Dictionary of Applied Machine Learning

data

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This entry distinguishes three main uses of the term data. In a general sense, data refer to an abstract raw material that is used (or consumed) by machine learning (ML) 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 (GDPR) and 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 machine learning systems (ML systems) (Standardization and Commission, 2015). Every machine learning (ML) method fits a model to 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 the distinction between unit and collection is irrelevant. 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 (easy to measure or compute) and labels (difficult to measure or compute, or in need of human annotation) (Shalev-Shwartz and Ben-David, 2014, Ch. 2).

As a formal collection, a dataset $\dataset = \{\datapoint^{(1)}, \ldots, \datapoint^{(\samplesize)}\}$ 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).

Figure 1 of the entry data
Figure 1: Three technical meanings of the word data in ML applications. (a) Data in the general sense: a temperature reading sampled at time instant $t^{*}$. (b) A data point $\datapoint=(\feature,\truelabel)$ with a single feature $\feature$ and label $\truelabel$. (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 (GDPR) (Parliament and Union, 2016) and the EU AI Act (Parliament and Union, 2024) use personal data and biometric data as defined legal terms. Here, data denote any recorded information about an identifiable natural person. 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 (Parliament and Union, 2016, Art. 4(1); Parliament and Union, 2024, Art. 3).

See also: data point, dataset, model, hypothesis, general data protection regulation, EU AI Act, personal data.

References

  1. Standardization and Commission (2015). Information technology --- Vocabulary.
  2. Shalev-Shwartz and Ben-David (2014). Understanding Machine Learning: From Theory to Algorithms. Cambridge Univ. Press.
  3. Goodfellow et al. (2016). Deep Learning. MIT Press.
  4. Parliament and 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).
  5. Parliament and 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).

Cite this entry

@misc{dictml_data,
  author = {Jung, Alexander},
  title = {data},
  howpublished = {Dictionary of Applied Machine Learning (course edition)},
  year = {2026},
  doi = {10.5281/zenodo.21569296},
  note = {ISBN 978-952-64-3013-3, CC BY 4.0, retrieved 2026-08-06},
  url = {https://dictionaryofml.org/terms/data.html}
}