# data *Entry of the [Dictionary of Applied Machine Learning](https://dictionaryofml.org) (course edition), by Alexander Jung, Aalto University. CC BY 4.0. Updated 2026-10-29. [HTML](https://dictionaryofml.org/terms/data.html) · [typeset PDF](https://dictionaryofml.org/terms/data.pdf) · [LaTeX](https://dictionaryofml.org/terms/data.tex) · [Python demo](https://dictionaryofml.org/terms/data.py) · [notebook](https://dictionaryofml.org/terms/data.ipynb)* Math is LaTeX with the book's macros expanded, so no notation package is needed to read it. ## Abstract Data are representations of information, recorded in a form suitable for storage, communication and processing, and the word carries three technical senses in machine learning. In its general sense, it denotes recorded information in a form suitable for processing by machine learning methods. As a data point, it is an elementary information-carrying unit; as a dataset, it is an indexed collection of data points. A related regulatory term, personal data, is defined in the general data protection regulation and adopted by the EU AI Act. ## Definition A weather station measures the temperature at its location and stores the value $23.4\,^{\circ}\mathrm{C}$ together with a timestamp (see Fig. 1(a)). Such recorded values are data: representations of information, recorded in a form suitable for storage, communication, and processing by ML systems (International Organization for Standardization and International Electrotechnical Commission, 2015). Every machine learning method trains a model on data, and the usefulness of the hypothesis the method learns is limited by the quality and quantity of the available data. The word *data* is used in three technical senses, summarized in Fig. 1, and additionally in a legal sense. In its broadest use, data refer to recorded information. This sense applies when it does not matter whether a single data point or a collection of them is meant. Examples are raw sensor measurements such as the temperature reading in Fig. 1(a) and compound terms such as data parallelism, networked data, and data augmentation. As a formal unit, a data point is the fundamental information-carrying unit (Fig. 1(b)). Its information is contained in two types of properties: features, which are easy to measure or compute, and labels, which are difficult to measure or compute or need human annotation. The formal model of a domain set, a label set and a labeling function that assigns each domain point its label is given by Shalev-Shwartz and Ben-David (2014, Sect. 2.1). As a formal collection, a dataset $\mathcal{D} = \{{\mathbf z}^{(1)}, \ldots, {\mathbf z}^{(m)}\}$ is the indexed collection of data points on which model training and validation are performed (Fig. 1(c)) (Goodfellow et al., 2016, Sect. 5.1). The quality and the quantity of data that limit a learned hypothesis are properties of this collection: the labels of its data points and their number. ![Figure 1 of the entry data](https://dictionaryofml.org/terms/fig/data-fig1.svg) *Figure 1: Three technical meanings of the word *data* in machine learning applications. (a) Data in the general sense: a temperature reading sampled at time instant $t^{*}$. (b) A data point ${\mathbf z}=(x,y)$ with a single feature $x$ and label $y$. (c) A dataset that consists of a finite number of data points* The word *data* also carries a regulatory meaning. The general data protection regulation defines personal data as any information relating to an identified or identifiable natural person (European Parliament and Council of the European Union, 2016, Art. 4(1)). The EU AI Act adopts that definition and defines biometric data as personal data resulting from specific technical processing (European Parliament and Council of the European Union, 2024, Art. 3(34) and 3(50)). Several regulatory obligations on ML systems, including the data minimization principle, the right to erasure, and the right to explanation, refer to data in this legal sense. **See also:** [data point](https://dictionaryofml.org/terms/datapoint.html), [dataset](https://dictionaryofml.org/terms/dataset.html), personal data, general data protection regulation, EU AI Act. ## References - International Organization for Standardization and International Electrotechnical Commission (2015). Information technology --- Vocabulary. - Goodfellow et al. (2016). Deep Learning. *MIT Press*. - European Parliament and Council of the European Union (2016). Regulation (EU) 2016/679 of the European Parliament and of the Council of 27 April 2016 on the protection of natural persons with regard to the processing of personal data and on the free movement of such data, and repealing Directive 95/46/EC (General Data Protection Regulation) (Text with EEA relevance). - European Parliament and Council of the European Union (2024). Regulation (EU) 2024/1689 of the European Parliament and of the Council of 13 June 2024 laying down harmonised rules on artificial intelligence and amending Regulations (EC) No 300/2008, (EU) No 167/2013, (EU) No 168/2013, (EU) 2018/858, (EU) 2018/1139 and (EU) 2019/2144 and Directives 2014/90/EU, (EU) 2016/797 and (EU) 2020/1828 (Artificial Intelligence Act) (Text with EEA relevance). - Shalev-Shwartz and Ben-David (2014). Understanding Machine Learning: From Theory to Algorithms. *Cambridge Univ. Press*. ## Cite this entry ```bibtex @incollection{dictml_data, author = {Jung, Alexander}, editor = {Olioumtsevits, Konstantina and Schnoor, Ekkehard}, title = {data}, booktitle = {Dictionary of Applied Machine Learning}, publisher = {Springer Nature Singapore}, year = {2026}, doi = {10.1007/978-981-92-2012-0_1-1}, isbn = {978-981-92-2012-0}, url = {https://doi.org/10.1007/978-981-92-2012-0_1-1} } ```