With today's flood of data circulating on storage media and the Internet, compression of digital data remains an immensely important aspect of data transmission and storage. This essential explains, without theoretical superstructure and with elementary mathematical methods, the most important compression methods, such as the entropy encodings of Shannon-Fano and of Huffman, as well as the dictionary encodings of the Lempel-Ziv family. Irrelevance reduction and quantization for optical and acoustic signals, which exploit the inadequacies of the human eye and ear for data compression, are also discussed in detail. The whole is illustrated by means of common practical applications from the everyday environment. The presentation allows for use in, for example, working groups at schools, introductory courses at universities, and is also suitable for interested laypersons. This Springer essential is a translation of the original German 1st edition essentials, Gut gepackt Kein Bit zu viel by Olaf Manz, published by Springer Fachmedien Wiesbaden GmbH, part of Springer Nature in 2020. The translation was done with the help of artificial intelligence (machine translation by the service DeepL.com). A subsequent human revision was done primarily in terms of content, so that the book will read stylistically differently from a conventional translation. Springer Nature works continuously to further the development of tools for the production of books and on the related technologies to support the authors. The Content Data transmission and storage Digitization Overview of data compression methods Entropy encodings Dictionary encodings Quantization The Author Dr. Olaf Manz worked as a research assistant and Heisenberg professor at the mathematical institutes of the universities of Mainz and Heidelberg. He then worked for many years at Siemens in IT product management and also knows data processing from the practical side. He is the author of the books "Fehlerkorrigierende Codes" and "Verschlusseln, Signieren, Angreifen" published by Springer