Data and information visualization

Information mapping
Topics and fields
Business decision mapping Data visualization Graphic communication Infographics Information design Knowledge visualization Mental model Morphological analysis Ontology (information science) Schema (psychology) Visual analytics Visual language
Node–link approaches
Argument map Cladistics Cognitive map Concept lattice Concept map Conceptual graph Decision tree Dendrogram Graph drawing Hyperbolic tree Hypertext Issue map Issue tree Layered graph drawing Mind map Object-role modeling Organizational chart Pathfinder network Radial tree Semantic network Sociogram Timeline Topic map Tree structure ZigZag
See also
Design rationale Diagrammatic reasoning Entity–relationship model Geovisualization List of concept- and mind-mapping software Olog Ontology (philosophy) Problem structuring methods Semantic Web Treemapping Wicked problem
v t e

Data and information visualization (data viz/vis or info viz/vis) is the practice of designing and creating graphic or visual representations of^[2] quantitative and qualitative data and information with the help of static, dynamic or interactive visual items. These visualizations are intended to help a target audience visually explore and discover, quickly understand, interpret and gain important insights into otherwise difficult-to-identify structures, relationships, correlations, local and global patterns, trends, variations, constancy, clusters, outliers and unusual groupings within data.^[3][4][5] When intended for the public to convey a concise version of information in an engaging manner,^[3] it is typically called infographics.

Data visualization is concerned with presenting sets of primarily quantitative raw data in a schematic form, using imagery. The visual formats used in data visualization include charts and graphs, geospatial maps, figures, correlation matrices, percentage gauges, etc..

Information visualization deals with multiple, large-scale and complicated datasets which contain quantitative data, as well as qualitative, and primarily abstract information, and its goal is to add value to raw data, improve the viewers' comprehension, reinforce their cognition and help derive insights and make decisions as they navigate and interact with the graphical display. Visual tools used include maps for location based data; hierarchical^[6] organisations of data; displays that prioritise relationships such as Sankey diagrams; flowcharts, timelines.

Emerging technologies like virtual, augmented and mixed reality have the potential to make information visualization more immersive, intuitive, interactive and easily manipulable and thus enhance the user's visual perception and cognition.^[7] In data and information visualization, the goal is to graphically present and explore abstract, non-physical and non-spatial data collected from databases, information systems, file systems, documents, business data, which is different from scientific visualization, where the goal is to render realistic images based on physical and spatial scientific data to confirm or reject hypotheses.^[8]

Effective data visualization is properly sourced, contextualized, simple and uncluttered. The underlying data is accurate and up-to-date to ensure insights are reliable. Graphical items are well-chosen and aesthetically appealing, with shapes, colors and other visual elements used deliberately in a meaningful and non-distracting manner. The visuals are accompanied by supporting texts. Verbal and graphical components complement each other to ensure clear, quick and memorable understanding. Effective information visualization is aware of the needs and expertise level of the target audience.^[9][2] Effective visualization can be used for conveying specialized, complex, big data-driven ideas to a non-technical audience in a visually appealing, engaging and accessible manner, and domain experts and executives for making decisions, monitoring performance, generating ideas and stimulating research.^[9][3] Data scientists, analysts and data mining specialists use data visualization to check data quality, find errors, unusual gaps, missing values, clean data, explore the structures and features of data, and assess outputs of data-driven models.^[3] Data and information visualization can be part of data storytelling, where they are paired with a narrative structure, to contextualize the analyzed data and communicate insights gained from analyzing it to convince the audience into making a decision or taking action.^[2][10] This can be contrasted with statistical graphics, where complex data are communicated graphically among researchers and analysts to help them perform exploratory data analysis or convey results of such analyses, where visual appeal, capturing attention to a certain issue and storytelling are less important.^[11]

Data and information visualization is interdisciplinary, it incorporates principles found in descriptive statistics,^[12] visual communication, graphic design, cognitive science and, interactive computer graphics and human-computer interaction.^[13] Since effective visualization requires design skills, statistical skills and computing skills, it is both an art and a science.^[14] Visual analytics marries statistical data analysis, data and information visualization and human analytical reasoning through interactive visual interfaces to help users reach conclusions, gain actionable insights and make informed decisions which are otherwise difficult for computers to do. Research into how people read and misread types of visualizations helps to determine what types and features of visualizations are most understandable and effective.^[15][16] Unintentionally poor or intentionally misleading and deceptive visualizations can function as powerful tools which disseminate misinformation, manipulate public perception and divert public opinion.^[17] Thus data visualization literacy has become an important component of data and information literacy in the information age akin to the roles played by textual, mathematical and visual literacy in the past.^[18]