The global data visualization market, currently valued at $9.84 billion (2025), is experiencing robust growth, projected to expand at a Compound Annual Growth Rate (CAGR) of 10.95% from 2025 to 2033. This expansion is fueled by several key drivers. The increasing volume and complexity of data generated across various sectors necessitate efficient tools for analysis and interpretation. Businesses are increasingly recognizing the importance of data-driven decision-making, leading to significant investments in data visualization solutions. Furthermore, the rising adoption of cloud-based platforms and the growing demand for advanced analytical capabilities, such as predictive analytics and machine learning integration within visualization tools, are significantly contributing to market growth. The market is segmented by organizational department (Executive Management, Marketing, Operations, Finance, Sales, Other), deployment mode (On-premise, Cloud/On-demand), and end-user industry (BFSI, IT & Telecommunication, Retail/E-commerce, Education, Manufacturing, Government, Other). The competitive landscape is characterized by a mix of established players like Salesforce (Tableau), SAP, Microsoft, and Oracle, and smaller, specialized vendors. The competitive intensity is likely to remain high, with vendors focusing on innovation, strategic partnerships, and expanding their product portfolios to cater to specific industry needs. The North American market currently holds a significant share, driven by early adoption of advanced technologies and a robust IT infrastructure. However, the Asia-Pacific region is anticipated to witness the fastest growth due to increasing digitalization across various sectors and rising demand for data-driven insights in rapidly developing economies. While the on-premise deployment model still holds a considerable market share, the cloud/on-demand model is gaining traction owing to its scalability, cost-effectiveness, and accessibility. Factors such as data security concerns, integration complexities, and the need for specialized skills could act as potential restraints on market growth. However, ongoing technological advancements, coupled with increasing awareness of data visualization benefits, are expected to mitigate these challenges and drive market expansion in the coming years. Recent developments include: September 2022: KPI 360, an AI-driven solution that uses real-time data monitoring and prediction to assist manufacturing organizations in seeing various operational data sources through a single, comprehensive industrial intelligence dashboard that sets up in hours, was recently unveiled by SymphonyAI Industrial., January 2022: The most recent version of the IVAAP platform for ubiquitous subsurface visualization and analytics applications was released by INT, a top supplier of data visualization software. IVAAP allows exploring, visualizing, and computing energy data by providing full OSDU Data Platform compatibility. With the new edition, IVAAP's map-based search, data discovery, and data selection are expanded to include 3D seismic volume intersection, 2D seismic overlays, reservoir, and base map widgets for cloud-based visualization of all forms of energy data.. Key drivers for this market are: Cloud Deployment of Data Visualization Solutions, Increasing Need for Quick Decision Making. Potential restraints include: Cloud Deployment of Data Visualization Solutions, Increasing Need for Quick Decision Making. Notable trends are: Retail Segment to Witness Significant Growth.

All 311 Service Requests from 2010 to present. This information is automatically updated daily.

Click here to download data from 2011 - https://data.cityofnewyork.us/dataset/311-Service-Requests-From-2011/fpz8-jqf4

Click here to download data from 2012 - https://data.cityofnewyork.us/dataset/311-Service-Requests-From-2012/as38-8eb5

Click here to download data from 2013 - https://data.cityofnewyork.us/dataset/311-Service-Requests-From-2013/hybb-af8n

Click here to download data from 2014 - https://data.cityofnewyork.us/dataset/311-Service-Requests-From-2014/vtzg-7562

Click here to download data from 2015 - https://data.cityofnewyork.us/dataset/311-Service-Requests-From-2015/57g5-etyj

In recent years, data visualization has been gaining space in journalism, not only in the specialized press, but also in the general press. The objective of this article is to analyze whether there are differences between the impact of receiving a traditional news item and that of a news item with data visualization, in terms of interest, comprehension and attitudes toward data visualization. For this, a study (N = 700) was carried out with two experimental conditions (traditional news vs. news with data visualization), using scientific and health communication news. Moderated mediation analysis were performed to understand how data visualization affects factors such as attitude, or interest, and affects public comprehension. The results showed significant indirect effects that indicate that reading a data visualization news item increases comprehension and, with it, positive attitudes toward data visualization. Variables related to comprehension and interest have been found to have a significant impact on attitudes toward data viewing, opening new lines of research to delve into the factors that affect data-driven news performance.

The global data visualization market size reached USD 4.2 Billion in 2024. Looking forward, IMARC Group expects the market to reach USD 8.2 Billion by 2033, exhibiting a growth rate (CAGR) of 7.38% during 2025-2033. The increasing volume of data, the growing demand for real-time analytics, the need for better decision-making tools, advancements in AI and machine learning, and rising user-friendly tools and cloud-based solutions are some of the major factors propelling the market growth.

Global Data Visualization Tools Market has experienced tremendous growth in recent years and is poised to continue its strong expansion. The Data Visualization Tools Market reached a value of USD 7.89 billion in 2022 and is projected to maintain a compound annual growth rate of 10.24% through 2028.

This spreadsheet contains a collection of over 230 data visualisations about public finances from media organisations, journalists, civil society organisations, advocacy groups, civic hackers, companies and public institutions. In order to build the collection I started with a collection of projects derived from another study mapping “open budget data” on digital media (Gray, 2015). Over 65% of the 120 fiscal data projects identified through the study used visualisations to present information about public finances. Examples were also incorporated from other lists, including relevant items from a database of 466 projects from The Guardian and the New York Times from between 2000 and 2015 (Rooze, 2015), as well as from expert data visualisation blogs such as Infosthetics and Visual Complexity. Further examples were solicited from expert mailing lists, forums and targeted outreach via email and social media. Analyses of the data visualisations are forthcoming in several publications. The collection will continue to be updated periodically. If you have suggestions for projects to add, please get in touch: http://jonathangray.org/contact/

References Gray, J. (2015) "Open Budget Data: Mapping the Landscape". Available at: http://dx.doi.org/10.2139/ssrn.2654878Rooze, M. (2015) "News Graphics Collection". Available at: http://collection.marijerooze.nl/

Research dissemination and knowledge translation are imperative in social work. Methodological developments in data visualization techniques have improved the ability to convey meaning and reduce erroneous conclusions. The purpose of this project is to examine: (1) How are empirical results presented visually in social work research?; (2) To what extent do top social work journals vary in the publication of data visualization techniques?; (3) What is the predominant type of analysis presented in tables and graphs?; (4) How can current data visualization methods be improved to increase understanding of social work research? Method: A database was built from a systematic literature review of the four most recent issues of Social Work Research and 6 other highly ranked journals in social work based on the 2009 5-year impact factor (Thomson Reuters ISI Web of Knowledge). Overall, 294 articles were reviewed. Articles without any form of data visualization were not included in the final database. The number of articles reviewed by journal includes : Child Abuse & Neglect (38), Child Maltreatment (30), American Journal of Community Psychology (31), Family Relations (36), Social Work (29), Children and Youth Services Review (112), and Social Work Research (18). Articles with any type of data visualization (table, graph, other) were included in the database and coded sequentially by two reviewers based on the type of visualization method and type of analyses presented (descriptive, bivariate, measurement, estimate, predicted value, other). Additional revi ew was required from the entire research team for 68 articles. Codes were discussed until 100% agreement was reached. The final database includes 824 data visualization entries.

A talk at the 2020 ngVLA Summer Short Talk Series, by Alyssa Goodman. Normally, when astronomers see beautiful, “3D” movies of the Milky Way or the stars and clouds within it, they assume those movies to be cartoons or computer simulations. In this talk, I will describe revolutionary data sets and data-science techniques that are enabling REAL 3D movies of the Milky Way to be made—right now. In particular, I will focus on how so-called “3D dust mapping” has gotten a tremendous distance resolution boost from Gaia, and how interweaving velocity information from spectral-line data cubes, dust maps and 3D stellar velocities reveals never-before-seen views of the solar neighborhood of the Milky Way. The recent discovery Radcliffe Wave, which redefines our understanding of Milky Way’s Local Arm (http://tinyurl.com/radwave), is just the first in a series of revelations to come from this knitting project.

Terms like ‘big data’, ‘data science’, and ‘data visualisation’ have become buzzwords in recent years and are increasingly intertwined with journalism. Data visualisation may further blur the lines between science communication and graphic design. Our study is situated in these overlaps to compare the design of data visualisations in science news stories across four online news media platforms in South Africa and the United States. Our study contributes to an understanding of how well-considered data visualisations are tools for effective storytelling, and offers practical recommendations for using data visualisation in science communication efforts.

IntroductionIt has proven a hard challenge to stimulate climate action with climate data. While scientists communicate through words, numbers, and diagrams, artists use movement, images, and sound. Sonification, the translation of data into sound, and visualization, offer techniques for representing climate data with often innovative and exciting results. The concept of sonification was initially defined in terms of engineering, and while this view remains dominant, researchers increasingly make use of knowledge from electroacoustic music (EAM) to make sonifications more convincing.MethodsThe Aesthetic Perspective Space (APS) is a two-dimensional model that bridges utilitarian-oriented sonification and music. We started with a review of 395 sonification projects, from which a corpus of 32 that target climate change was chosen; a subset of 18 also integrate visualization of the data. To clarify relationships with climate data sources, we determined topics and subtopics in a hierarchical classification. Media duration and lexical diversity in descriptions were determined. We developed a protocol to span the APS dimensions, Intentionality and Indexicality, and evaluated its circumplexity.ResultsWe constructed 25 scales to cover a range of qualitative characteristics applicable to sonification and sonification-visualization projects, and through exploratory factor analysis, identified five essential aspects of the project descriptions, labeled Action, Technical, Context, Perspective, and Visualization. Through linear regression modeling, we investigated the prediction of aesthetic perspective from essential aspects, media duration, and lexical diversity. Significant regressions across the corpus were identified for Perspective (ß = 0.41***) and lexical diversity (ß = −0.23*) on Intentionality, and for Perspective (ß = 0.36***) and Duration (logarithmic; ß = −0.25*) on Indexicality.DiscussionWe discuss how these relationships play out in specific projects, also within the corpus subset that integrated data visualization, as well as broader implications of aesthetics on design techniques for multimodal representations aimed at conveying scientific data. Our approach is informed by the ongoing discussion in sound design and auditory perception research communities on the relationship between sonification and EAM. Through its analysis of topics, qualitative characteristics, and aesthetics across a range of projects, our study contributes to the development of empirically founded design techniques, applicable to climate science communication and other fields.

A data visualization representing Last-mile explosion

Data Visualization Market Outlook

As per our latest research, the global data visualization market size reached USD 12.8 billion in 2024, reflecting robust adoption across diverse industries. The market is projected to expand at a strong CAGR of 10.4% from 2025 to 2033, reaching an estimated USD 31.2 billion by 2033. This remarkable growth is primarily driven by the increasing need for actionable insights from big data, the proliferation of advanced analytics tools, and the growing emphasis on real-time decision-making within enterprises worldwide.

One of the primary growth factors propelling the data visualization market is the exponential increase in data generation across all sectors. Organizations are now inundated with structured and unstructured data from multiple sources such as IoT devices, social media platforms, enterprise applications, and transactional systems. The sheer volume and complexity of this data make traditional reporting tools inadequate for deriving meaningful insights. As a result, businesses are turning to advanced data visualization solutions that enable them to quickly interpret complex datasets, identify trends, and make informed decisions. The integration of artificial intelligence and machine learning into visualization platforms further enhances their capability to deliver predictive analytics and automated insights, which is fueling market expansion.

Another significant driver is the growing adoption of business intelligence (BI) and analytics platforms across organizations of all sizes. Companies are increasingly recognizing the value of data-driven decision-making, which has led to the widespread implementation of BI tools that rely heavily on effective data visualization. These platforms not only facilitate the exploration of large datasets but also enable users to create interactive dashboards and reports that can be easily shared across departments. The democratization of data analytics, where non-technical users can generate their own visualizations without relying on IT teams, has further accelerated market growth. Additionally, the shift towards cloud-based deployment models is making these solutions more accessible and cost-effective for small and medium enterprises (SMEs), broadening the market’s reach.

The rapid digital transformation initiatives undertaken by enterprises, particularly in emerging economies, are also contributing to the robust growth of the data visualization market. Digitalization efforts have led to the modernization of legacy IT infrastructure, the adoption of cloud computing, and the implementation of advanced analytics solutions. Governments and regulatory bodies are also encouraging the use of data analytics for transparency and efficiency, especially in sectors such as healthcare, public services, and finance. The increasing focus on customer experience, operational efficiency, and competitive differentiation is compelling organizations to invest in visualization tools that provide real-time insights and facilitate agile business processes. These factors collectively underpin the sustained growth trajectory of the global data visualization market.

From a regional perspective, North America continues to dominate the data visualization market, accounting for the largest revenue share in 2024, followed closely by Europe and Asia Pacific. The region’s leadership is attributed to the high adoption rate of advanced analytics solutions, the presence of major technology providers, and a mature digital ecosystem. Meanwhile, Asia Pacific is witnessing the fastest growth, driven by rapid industrialization, increasing IT investments, and the proliferation of cloud computing across countries like China, India, and Japan. Latin America and the Middle East & Africa are also experiencing steady growth, fueled by digital transformation initiatives and the rising demand for data-driven decision-making in both public and private sectors.

Component Analysis

The data visualization market is segmented by component into software

Visualizing research data can be an important science communication tool. In recent decades, 3D data visualization has emerged as a key tool for engaging public audiences. Such visualizations are often embedded in scientific documentaries screened on giant domes in planetariums or delivered through video streaming services such as Amazon Prime. 3D data visualization has been shown to be an effective way to communicate complex scientific concepts to the public. With its ability to convey information in a scientifically accurate and visually engaging way, cinematic-style 3D data visualization has the potential to benefit millions of viewers by making scientific information more understandable and interesting. Maximizing the effectiveness of 3D data visualization can benefit millions of viewers. To support a wider shift in this professional field towards more evidence-based practice in 3D data visualization to enhance science communication impact, we have conducted a survey experiment comparing audience responses to two versions of 3D data visualizations from a scientific documentary film on the theme of ‘solar superstorms’ (n = 577). This study was conducted using a single (with two levels: labeled and unlabeled), between-subjects, factorial design. It reveals key strengths and weaknesses of communicating science using 3D data visualization. It also shows the limited power of strategically deployed informational labels to affect audience perceptions of the documentary film and its content. The major difference identified between experimental and control groups was that the quality ratings of the documentary film clip were significantly higher for the ‘labeled’ version. Other outcomes showed no statistically significant differences. The limited effects of informational labels point to the idea that other aspects, such as the story structure, voiceover narration and audio-visual content, are more important determinants of outcomes. This study concludes with a discussion of how this new research evidence informs our understanding of ‘what works and why’ with cinematic-style 3D data visualizations for the public.

AI in Data Visualization Market Outlook

According to our latest research, the global AI in Data Visualization market size reached $3.8 billion in 2024, demonstrating robust growth as organizations increasingly leverage artificial intelligence to enhance data-driven decision-making. The market is forecasted to expand at a CAGR of 21.1% from 2025 to 2033, reaching an estimated $26.6 billion by 2033. This exceptional growth is fueled by the rising demand for actionable insights, the proliferation of big data, and the integration of AI technologies to automate and enrich data visualization processes across industries.

A primary growth factor in the AI in Data Visualization market is the exponential increase in data generation from various sources, including IoT devices, social media platforms, and enterprise systems. Organizations face significant challenges in interpreting complex datasets, and AI-powered visualization tools offer a solution by transforming raw data into intuitive, interactive visual formats. These solutions enable businesses to quickly identify trends, patterns, and anomalies, thereby improving operational efficiency and strategic planning. The integration of AI capabilities such as natural language processing, machine learning, and automated analytics further enhances the value proposition, allowing users to generate dynamic visualizations with minimal technical expertise.

Another significant driver is the growing adoption of business intelligence and analytics platforms across diverse sectors such as BFSI, healthcare, retail, and manufacturing. As competition intensifies and consumer expectations evolve, enterprises are prioritizing data-driven decision-making to gain a competitive edge. AI in data visualization solutions empower users at all organizational levels to interact with data in real-time, uncover hidden insights, and make informed decisions rapidly. The shift towards self-service analytics, where non-technical users can generate their own reports and dashboards, is accelerating the uptake of AI-driven visualization tools. This democratization of data access is expected to continue propelling the market forward.

The rapid advancements in cloud computing and the increasing adoption of cloud-based analytics platforms are also contributing to the growth of the AI in Data Visualization market. Cloud deployment offers scalability, flexibility, and cost-effectiveness, enabling organizations to process and visualize vast volumes of data without substantial infrastructure investments. Additionally, cloud-based solutions facilitate seamless integration with other enterprise applications and data sources, supporting real-time analytics and collaboration across geographically dispersed teams. As more organizations transition to hybrid and multi-cloud environments, the demand for AI-powered visualization tools that can operate efficiently in these settings is poised to surge.

From a regional perspective, North America currently dominates the AI in Data Visualization market due to the presence of leading technology providers, high digital adoption rates, and significant investments in AI and analytics. However, the Asia Pacific region is anticipated to witness the fastest growth over the forecast period, driven by rapid digitalization, expanding IT infrastructure, and increasing awareness of the benefits of AI-driven data visualization. Europe is also expected to see substantial adoption, particularly in industries such as finance, healthcare, and manufacturing, where regulatory compliance and data-driven strategies are critical. Meanwhile, emerging markets in Latin America and the Middle East & Africa are gradually embracing these technologies as digital transformation initiatives gain momentum.

Component Analysis

The Component segment of the AI in Data Visualization market is bifurcated into Software and Services, each playing a pivotal role in shaping the industry landscape. Software solutions encompass a wide array of platforms and tools that leverage AI algorithms to automate, enhance, and personalize data visualization. These solutions are designed to cater to varying business needs, from simple dashboard creation to advanced predictive analytics and real-time data exploration. The software segment is witnessing rapid innovation, with vendors continuously integrating new AI capabilities such as natural language queries, automated anomaly detection, and adaptive visualization techniques. This has significantly reduced the learning

03/07/2020 14:00 Room 2 #humelpIn recent years scholars and practitioners have drawn attention to the need for data to be humanised (Lupi 2017, D’Ignazio and Klein 2020, Alamalhodaei et al 2020). In a piece circulated around social media, data visualizer Giorgia Lupi provocatively asked, “Can a data visualization evoke empathy and activate us also at an emotional level, and not only at a cognitive one? Can [it] make you feel part of a story of a human’s life?” (2017).

This workshop explores the emergent area of ‘data comics’, looking at how the fields of graphic medicine and graphic social science integrate quantitative, evidence-based statistics into narratives of human experience in efforts to evoke empathy (Bach et al 2017, Wysocki 2018, McNicol and Wysocki 2019). It then turns to consider the recent rise of data visualisation, and with them data comics, during the COVID-19 pandemic.

From the fear of getting sick to the boredom of working at home, from the struggles of full-time parenting to the threat of economic upheaval, we offer a brief masterclass in how recent data comics on COVID-19 explore the complexities and potential of presenting data in more humanising ways.

Drawing on examples gathered over the past three months, we argue that integrating data and comics can be a powerful tool for public health communications, social justice and advocacy work. The workshop then turns to a hands-on activity asking participants to create their own data comics from a live brief. The session concludes with practical advice for the planning, production and distribution of data comics.

Work cited

Alamalhodaei, A., Alberda, A. P., & Feigenbaum, A. (2020). 21. Humanizing data through ‘data comics’: An introduction to graphic medicine and graphic social science. Data Visualization in Society, 347.

Bach, B., Riche, N. H., Carpendale, S., & Pfister, H. (2017). The emerging genre of data comics. IEEE computer graphics and applications, 37(3), 6-13.

D'Ignazio, C., & Klein, L. F. (2020). Data feminism. MIT Press.

Feigenbaum, A., & Alamalhodaei, A. (2020). The Data Storytelling Workbook. Routledge.

Lupi, G. (2017, January 30). Data Humanism: The Revolutionary Future of Data Visualization. Print Magazine. Retrieved from

http://www.printmag.com/information-design/data-humanism-future-of-data-visualization.

McNicol, S., & Wysocki, L. (2019). Comics in Qualitative Research. In P. Atkinson, S. Delamont, A. Cernat, J.W. Sakshaug, & R.A. Williams (Eds.), SAGE Research Methods Foundations. doi: 10.4135/9781526421036832018

Wysocki, L. (2018). Farting Jellyfish and Synergistic Opportunities: The Story and Evaluation of Newcastle Science Comic. The Comics Grid: Journal of Comics Scholarship, 8.

A visual exploration focused on Moove raised significant capital in the last four years

NYC 311's mission is to provide the public with quick and easy access to all New York City government services and information while offering the best customer service. Each day, NYC311 receives thousands of requests related to several hundred types of non-emergency services, including noise complaints, plumbing issues, and illegally parked cars. These requests are received by NYC311 and forwarded to the relevant agencies such as the police, buildings, or transportation. The agency responds to the request, addresses it, and then closes it.

Context

I created these files and analysis as part of working on a case study for the Google Data Analyst certificate.

Question investigated: Do annual members and casual riders use Cyclistic bikes differently? Why do we want to know?: Knowing bike usage/behavior by rider type will allow the Marketing, Analytics, and Executive team stakeholders to design, assess, and approve appropriate strategies that drive profitability.

Content

I used the script noted below to clean the files and then added some additional steps to create the visualizations to complete my analysis. The additional steps are noted in corresponding R Markdown file for this data set.

Acknowledgements

Files: most recent 1 year of data available, Divvy_Trips_2019_Q2.csv, Divvy_Trips_2019_Q3.csv, Divvy_Trips_2019_Q4.csv, Divvy_Trips_2020_Q1.csv Source: Downloaded from https://divvy-tripdata.s3.amazonaws.com/index.html

Data cleaning script: followed this script to clean and merge files https://docs.google.com/document/d/1gUs7-pu4iCHH3PTtkC1pMvHfmyQGu0hQBG5wvZOzZkA/copy

Note: Combined data set has 3,876,042 rows, so you will likely need to run R analysis on your computer (e.g., R Console) rather than in the cloud (e.g., RStudio Cloud)

Inspiration

This was my first attempt to conduct an analysis in R and create the R Markdown file. As you might guess, it was an eye-opening experience, with both exciting discoveries and aggravating moments.

One thing I have not yet been able to figure out is how to add a legend to the map. I was able to get a legend to appear on a separate (empty) map, but not on the map you will see here.

I am also interested to see what others did with this analysis - what were the findings and insights you found?

This article proposes a new graphical tool, the magnitude-shape (MS) plot, for visualizing both the magnitude and shape outlyingness of multivariate functional data. The proposed tool builds on the recent notion of functional directional outlyingness, which measures the centrality of functional data by simultaneously considering the level and the direction of their deviation from the central region. The MS-plot intuitively presents not only levels but also directions of magnitude outlyingness on the horizontal axis or plane, and demonstrates shape outlyingness on the vertical axis. A dividing curve or surface is provided to separate nonoutlying data from the outliers. Both the simulated data and the practical examples confirm that the MS-plot is superior to existing tools for visualizing centrality and detecting outliers for functional data. Supplementary material for this article is available online.

The global data visualization for large screen software market size was valued at USD 1,313.1 million in 2022 and is projected to reach USD 2,676.0 million by 2030, exhibiting a CAGR of 9.1% during the forecast period. The growth of the market is attributed to the increasing adoption of large-screen displays in various applications, such as command and control centers, surveillance systems, and digital signage. The key drivers of the data visualization for large screen software market include the rising demand for real-time data visualization, the growing adoption of cloud-based solutions, and the increasing popularity of interactive data visualization tools. The market is also witnessing the emergence of new trends such as augmented reality (AR) and virtual reality (VR), which are expected to further fuel the growth of the market in the coming years.