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'''Information visualization''', on the other hand, deals with multiple, large-scale and complicated datasets which contain quantitative (numerical) data as well as qualitative (non-numerical, i.e. verbal or graphical) and primarily abstract information and its goal is to add value to raw data, improve the viewers' comprehension, reinforce their cognition and help them derive insights and make decisions as they navigate and interact with the computer-supported graphical display. Visual tools used in information visualization include [[map]]s (such as [[tree map]]s), [[animation]]s, [[infographic]]s, [[Sankey diagram]]s, [[flow chart]]s, [[network diagram]]s, [[semantic network]]s, [[entity-relationship diagram]]s, [[venn diagram]]s, [[timeline]]s, [[mind map]]s, etc.
[[Emerging technologies]] like [[virtual reality|virtual]], [[augmented reality|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]].<ref>{{Citation |title=Visualizing Big Data with augmented and virtual reality: challenges and research agenda. |last1=Olshannikova |first1=Ekaterina |last2=Ometov |first2=Aleksandr |last3=Koucheryavy |first3=Yevgeny |last4=Ollson |first4=Thomas |journal=Journal of Big Data |volume=2 |issue=22 |year=2015 |doi=10.1186/s40537-015-0031-2|doi-access=free }}</ref> In data and information visualization, the goal is to graphically present and explore abstract, non-physical and non-spatial data collected from [[database]]s, [[information system]]s, [[file system]]s, [[document]]s, [[
Effective data visualization is properly sourced, contextualized, simple and uncluttered. The underlying data is accurate and up-to-date to make sure that insights are reliable. Graphical items are well-chosen for the given datasets 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 (labels and titles). These verbal and graphical components complement each other to ensure clear, quick and memorable understanding. Effective information visualization is aware of the needs and concerns and the level of expertise of the target audience, deliberately guiding them to the intended conclusion.<ref name=IBM>{{Cite web |title=What is data visualization? |url=https://www.ibm.com/topics/data-visualization |website=IBM |access-date=27 March 2023}}</ref><ref name="Nussbaumer Knaflic"/> Such effective visualization can be used not only for conveying specialized, complex, big data-driven ideas to a wider group of non-technical audience in a visually appealing, engaging and accessible manner, but also to ___domain experts and executives for making decisions, monitoring performance, generating new ideas and stimulating research.<ref name=IBM/><ref name=HDSR/> In addition, data scientists, data analysts and data mining specialists use data visualization to check the quality of data, find errors, unusual gaps and missing values in data, clean data, explore the structures and features of data and assess outputs of data-driven models.<ref name=HDSR>{{Cite journal |title=Why Is Data Visualization Important? What Is Important in Data Visualization? |author=Antony Unwin |journal=Harvard Data Science Review |url=https://hdsr.mitpress.mit.edu/pub/zok97i7p/release/4 |date=31 January 2020 |volume=2 |issue=1 |doi=10.1162/99608f92.8ae4d525 |access-date=27 March 2023|doi-access=free }}</ref> In [[business]], data and information visualization can constitute a part of ''data storytelling'', where they are paired with a coherent [[narrative]] structure or [[Plot (narrative)|storyline]] to contextualize the analyzed data and communicate the insights gained from analyzing the data clearly and memorably with the goal of convincing the audience into making a decision or taking an action in order to create [[business value]].<ref name="Nussbaumer Knaflic"/><ref>{{Citation |title=Effective Data Storytelling: How to Drive Change with Data, Narrative and Visuals |author=Brent Dykes |publisher=John Wiley & Sons |page=16 |year=2019}}</ref> This can be contrasted with the field of [[statistical graphics]], where complex statistical data are communicated graphically in an accurate and precise manner among researchers and analysts with statistical expertise to help them perform [[exploratory data analysis]] or to convey the results of such analyses, where visual appeal, capturing attention to a certain issue and storytelling are not as important.<ref>{{Citation |title=Statistics: Concepts and Applications for Science |author=David C. LeBlanc |publisher=Jones & Bartlett Learning |year=2004 |pages=35–36}}</ref>
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