This dissertation explores how embedding small data-driven contextual visualizations can complement text documents. More specifically, I identify and define important aspects and relevant research directions for the integration of small data-driven contextual visualizations into text. This integration should eventually become as fluid as writing and as usable as reading a text. I define word-scale visualisations as small data-driven contextual visualizations embedded in text documents. These visualizations can use various visual encodings including geographical maps, heat maps, pie charts, and more complex visualizations. They can appear at a range of word scales, including sizes larger than a letter, but smaller than a sentence or paragraph. Word-scale visualisations can help support and be used in many forms of written discourse such as text books, notes, blog posts, reports, stories, or poems. As graphical supplements to text, word-scale visualisations can be used to emphasize certain elements of a document (e.g. a word or a sentence), or to provide additional information. For example, a small stock chart can be embedded next to the name of a company to provide additional information about the past trends of its stocks. In another example, game statistics can be embedded next to the names of soccer teams or players in daily reports from the UEFA European Championship. These word-scale visualisations can then for example allow readers to make comparison between number of passes of teams and players. The main benefit of word-scale visualisations is that the reader can remain focused on the text as the visualization are within the text rather than alongside it.In the thesis, I make the following main contributions: I explore why word-scale visualisations can be useful and how to support their creation. I investigate placement options to embed word-scale visualisations and quantify their effects on the layout and flow of the text. As word-scale visualisations also have implications on the reader's reading behavior I propose a first study that investigates different word-scale visualisation positions on the reading behavior. I also explore how word-scale visualisations can be combined with interaction to support a more active reading by proposing interaction methods to collect, arrange and compare word-scale visualisations. Finally, I propose design considerations for the authoring of word-scale visualisations and conclude with application examples.In summary, this dissertation contributes to the understanding of small data-driven contextual visualizations embedded into text and their value for Information Visualization.