records)

How it differs: A blockchain is not a database in the classic sense; it is optimised for immutability and trust, not for fast queries or flexible data structures.

Related terms: Data Warehouse, Structured Data

A bot is a program that automatically performs defined, recurring tasks – such as answering standard questions or filling in forms. Classic bots follow fixed rules or scripts.

How it works: A bot reacts to a trigger (e.g. a message or a schedule) and works through a predefined sequence. Modern chatbots combine this with a language model to understand more freely worded requests.

Business example: A chatbot on the website answers common customer questions about opening hours, shipping and prices, and only escalates complex cases to a human.

Applications:

• Customer service and FAQ automation
• Appointment booking and form filling
• Monitoring and notifications
• Routine tasks in chat and messaging channels

Distinction: A bot follows fixed rules; an agent plans independently and uses tools flexibly to reach a goal.

Related terms: Agent, Agentic AI

Business intelligence refers to the systematic use of technologies, processes and methods to collect, prepare and visualise company data, with the goal of enabling sound, fact-based decisions.

How it works: BI systems draw on data from various sources (ERP, CRM, databases), consolidate it in a central data warehouse and present it clearly through dashboards and reports. Users can filter, drill into and analyse data interactively, without programming knowledge.

Business example: The head of sales opens the BI dashboard in the morning and sees immediately: which region missed its revenue targets yesterday? Which product is outperforming the plan? On that basis they make informed decisions before the first meeting.

Applications:

• Revenue and cost analysis
• Customer and market segmentation
• Operational KPI monitoring
• Financial and budget planning

How it differs: BI explains what happened in the past (descriptive analytics). Predictive analytics goes a step further and forecasts what will happen next.

Related terms: Data Warehouse, Descriptive Analytics, Data Scientist

Classification is a supervised learning method in which a model learns to assign new data points to one of several predefined categories.

How it works: The model is trained on labelled data (e.g. emails marked "spam" or "not spam"). It learns which features are typical of which class. For new, unknown data points it then makes an assignment based on these learned patterns.

Business example: A bank trains a classification model on historical loan applications. The model learns which combination of income, debt and payment history led to defaults, and classifies new applicants as "low risk" or "high risk".

Common algorithms:

• Logistic regression
• Decision trees and random forests
• Support vector machines (SVM)
• Gradient boosting (XGBoost, LightGBM)
• Neural networks

How it differs: In classification the possible outputs are discrete categories (e.g. "yes/no", "A/B/C"). In regression a continuous value is predicted (e.g. a price or a temperature).

Related terms: Supervised Learning, Clustering, Feature Engineering

Clustering is an unsupervised learning method in which an algorithm groups data points by similarity, without the groups being defined in advance.

How it works: The algorithm measures the similarity between data points (e.g. by distance) and groups similar points together. Different methods weight this differently: K-Means splits data into a predefined number of clusters, hierarchical clustering builds a tree structure step by step, and DBSCAN also detects irregularly shaped clusters and filters out outliers.

Business example: An e-commerce company clusters its customers by purchasing behaviour and interests. The result: clusters such as "bargain hunters", "brand fans" and "occasional buyers", each targeted with different marketing.

Applications:

• Customer segmentation in marketing
• Anomaly and fraud detection
• Document classification
• Gene expression analysis in bioinformatics

How it differs: In clustering there are no predefined categories; the algorithm discovers the structure itself. In classification, by contrast, the categories are known and the model learns to assign new data to them.

Related terms: Unsupervised Learning, Classification, Feature Engineering

Data engineers are the architects of the data infrastructure. They build, operate and optimise the data pipelines that capture, transform and provide raw data from various sources for analysis. Their work is the foundation data scientists build on.

How it works: Data engineers develop and maintain ETL processes (extract, transform, load), implement databases and data warehouses, manage cloud infrastructure and make sure data is complete, correct and available on time.

Business example: A retail group has sales data in five different regional systems. The data engineer builds a pipeline that consolidates, cleans and loads this data into a central data warehouse each night, so the BI team finds current, consistent data each morning.

Core tasks:

• Building and maintaining data pipelines
• Data cleaning and transformation
• Implementing and managing databases
• Cloud platform management (AWS, Azure, GCP)
• Ensuring data quality and availability
• Developing APIs for data distribution

How it differs: Data engineers build the infrastructure; data scientists use it. A data scientist analyses data and develops models; a data engineer makes sure the right data arrives in the right quality.

Related terms: Data Scientist, Data Warehouse, Data Pipeline

Extraction is the first step in the ETL process. Data is read out from one or more source systems and made available for further processing.

How it works: Extraction must place as little load as possible on the source systems (e.g. through incremental extraction, only new or changed records are loaded) while still ensuring completeness.

Typical sources: databases, ERP systems, web APIs, log files, external data feeds.

Related terms: Data Transformation, Data Loading, Data Pipeline

Data ingestion describes the first step of a data pipeline: importing data from various sources into a target system for immediate processing or storage.

How it works: A distinction is made between batch ingestion (data is collected at regular intervals, e.g. daily) and streaming ingestion (data is transferred continuously in real time). During ingestion, data is checked for completeness and basic consistency.

Typical sources:

• Relational databases (SQL)
• REST APIs and web services
• File-based sources (CSV, JSON, Parquet)
• Streaming systems (Kafka, Kinesis)
• IoT sensors and device data

How it differs: Data ingestion is the entry point of the data pipeline, before data wrangling. It transfers data but changes it as little as possible.

Related terms: Data Pipeline, Data Wrangling, ETL

A data lake is a central storage location where large volumes of raw data are kept in their original format, structured, semi-structured or unstructured. Unlike a data warehouse, a data lake does not enforce a predefined data structure.

How it works: Data is first stored unchanged ("schema on read"). The structure is defined only when the data is read and analysed, depending on the question. Modern data lakes are usually built on cloud storage systems such as AWS S3, Azure Data Lake Storage or Google Cloud Storage.

Business example: A pharmaceutical company stores clinical trial data, lab results, patient records and sensor data from production facilities in a data lake. Depending on the analysis need, efficacy, safety or production efficiency, different subsets of this data are retrieved and analysed.

Advantages:

• Maximum flexibility in data storage
• Cost-effective for very large volumes of data
• Suitable for exploratory analysis and machine learning

Disadvantages:

• Without careful management it quickly becomes an unmanageable "data swamp"
• Slower queries than optimised data warehouses

How it differs: A data warehouse stores structured, already-prepared data for defined analyses. A data lake stores everything, raw and unprocessed, for as-yet-unknown future questions.

Related terms: Data Warehouse, Data Engineer, Unstructured Data

Loading is the final step in the ETL process: the transformed data is written to the target system, typically a data warehouse, a database or a data lake.

Variants:

• Full load: a complete rewrite of all data (simple, but resource-intensive)
• Incremental load: only new or changed records are loaded (more efficient)
• Upsert: new records are inserted, existing ones updated

Related terms: Data Extraction, Data Transformation, Data Warehouse

A data pipeline is an automated process that captures, transforms and moves data from one or more sources into a target system. It is the technical infrastructure on which data-driven applications are based.

How it works: A pipeline consists of a sequence of steps: data is extracted (e.g. from an API or database), transformed (cleaned, enriched, aggregated) and loaded (into a data warehouse, a data lake or directly into an application). Modern pipelines are event-driven or scheduled and run fully automatically.

Business example: A fintech company has a real-time data pipeline that checks transaction data for signs of fraud in under a second and automatically triggers an alert on suspicion.

Applications:

• Real-time analytics (streaming pipelines)
• Nightly batch processing for BI systems
• ML feature pipelines for model training and inference

How it differs: A data pipeline is the route the data travels. ETL is the pattern many pipelines are built on. A data warehouse is the target system a pipeline often delivers to.

Related terms: Data Engineer, ETL, Data Lake

Data science is an interdisciplinary field that combines methods from statistics, computer science and domain knowledge to gain insights from data and enable data-driven decisions. It links theory and practice: from data collection through model development to communicating results.

Core areas:

• Statistical analysis and probability
• Machine learning and modelling
• Data wrangling and data preparation
• Data visualisation and storytelling
• Deployment and monitoring of models (MLOps)

Business example: A hospital uses data science to reduce readmission rates: models analyse patient data to identify at-risk patients early and provide targeted follow-up care.

How it differs: Data science is broader than business intelligence (which mainly analyses historical data) and broader than machine learning alone (which covers only one part of the methods used). Data science spans the entire process from question to decision.

Related terms: Data Scientist, Machine Learning, Business Intelligence

Data scientists combine statistical expertise, programming skills and domain knowledge to turn large volumes of data into actionable insights. They develop models that recognise patterns in data and enable predictions.

How it works: A typical data science project follows an iterative process: problem definition → data collection → data wrangling → exploratory analysis → model development → validation → deployment → monitoring.

Business example: An insurance company commissions a data scientist to develop a fraud-detection model. The model analyses historical claims, identifies suspicious patterns and automatically flags new cases for manual review, which noticeably eases the workload on claims handlers.

Core tasks:

• Exploratory data analysis (EDA)
• Developing and validating machine learning models
• Statistical evaluation and hypothesis testing
• Communicating results to non-technical stakeholders
• Working with data engineers to bring models into production

How it differs: A data scientist develops models and gains insights. A data engineer builds the infrastructure for it. A BI analyst creates reports and dashboards on existing structures, without model development.

Related terms: Data Engineer, Machine Learning, Feature Engineering

A data staging area is a temporary buffer in the ETL process. Data from various source systems is collected here before it is transformed and loaded into the target system (e.g. a data warehouse).

How it works: The staging area isolates the load process from the production system. Raw data is first stored unchanged, checked, and only processed further after successful validation. If errors occur, the process can be repeated without affecting the target system.

Advantages:

• Increased data security and integrity
• Fault tolerance: problems are caught before they reach the data warehouse
• Enables fast re-runs of failed loads

Disadvantages:

• Additional storage and maintenance effort
• Increased complexity of the data pipeline

Related terms: Data Warehouse, Data Extraction, Data Engineer

Transformation is the second step in the ETL process. Raw data is brought into a uniform, analysable format.

Typical transformation steps:

• Removing duplicates and inconsistent values
• Standardising formats and data types
• Enriching with data from other sources
• Aggregation (e.g. building daily totals from individual transactions)
• Calculating derived metrics

Related terms: Data Extraction, Data Loading, Data Wrangling

A data warehouse is a central, structured database that consolidates data from various source systems, standardises it and makes it available, optimised for analysis and reporting. It is the heart of many business intelligence architectures.

How it works: Data is extracted from operational systems via ETL processes, transformed (standardised, cleaned) and loaded into the data warehouse. The data structure is defined in advance ("schema on write"), which enables fast, consistent queries.

Business example: A retail group consolidates sales data from 200 stores, its online shop and its ERP system into a central data warehouse. The BI team can then retrieve cross-region analyses in real time.

Common solutions:

• Cloud-based: AWS Redshift, Google BigQuery, Snowflake, Azure Synapse
• On-premises: SAP BW, Oracle Warehouse Builder, IBM Db2

How it differs: A data warehouse is structured and optimised for known analyses. A data lake stores raw data flexibly for unknown, future questions.

Related terms: Data Lake, Business Intelligence, ETL

Data wrangling (also: data munging) is the process of cleaning, transforming and standardising raw data so it can be used for analysis or in machine learning models. In practice this step often takes up the largest share of a data scientist's time.

How it works: Real-world raw data is rarely clean: values are missing, formats are inconsistent, duplicates exist, units do not match. Data wrangling addresses all of this systematically before the actual analysis begins.

Typical steps:

• Merging data from various sources
• Handling missing values (delete, interpolate, replace)
• Removing duplicates and outliers
• Standardising formats (date, currency, spelling)
• Type conversions and unit conversions
• Creating new, derived variables

Business example: A logistics company captures delivery data from three systems in different formats. Before routes can be optimised, addresses must be normalised, missing timestamps estimated and duplicate entries removed, that is data wrangling.

How it differs: Data wrangling is a preparatory step before the actual analysis. It is not about gaining insights but about ensuring data quality. "Garbage in, garbage out", without careful wrangling, every downstream analysis is unreliable.

Related terms: Data Engineer, Feature Engineering, Data Ingestion

Deep learning is a subfield of machine learning based on multi-layer neural networks. Thanks to their large number of layers, these models can learn extremely complex patterns and representations, without manual feature engineering steps.

How it works: Each layer of the network learns increasingly abstract representations of the input data. In image recognition, for example: layer 1 detects edges, layer 2 shapes, layer 5 faces. Training requires large volumes of data and considerable computing power (typically GPUs).

Business example: A radiologist is supported by a deep learning model trained on millions of X-ray images that flags anomalies in new scans with high accuracy, as a second opinion, not a replacement.

Key architectures:

• Convolutional neural networks (CNN): image recognition
• Recurrent neural networks (RNN / LSTM): time series, text
• Transformers: NLP, LLMs
• Generative adversarial networks (GAN): image synthesis

How it differs: Deep learning is a subset of machine learning that relies specifically on deep neural networks. Classic ML (e.g. random forest, linear regression) needs neither deep networks nor necessarily large datasets.

Related terms: Neural Network, Machine Learning, Transformer Architecture

Descriptive analytics answers the question: "What happened?" It analyses historical data and summarises it in understandable reports, metrics and visualisations.

How it works: Based on data aggregation and statistical measures (mean, median, distributions), trends, patterns and anomalies are made visible, often in dashboards or standardised reports.

Business example: A retailer analyses the past Christmas season: which products sold best? In which regions? Which discount campaigns boosted revenue the most?

Typical visualisations:

• Bar and line charts
• Heatmaps and geographic maps
• Pivot tables and KPI tiles

How it differs: Descriptive analytics looks back. Predictive analytics looks ahead. Prescriptive analytics recommends actions.

Related terms: Business Intelligence, Predictive Analytics, Prescriptive Analytics

An embedding is a numerical representation of objects, typically text, images or other data, as a vector in a high-dimensional space. Similar objects sit close together, dissimilar ones further apart.

How it works: An embedding model (e.g. Word2Vec, BERT or OpenAI's text-embedding-ada) processes an input value and returns a vector of numbers, e.g. 1,536 numbers for a piece of text. This vector encodes the semantic meaning. When "king" minus "man" plus "woman" roughly equals "queen", embeddings are working.

Business example: A customer service system stores all previous support requests as embeddings. When a new request comes in, the system immediately finds the semantically most similar past requests and suggests proven answers.

Applications:

• Semantic search (search by meaning, not just keywords)
• Recommendation systems
• Duplicate detection in large text collections
• The foundation for RAG systems
• Similarity analysis in product catalogues

How it differs: An embedding is not a classification, it does not assign anything to a category but represents objects as a point in a semantic space. Statements about similarity only emerge from comparing embeddings (e.g. via cosine similarity).

Related terms: Large Language Model, RAG, Tokenisation

Feature engineering is the process of extracting, transforming or creating the most relevant and informative features from raw data for a machine learning model. Good feature engineering is often more decisive for model quality than the choice of algorithm.

How it works: A feature is a single measurable property of a data point. From a transaction timestamp, for example, you can derive: weekday, time of day, days since last purchase, season, all potentially informative features. Feature selection then picks the most relevant ones to avoid overfitting.

Business example: For a credit risk model the raw data (account number, date of birth, transactions) says little. Feature engineering turns it into: average monthly income, spending volatility, number of payment defaults in the last 12 months, features the model can actually use.

Key techniques:

• Normalisation and standardisation of numerical values
• One-hot encoding of categorical variables
• Deriving time-based features from timestamps
• Interaction features (product or ratio of two features)
• Dimensionality reduction (PCA)

How it differs: Feature engineering is manual and domain-knowledge-driven. Deep learning models can learn many features automatically (automatic feature learning), which displaces manual feature engineering in some areas but does not replace it everywhere.

Related terms: Data Wrangling, Machine Learning, Supervised Learning

Fine-tuning is the process of further training a pre-trained model, e.g. a large language model, on a specific, smaller dataset to adapt it to a particular task or domain.

How it works: A pre-trained model already has broad general knowledge. In fine-tuning, this model is further optimised with domain-specific examples (e.g. internal documents, specific writing styles, technical terminology). The model keeps its general knowledge but learns the specific requirements of the new task.

Business example: A pharmaceutical company takes a general language model and fine-tunes it with internal research reports. The resulting model answers questions about internal studies more precisely and uses the company's own terminology correctly.

Variants:

• Full fine-tuning: all model parameters are adjusted (costly, expensive)
• Parameter-efficient fine-tuning (PEFT): only a small share of parameters is adjusted (e.g. LoRA, more efficient and cheaper)
• Instruction tuning: the model is trained to follow instructions better
• RLHF (reinforcement learning from human feedback): the model is optimised against human ratings

How it differs: Fine-tuning changes the model itself. Prompt engineering changes only the input; the model stays unchanged. RAG enriches the input with external knowledge without changing the model.

Related terms: Large Language Model, Prompt Engineering, Transfer Learning

Generative AI refers to AI systems that can create new content, text, images, audio, video, code or synthetic data. Unlike classic AI models that only classify or predict, generative models produce original outputs.

How it works: Generative models learn the statistical structure of their training data so well that they can produce new data matching that structure. The best-known architectures are transformer-based large language models for text and diffusion models for images (e.g. Stable Diffusion, DALL·E).

Business example: A marketing team uses generative AI to create dozens of text variants for A/B tests in minutes. A software company speeds up development by having developers generate code snippets from natural-language requests.

Applications:

• Text creation and summarisation
• Code generation and completion
• Image and video synthesis
• Synthesising training data for other models
• Automated report generation
• Personalised customer communication

How it differs: Classic AI recognises and classifies (e.g. "Is this spam?"). Generative AI creates (e.g. "Write an email"). Generative AI also carries risks: hallucinations, deepfakes or copyright questions.

Related terms: Large Language Model, Transformer Architecture, Hallucination

Hallucination is the phenomenon where an AI language model generates statements that sound plausible but are factually wrong or entirely made up, without the model signalling this.

How it works: Language models generate text by computing statistically likely continuations. They do not "know" whether a statement is true, they produce what reads as linguistically coherent. In areas where the model has little training data, or when asked for specific facts, the risk of hallucination rises.

Business example: An employee asks an AI system about an internal company standard. The model answers confidently with a document, one it invented. Without verification the wrong information is passed on.

Typical types of hallucination:

• Invented facts (wrong figures, names, dates)
• Non-existent sources or citations
• Misinterpretation of context
• Seemingly logical but factually wrong conclusions

Countermeasures:

• Retrieval-augmented generation (RAG): the model is given verified sources as context
• Fine-tuning on verified data
• Human review of critical outputs
• Using models that cite their sources

How it differs: Hallucination is not an "error" in the classic sense; the model works technically correctly. It is a structural property of generative models that can be reduced through suitable system architecture but not fully eliminated.

Related terms: Large Language Model, RAG, Generative AI

Image recognition (computer vision) is the ability of software systems to automatically identify, classify and interpret content in images or videos. It is based on neural networks, in particular convolutional neural networks (CNNs).

How it works: The model is trained on large, labelled image datasets. It gradually learns to assemble simple features (edges, colours) into more complex structures (shapes, objects). After training it can classify new, unknown images.

Business example: A machine manufacturer uses image recognition in quality control: a camera photographs each component and the model detects cracks or deviations in milliseconds, more reliably and faster than the human eye.

Applications:

• Quality assurance in production
• Face recognition and access control
• Medical image analysis (X-ray, MRI)
• Autonomous driving (object detection in traffic)
• Plant disease or pest detection in agriculture

How it differs: Image recognition classifies images (e.g. "cat" or "dog"). Object detection goes a step further and locates multiple objects within an image at once using bounding boxes.

Related terms: Neural Network, Deep Learning, Supervised Learning

Industry 4.0 refers to the digital transformation of industrial production through the networking of machines, plants and processes, also known as the fourth industrial revolution. At its core is the integration of cyber-physical systems, the Internet of Things (IoT) and data-driven algorithms.

How it works: Sensors continuously capture machine data (temperature, vibration, utilisation). This data is transmitted in real time, analysed and used to control processes, either automatically through algorithms or as a basis for operators' decisions.

Business example: A factory monitors all production lines via sensors. An algorithm detects from vibration data that a machine will fail in 48 hours, and automatically triggers a maintenance order before an unplanned outage occurs (predictive maintenance).

Core technologies:

• Internet of Things (IoT)
• Edge computing
• Cloud platforms
• Machine learning for predictive maintenance
• Digital twins

Related terms: Machine Learning, Time Series Analysis, Pattern Recognition

Knowledge discovery in databases (KDD) describes the complete, multi-stage process of extracting useful knowledge from large datasets, from selecting raw data to the interpreted insight.

Process steps:

1. Data selection: select relevant data from existing sources
2. Pre-processing: cleaning, handling missing values, consistency checks
3. Transformation: convert data into suitable formats and representations
4. Data mining: apply algorithms (clustering, classification, regression, etc.)
5. Interpretation & evaluation: check results for domain relevance and communicate them

How it differs: Data mining is only one step within the KDD process, namely the algorithmic search for patterns. KDD describes the entire knowledge-generation process.

Related terms: Data Wrangling, Data Science

A large language model (LLM) is an AI model trained on very large amounts of text that understands, generates and processes natural language. LLMs are the foundation behind systems such as ChatGPT, Claude or Gemini.

How it works: LLMs are based on the transformer architecture. They are first trained unsupervised on enormous amounts of text (often hundreds of billions of words) to learn language structures. They are then optimised for helpful, safe conversations through fine-tuning and RLHF (reinforcement learning from human feedback).

Business example: An insurance group uses an internally hosted LLM that helps claims handlers analyse and summarise long claim reports. Handling time per case drops noticeably.

Applications:

• Text summarisation and extraction
• Automated customer service (chatbots)
• Code assistance for developers
• Document analysis and classification
• Internal knowledge search (RAG)
• Automated report generation

Well-known models: GPT-4 (OpenAI), Claude (Anthropic), Gemini (Google), Llama (Meta), Mistral.

How it differs: LLMs are a subfield of generative AI specialised in language. Multimodal models can additionally process images, audio or video. Classic NLP models (e.g. named entity recognition) are smaller and more task-specific.

Related terms: Transformer Architecture, Prompt Engineering, RAG, Fine-Tuning, Hallucination

Machine learning (ML) is a subfield of artificial intelligence in which algorithms learn from data to recognise patterns and make predictions, without being explicitly programmed for every situation.

How it works: An ML model is optimised on a training dataset by iteratively adjusting its internal parameters to minimise a defined error. The result is a model that generalises to new, unseen data.

Learning paradigms:

• Supervised learning: learning from labelled examples
• Unsupervised learning: finding patterns without labels
• Reinforcement learning: learning through reward and penalty

Business example: A utility company uses ML to forecast electricity consumption for the next 24 hours, based on weather data, time of day and historical consumption patterns. This enables more efficient grid management.

How it differs: ML is a tool within the broader field of AI. Not all AI systems use ML (rule-based systems, for example, do not). Deep learning is a subfield of ML based on multi-layer neural networks.

Related terms: Deep Learning, Feature Engineering, Supervised Learning

Microservices are a software architecture pattern in which an application consists of a collection of small, independently deployable services, each fulfilling a clearly defined function and communicating through defined interfaces (APIs).

How it works: Instead of a monolithic application in which all functions are tightly interwoven, a microservice architecture consists of services such as "authentication", "order management" or "recommendation engine", which can be developed, tested, scaled and updated independently of one another.

Business example: On Black Friday, an e-commerce company can scale up just the "checkout" microservice, rather than upgrading the entire application. If one service fails, all the others keep running.

Advantages:

• Independent scalability of individual components
• Faster development through parallel work by multiple teams
• Technology flexibility (each service can be written in the best-suited language)
• Better fault tolerance

How it differs: Unlike a monolith (everything in one application) or a service-oriented architecture (SOA), microservices are smaller, more autonomous and more strongly geared towards independent deployment.

Related terms: API, Data Pipeline

A multimodal model is an AI system that can jointly process and understand several data modalities, typically text, images, audio and video.

How it works: Multimodal models have separate encoders for different modalities, which are merged into a shared representation space. The model learns to understand relationships between modalities, e.g. describing the content of an image in text, or generating an image from a text description.

Business example: An insurance company has damage photos analysed automatically by a multimodal model: the model describes the damage, estimates the repair cost and generates a first draft of the claim report, all in one step.

Applications:

• Automatic image captioning
• Visual document analysis (invoices, forms)
• Video analysis with language understanding
• Medical diagnostic support (image + text)

Well-known models: GPT-4o (OpenAI), Gemini (Google), Claude 3 (Anthropic).

How it differs: A pure LLM processes text only. A multimodal model extends this to further input types. Generative multimodal models can also output images or audio (e.g. DALL·E, Sora).

Related terms: Large Language Model, Generative AI, Image Recognition

Natural language processing (NLP) is a subfield of AI concerned with the understanding, analysis and generation of human language by computer systems.

How it works: NLP systems process text in several steps: tokenisation (breaking text into units), parsing (recognising grammatical structures), semantic analysis (understanding meaning) and pragmatic analysis (taking context into account). Modern NLP systems are based on transformer architectures and far exceed the capabilities of earlier rule-based approaches.

Business example: An energy group analyses thousands of customer complaints from emails and chats automatically via NLP each day: topics are recognised, urgency is assessed and tickets are automatically routed to the right teams.

Applications:

• Automatic text summarisation
• Sentiment analysis
• Chatbots and virtual assistants
• Machine translation
• Information extraction from unstructured documents

How it differs: Classic NLP used rule-based and statistical methods for specific subtasks. Modern LLMs can handle almost all NLP tasks in a single model, at a considerably higher level.

Related terms: Large Language Model, Token / Tokenisation, Text Mining

An artificial neural network (ANN) is a computational model loosely inspired by the structure of the human brain. It consists of layers of interconnected nodes (neurons) that together can learn complex patterns in data.

How it works: Information flows from the input layer through several hidden layers to the output layer. Each connection has a weight that is adjusted during training via backpropagation to minimise the prediction error. With more layers, deep neural networks (deep learning) emerge.

Business example: A credit card provider uses a neural network for fraud detection. It analyses over 100 features of a transaction in real time and decides in milliseconds whether a fraud attempt is taking place.

Applications:

• Image and speech recognition
• Predictive models
• Recommendation systems
• Medical diagnosis

How it differs: Shallow neural networks have few layers and suit simpler tasks. Deep learning refers to networks with many layers that can model more complex relationships.

Related terms: Deep Learning, Machine Learning, Transformer Architecture

Open data is data that is freely accessible, usable and redistributable without restrictions from licences or copyright, for anyone and for any purpose.

How it works: Open data is typically published by governments, research institutions or companies under open-data licences (e.g. Creative Commons). It is available in standardised, machine-readable formats (CSV, JSON, XML).

Business example: A logistics start-up uses open geodata from the Federal Agency for Cartography and weather data from the German Weather Service free of charge for its route-optimisation model, without collecting any data of its own.

Important sources:

• GovData (Germany): open administrative data
• Eurostat: statistical data of the EU
• OpenStreetMap: map data
• UCI Machine Learning Repository: datasets for ML research
• Destatis: Federal Statistical Office

How it differs: Open data does not mean anonymised data. Data subject to data-protection law (e.g. personal data) may not be published even when it is technically available.

Related terms: Data Lake, Data Ingestion

Pattern recognition deals with the automatic identification of regularities, structures and hidden relationships in data, regardless of whether they come as images, text, time series or measurements.

How it works: Algorithms analyse data points for shared properties or recurring structures. Depending on the task, rule-based methods, statistical methods or neural networks are used.

Business example: A telecommunications provider uses pattern recognition on network data to detect the typical early signs of a device failure, and can intervene preventively before customers are affected.

Applications:

• Anomaly detection in production processes
• Fraud detection in financial transactions
• Speech recognition and processing
• Biometric authentication

Related terms: Machine Learning, Image Recognition, Signal Processing

Predictive analytics answers the question: "What will happen?" It uses historical data, statistical models and machine learning algorithms to calculate the probabilities of future events or developments.

How it works: Based on historical patterns, the model learns which factors influence future events. The model is optimised on training data and then applied to new data to make predictions.

Business example: A telecommunications provider analyses usage patterns, complaint history and contract data to predict the probability that a customer will cancel within the next 30 days (churn prediction). High-risk customers proactively receive an offer.

Applications:

• Churn prediction (customer retention)
• Predictive maintenance (forecasting machine failures)
• Demand forecasting and inventory management
• Credit risk assessment
• Weather forecasting

How it differs: Descriptive analytics explains the past. Predictive analytics forecasts the future. Prescriptive analytics goes a step further and recommends concrete actions.

Related terms: Descriptive Analytics, Prescriptive Analytics, Machine Learning

Prescriptive analytics answers the question: "What should we do?" It goes beyond description and prediction and recommends concrete options for action by simulating and evaluating their effects.

How it works: Based on predictive models and optimisation algorithms, various decision scenarios are played through. The system calculates which measure leads to the best results, taking defined goals and constraints into account.

Business example: An airline uses prescriptive analytics for dynamic pricing: the system calculates in real time the optimal ticket price that maximises capacity utilisation while optimising revenue per flight, taking demand, booking status and competitor prices into account.

Applications:

• Dynamic price modelling
• Supply chain optimisation
• Capacity planning in healthcare
• Portfolio optimisation in finance

How it differs: Predictive analytics forecasts what will happen. Prescriptive analytics says what should be done. The latter builds on predictive models and adds optimisation logic.

Related terms: Predictive Analytics, Descriptive Analytics, Machine Learning

A prompt is the input given to an AI language model, a question, instruction or context on which the model generates an output. Prompt engineering is the systematic optimisation of these inputs to maximise the quality of the outputs.

How it works: Language models are highly context-dependent. Small changes in how a prompt is phrased can fundamentally change the output. Prompt engineering uses this effect deliberately: through clear role instructions, examples, format specifications or step-by-step reasoning (chain-of-thought).

Business example: A legal team uses an LLM daily for contract review. With a well-designed prompt ("Analyse this contract as an experienced lawyer. List every clause that could be disadvantageous to us, with a reason and page number.") it gets structured, usable outputs, instead of generic summaries.

Key techniques:

• Zero-shot: pose the task directly, without examples
• Few-shot: provide a few examples as a template
• Chain-of-thought: let the model reason step by step
• System prompt: define the model's basic role and behaviour
• Structured output: specify the output format (JSON, table, list)

How it differs: Prompt engineering changes only the input; the model itself stays unchanged. Fine-tuning changes the model through further training. For many use cases, prompt engineering is the faster, cheaper first approach.

Related terms: Large Language Model, Fine-Tuning, RAG

Reinforcement learning (RL) is a machine learning paradigm in which an agent learns to make decisions that maximise a long-term reward through interaction with an environment.

How it works: The agent observes the state of the environment, chooses an action, receives a reward (positive or negative) and updates its strategy (policy) accordingly. Over many iterations it learns which sequences of actions lead to the best results.

Business example: A robot in a warehouse learns through reinforcement learning how to grip and transport packages efficiently, without explicit programming of every movement. Through millions of simulated attempts it optimises the use of its gripper arm.

Applications:

• Robot control and autonomous systems
• Autonomous driving
• Game optimisation (AlphaGo, AlphaFold)
• Optimising supply chains and resource planning
• Training LLMs (RLHF)

How it differs: In supervised learning, the model learns from labelled examples. In reinforcement learning it learns through its own actions and feedback, without being shown beforehand what is correct.

Related terms: Machine Learning, Fine-Tuning, Neural Network

RAG is an architecture that combines large language models with an external knowledge base. Before the model generates an answer, it searches a structured source for relevant information and uses it as context.

How it works:

1. The user's query is converted into an embedding.
2. The vector database searches for the semantically most similar documents or passages.
3. These documents are passed to the LLM together with the original query as context.
4. The model generates an answer based on the supplied information.

Business example: A mechanical engineering company has thousands of internal maintenance manuals, service reports and technical specifications. A RAG system lets technicians ask questions in natural language ("What torque is used for bolt M12 on model X?") and receive precise answers from the actual documents, with a source reference.

Advantages:

• Significantly reduces hallucinations
• No retraining needed for new information
• Answers are traceable to verifiable sources
• Cheaper than fine-tuning for knowledge-based applications

How it differs: RAG supplements the model with external knowledge at runtime. Fine-tuning anchors knowledge permanently in the model through training. RAG is more flexible and current; fine-tuning is better suited to adjusting style or behaviour.

Related terms: Large Language Model, Embedding, Hallucination, Vector Database

Signal processing refers to methods for processing and analysing data that comes as continuous or discrete signals, e.g. time series, audio data, images or sensor measurements.

How it works: Signals are filtered (noise removed), transformed (e.g. Fourier transform for frequency analysis), segmented and reduced to meaningful features, which are then used for further analysis or ML models.

Business example: A wind farm operator analyses vibration signals from the turbine bearings in real time. Signal processing algorithms detect frequency changes that indicate impending wear, before a human technician notices anything.

Applications:

• Predictive maintenance through vibration analysis
• Speech recognition (audio processing)
• ECG and EEG analysis in medicine
• Radar and sonar signal processing

Related terms: Pattern Recognition, Time Series Analysis, Industry 4.0

A smart city uses digital technologies and data analysis to make urban infrastructure more efficient, conserve resources and improve residents' quality of life.

How it works: Sensor networks, IoT devices and connected systems continuously capture data on traffic, energy, water, waste and the environment. This data feeds into analysis platforms that enable real-time decisions or automated control interventions.

Applications:

• Adaptive traffic control to reduce congestion
• Smart street lighting (active only when needed)
• Forecast-based waste collection (sensors report full bins)
• Real-time monitoring of air quality and noise
• Digital citizen services and participation platforms

Related terms: Industry 4.0, Internet of Things (IoT), Time Series Analysis

Statistical inference refers to methods used to draw conclusions about a larger population from a sample, taking uncertainty and variability into account.

How it works: Based on sample data, estimators (e.g. mean, variance) are calculated, hypotheses are tested (e.g. "Does measure A have an effect?") and confidence intervals are determined, indicating how certain a statement is.

Business example: A pharmaceutical company tests a new drug on 500 patients. Statistical inference enables the statement: "With 95% confidence the drug is more effective than the placebo", based on the sample, not the entire population.

Key concepts:

• Hypothesis testing and p-value
• Confidence intervals
• Bayesian vs. frequentist statistics
• A/B tests in marketing and product development

Related terms: Descriptive Analytics, Data Science, Predictive Analytics

Structured data is data organised in a clearly defined schema, typically in tabular form with fixed columns and data types. It is easy to store, query and analyse.

How it works: Structured data can be stored directly in relational databases (SQL). Each row represents a record, each column an attribute with a defined type (text, number, date, etc.).

Examples: customer databases, transaction data, product catalogues, measurements from production facilities.

How it differs: Unstructured data such as emails, images or social media posts follows no fixed schema and requires special processing. Semi-structured data (JSON, XML) has a flexible but recognisable structure.

Related terms: Unstructured Data, Data Warehouse, Data Wrangling

Supervised learning is the most widely used learning paradigm in machine learning. The model learns a mapping from input to output by training on examples where the correct output is known.

How it works: The training dataset contains pairs of input (features) and known output (label). The model iteratively adjusts its parameters to minimise the difference between prediction and actual output. After training it generalises to new, unseen data.

Business example: An email provider trains a spam-filter model on millions of labelled emails ("spam" / "not spam"). The model learns which features (sender, subject, content) are characteristic of spam and reliably filters new emails.

Main tasks:

• Classification: the output is a category
• Regression: the output is a continuous value

How it differs: Supervised learning needs labelled data, which is often expensive and time-consuming to create. Unsupervised learning works without labels but delivers results that are less directly controllable.

Related terms: Classification, Unsupervised Learning, Feature Engineering

TensorFlow is an open-source machine learning library originally developed by Google's Brain team. It enables the development, training and deployment of ML models across various platforms, from mobile devices to cloud clusters.

How it works: TensorFlow represents computations as directed graphs in which nodes are mathematical operations and edges are data tensors. The library automatically optimises these computations for the available hardware (CPU, GPU, TPU).

Ecosystem:

• TensorFlow Core: fundamental ML operations
• Keras: high-level API for fast model prototyping
• TensorFlow Lite: optimised for mobile and edge devices
• TensorFlow.js: ML in the browser via JavaScript
• TensorFlow Serving: deploying models in production

How it differs: TensorFlow is one of several leading ML frameworks. PyTorch (Meta) is especially popular in research. Scikit-learn suits classic ML. Keras abstracts TensorFlow for easier use.

Related terms: Deep Learning, Neural Network, Machine Learning

Text mining (text analytics) refers to the automatic extraction of structured information from unstructured text using NLP and data-mining methods.

How it works: Texts are tokenised, analysed linguistically and examined for patterns. Results can be classifications (e.g. sentiment: positive/negative), extracted entities (names, places, dates) or thematic clusters.

Business example: A consumer goods manufacturer analyses thousands of online reviews and social media comments each day. Text mining automatically identifies which product features are rated positively and which negatively, without manual reading.

Applications:

• Customer feedback analysis
• Market research and competitive monitoring
• Automatic document classification
• Forensics and compliance monitoring
• News monitoring

How it differs: Text mining extracts information from existing text. Generative AI creates new text. NLP is the technical foundation text mining builds on.

Related terms: NLP, Unstructured Data

Time series analysis covers statistical and algorithmic methods for examining data that is ordered in time and collected at regular intervals. The goal is to understand patterns, detect anomalies and predict future values.

How it works: Time series data has typical structures: trend (long-term direction), seasonality (recurring patterns), cycles and noise. Common methods such as ARIMA, SARIMA or neural approaches (LSTM, transformer) model these structures and use them for forecasts.

Business example: A utility company forecasts hourly electricity demand based on historical consumption data, temperature and calendar events. Time series analysis enables precise production planning and prevents costly overcapacity.

Applications:

• Demand forecasting in retail
• Anomaly detection in production processes
• Financial market and stock analysis
• Forecasting maintenance needs (predictive maintenance)
• Epidemiological modelling

How it differs: Time series analysis is a specialised form of data analysis that explicitly models the temporal dependence between observations, unlike classic ML models, which treat data points as independent.

Related terms: Predictive Analytics, Signal Processing, Machine Learning

A token is the smallest processing unit of a language model. Tokenisation is the process of breaking a text into these units. Tokens are neither necessarily words nor individual letters, they typically lie somewhere in between.

How it works: Modern tokenisers (e.g. byte pair encoding, BPE) break text into frequently occurring character sequences. The word "tokenisation", for example, might be split into "token", "is", "ation". Each token is assigned a number (ID) that the model processes.

Why tokens matter:

• The context length of an LLM is measured in tokens (e.g. 128,000 tokens ≈ 100,000 words)
• API costs are calculated per token
• Tokens influence how a model "sees" text, typos or unusual spellings can disrupt tokenisation

Business example: A company plans to have long contracts (30 pages ≈ 15,000 tokens) analysed by an LLM. Understanding tokens helps with cost planning and choosing the right model.

How it differs: Tokens are the model's "vocabulary", not to be confused with semantic embeddings, which encode the meaning of a text as vectors.

Related terms: Large Language Model, Embedding, NLP

Transfer learning is the method of using an already pre-trained model as the starting point for a new task, instead of training a model from scratch.

How it works: A large model is first trained on a general, extensive dataset (pre-training). The learned representations are then transferred to a more specific task, either through fine-tuning (the model is further adapted) or feature extraction (the representations are used directly).

Business example: A start-up wants to develop a model for medical image recognition. Instead of needing millions of training images, it uses a pre-trained image recognition model (e.g. ResNet) and adapts it with a few thousand X-ray images. Transfer learning reduces the effort considerably.

Advantages:

• Drastically reduced data requirement for new tasks
• Considerably lower training costs
• A faster route to high-quality models

How it differs: Transfer learning is the principle of transferring existing knowledge. Fine-tuning is one concrete method by which transfer learning is implemented.

Related terms: Fine-Tuning, Large Language Model, Deep Learning

The transformer architecture is the technical foundation of most modern AI language models, including GPT, Claude, BERT and many others. It was introduced in 2017 by Google in the landmark paper "Attention Is All You Need" and has displaced earlier architectures such as RNNs and LSTMs in most NLP tasks.

How it works: The core mechanism is the self-attention mechanism: for each part of a text, the model learns which other parts are relevant, regardless of their position in the text. This enables it to capture long-range dependencies (e.g. a pronoun and its associated noun at the start of a long sentence). Transformers can also be trained in a highly parallel way, which is what makes training on huge datasets practical in the first place.

Business example: The transformer is the architecture behind every modern LLM-powered assistant. Without transformers there would be no GPT-4, no Claude and no Gemini, and so none of the modern AI applications companies use today.

Key variants:

• Encoder-only (e.g. BERT): good for text understanding and classification
• Decoder-only (e.g. GPT, Claude): good for text generation
• Encoder-decoder (e.g. T5, BART): good for translation and summarisation

How it differs: The transformer architecture is the blueprint. A large language model is the concrete implementation, a transformer trained on huge amounts of text.

Related terms: Large Language Model, Deep Learning, NLP

Unstructured data is data without a fixed schema or database format. It makes up the bulk of all data generated daily and often contains valuable information, but requires special processing methods.

Examples: emails, documents, social media posts, images, videos, audio files, raw sensor signals.

How it works: To make unstructured data analysable, it is converted into structured or vectorised formats through NLP (for text), image recognition (for images) or signal processing (for audio/sensor data).

Business example: A large share of the information in a pharmaceutical company sits in unstructured research reports, emails and clinical notes. Only through NLP and text mining does this information become systematically usable.

How it differs: Structured data fits into tables and is directly queryable. Unstructured data needs pre-processing but often offers richer information.

Related terms: Structured Data, Text Mining, NLP

Unsupervised learning refers to ML methods in which the model recognises patterns and structures in data without those being labelled. The model discovers the structure of the data on its own.

How it works: Without predefined answers, the algorithm searches for inherent structures, similarities, groupings, compressions or dependencies in the data.

Key methods:

• Clustering: grouping similar data points
• Dimensionality reduction (PCA, t-SNE, UMAP): making high-dimensional data visualisable
• Autoencoders: learning compressed representations
• Association analysis: finding frequent item combinations (e.g. "those who buy X often also buy Y")

Business example: A retailer analyses purchasing data without predefined customer segments. Unsupervised learning independently discovers five distinct customer groups, which are then targeted with marketing.

How it differs: In supervised learning there is a known answer for every training data point. In unsupervised learning there are no such answers, the model derives the structure itself.

Related terms: Clustering, Supervised Learning, Reinforcement Learning

A vector database is a specialised database system designed to efficiently store high-dimensional vectors (e.g. embeddings) and search them by similarity.

How it works: Instead of searching for exact matches (like SQL), a vector database performs a similarity search (approximate nearest neighbour search). It finds the vectors most similar to a query vector, in milliseconds, even with millions of entries.

Business example: A media company stores all articles as embeddings in a vector database. When a reader reads an article, the recommendation system immediately finds the ten most similar articles in content, without keyword matching.

Well-known solutions: Pinecone, Weaviate, Qdrant, Milvus, pgvector (PostgreSQL extension).

Applications:

• Semantic search
• RAG systems
• Recommendation systems
• Duplicate detection in large datasets

How it differs: Classic databases search for exact values. Vector databases search for semantic similarity. They are not a replacement for relational databases but a complement for AI applications.

Related terms: Embedding, RAG, Large Language Model

Web mining is the application of data-mining methods to data from the World Wide Web in order to extract patterns, structures and insights.

How it works: Web mining is divided into three areas: web content mining (analysing the content of web pages), web structure mining (evaluating link structures) and web usage mining (analysing user behaviour on web pages).

Business example: An e-commerce company uses web usage mining to analyse the paths users take through the online shop before they buy or drop off. The insights feed directly into UX optimisations.

Applications:

• SEO and search engine optimisation
• Competitive and price monitoring
• Trend analysis from social media
• User behaviour and conversion optimisation

Related terms: Text Mining

Last updated: 2026 | Supper & Supper GmbH, Berlin