What is machine learning?

Machine learning is a subfield of artificial intelligence in which systems learn from data, recognize patterns, and make predictions without being explicitly programmed to do so. Algorithms analyze existing datasets, continuously adapt, and improve their results as more data becomes available. In companies, machine learning is used to automate processes, predict trends, and support data-driven decision-making.

Machine learning is a subfield of artificial intelligence (AI). It focuses on enabling computers to learn from data and algorithms, learn from experience independently, and continuously improve. Rather than being explicitly programmed for specific tasks, these systems identify patterns, make predictions, and optimize their performance as more data becomes available.

Machine learning technologies are used in many areas, from personalized recommendations in e-commerce to automated processes in industry, and continue to evolve to deliver increasingly accurate and efficient results.

The machine learning process requires selecting an appropriate learning model based on the type of data and the desired results. At its core are algorithms that learn from existing data in order to make predictions or decisions. These algorithms can be used individually or in combination. But how are these algorithms applied in the machine learning process?

Because machine learning is based on the imitation of certain human cognitive functions, machines must be trained to develop their own models. These models analyze data and enable the machine to learn and improve independently.

Machine learning models have three essential functions:

1. Describe what has happened by analyzing historical data.
2. Predict what will happen in the future based on identified patterns.
3. Recommend which actions should be taken next to achieve optimal results.

The machine learning process begins with data collection, followed by data preprocessing to clean and prepare the data. An appropriate learning model is then selected and trained during model training to identify patterns. During model evaluation, the model is tested and optimized. After model deployment, it is applied to new data in a real-world environment, continuously monitored, and updated when necessary to maintain accuracy.

Choosing the right type of machine learning is essential for achieving the desired results. Learning methods can be divided into three main categories:

In supervised learning, the algorithm works with labeled datasets. It learns by analyzing input data and identifying patterns that lead to the correct outputs. During training, the model continuously adjusts itself to optimize prediction accuracy. This method is particularly useful for tasks where the target is clearly defined, such as spam filtering or credit risk prediction. Common algorithms in this area include linear regression and neural networks.

Unsupervised learning uses algorithms that work without labeled data. The algorithm analyzes input data and searches for hidden patterns or structures without a predefined target variable. This method is particularly suitable for exploratory data analysis, where the goal is to uncover new insights, such as through customer segmentation. Common algorithms include K-means clustering and principal component analysis (PCA).

Semi-supervised learning combines elements of both approaches. A small labeled dataset is used to train the model, which is then applied to a larger amount of unlabeled data. This method is particularly effective when manually labeling large datasets is expensive or time-consuming, as is often the case with image or speech recognition. A semi-supervised model can therefore work more accurately and efficiently.

Machine learning is particularly useful when the following benefits are sought:

Artificial intelligence, machine learning, and deep learning are closely related concepts, but they have clear differences.

Artificial intelligence is the broader term and refers to the ability of machines to perform tasks that normally require human intelligence. These include problem-solving, decision-making, and natural language processing. AI uses algorithms to process large amounts of data, identify patterns, make decisions, and automate complex tasks.

Machine learning is a specialized subfield of AI and represents a significant advancement because it enables machines to learn from data rather than relying strictly on predefined rules. Machine learning enables systems to learn and improve independently through data analysis. Algorithms identify patterns in large datasets and use these patterns to make predictions or decisions. More data can help a model improve the accuracy of its results, provided that the additional data is relevant and of sufficient quality.

Deep learning is a subfield of machine learning. It refers to complex neural networks with many layers, hence the term “deep.” These networks are particularly suited to processing very large and unstructured datasets. Deep learning is especially powerful for tasks such as image and speech recognition, natural language processing, and autonomous decision-making.

At Bissantz, machine learning is not treated as an isolated discipline. Instead, it is applied where it provides tangible value for business management, embedded in carefully designed BI methods. In the AI-supported BI software DeltaMaster, machine learning methods are used, for example, for the automated prioritization and evaluation of KPIs. The software automatically identifies deviations, patterns, and correlations without requiring users to manually define every rule.

Unlike many black-box approaches, DeltaMaster presents results using a transparent and traceable logic. Users see not only a forecast, but can also understand why a value or development is relevant. This strengthens trust in the technology and supports data-driven decision-making at all levels.

At Bissantz, machine learning complements traditional KPI-based management by providing early indications of anomalies that might otherwise be lost in the sheer volume of data – as one component of a BI solution designed to support both visual and analytical understanding.

Machine learning enables systems to learn from data, identify patterns, and support data-driven decisions without relying on fixed programming rules. Depending on the available data and the objective, supervised, unsupervised, or semi-supervised methods can be used. At Bissantz, machine learning is integrated into DeltaMaster in a targeted and transparent way to automatically evaluate KPIs and provide a solid, traceable foundation for business management.