"Visual analytics" is yet another sexy new name for (part of) statistics. In this case: for interactive graphical exploratory data analysis. If you work in some business you won't get support from your boss if you propose to do some statistical research. However if you say that you are going to do some visual analytics your boss will be very supportive.

Hi

According to ""Thomas, J., Cook, K.: Illuminating the Path: Research and Development Agenda for Visual Analytics. IEEE-Press (2005)"" Visual Analytics is the science of analytical reasoning supported by interactive visual interfaces. Today, data is produced at an incredible rate and the ability to collect and store the data is increasing at a faster rate than the ability to analyze it. Over the last decades, a large number of automatic data analysis methods have been developed. However, the complex nature of many problems makes it indispensable to include human intelligence at an early stage in the data analysis process. Visual Analytics methods allow decision makers to combine their human flexibility, creativity, and background knowledge with the enormous storage and processing capacities of today’s computers to gain insight into complex problems. Using advanced visual interfaces, humans may directly interact with the data analysis capabilities of today’s computer, allowing them to make well-informed decisions in complex situations.

for more detail please visit ""http://www.visual-analytics.eu/""

"Visual analytics" is yet another sexy new name for (part of) statistics. In this case: for interactive graphical exploratory data analysis. If you work in some business you won't get support from your boss if you propose to do some statistical research. However if you say that you are going to do some visual analytics your boss will be very supportive.

A delightfully cynical and totally accurate answer from Richard

Dear Vyacheslav

Visual Analytics is a body of knowledge that allows us to use techniques with interactive visualization algorithms and methods of data analysis in order to support the analytical reasoning for decision making.

It is used in diverse areas such as science, engineering, business and government. Areas of application :

-Analysis of Intelligence for National Security,

- Business Intelligence to Support the Understanding of Commercial Contexts,

- Viewing Alerts for Emergencies and Natural Disasters,

- Health Monitoring for Epidemic Control,

- Analysis and Visualization for Planning in Urban Systems , among others.

Visual analytic close union between man and machine

As a research agenda, visual analytics brings together several scientific and technical communities from computer science, information,visualization, cognitive and perceptual sciences, interactive design, graphic design, and social sciences.

see, http://en.wikipedia.org/wiki/Visual_analytics.

With the advance of Information society, new technological tools are available, which they cover "soft sciences too". It is expected then that the Statistical community is taken by storm! We have for example:

1. Data mining

2. Visual analytics

3. Qualitative Methods of social sciences, that it is a kind of anti-positivism, uses even Husserl's Phenomenology, to describe experience, using , interviews, films, etc.

see, http://en.wikipedia.org/wiki/Qualitative_research.

All these techniques have been developed outside "Statistical Departments" In summary the advanced of information society brought new ideas and techniques, which the Statistical community will absorb slowly.

It is indicative of this the following paper by the Master of Statistics, C. R. Rao:

Dear Costas Drossos.

Thank you for your insightful and helpful answer.

Vyacheslav

I agree with Richard and I have observed that this habit (the renaming of old practises) is an evolving trend in modern times... Sometimes I really wonder why it is necessary to rename things in order to be easily accepted. Probably science has affected by the virus of 'marketing & promotion', so maybe we have to hire advertisers to promote our work efficiently.

@Vyacheslav, Dr. Pat Hanrahan gave a 7 fine characteristics of visual analytics:

"What you’ll learn about visual analytics:

How visual analysis software supports the cycle of visual analysis

Why visual analysis helps humans process data and information more rapidly

The importance of multi-dimensional visual expressiveness

How automatic data visualization significantly reduces work time and helps people think visually

Why being able to shift perspectives on data is so critical to understanding

How linking perspectives across dashboards and views makes analysis faster and more effective

Why data visualization naturally extends collaboration across your organization"

All of mentioned is in his white paper "Selecting a Visual Analytics Application"

These are all copied from the link that I can not attach because it is commercial and RG will remove it! Hope it will be helpful. Regards!

Thank Ljubomir. This is a good clue.

In the midst of the big data, open data and transparency revolutions, ideas around data consumption and usability are those that should be (but often are not) discussed as part of these modernizations. In health care, information usability is perhaps most critical; the fact that we’re making decisions regarding human life is in itself a reason to better use our data. Not to mention the exorbitant health care spending in the US. However, usability of data has traditionally been less advanced in health care compared to other fields. The good news? Visual analytics is changing that.

Visual analytics is an integrated approach that combines visualization, human factors and data analysis (see Visual analytics: Scope and challenges). The goal is to allow users to draw conclusions from data by representing information through human-centered, intuitive visualizations. It’s much more than what meets the eye, though. Behind the scenes, it’s the work of advanced analytics that prepare and organize massive amounts of data so that users can make sense of hundreds of thousands of variables. It’s what makes visual interaction with big data possible so that users can pose known questions to the data, and also explore the data for the unknown.http://blogs.sas.com/content/hls/2014/02/06/data-meets-design-how-visual-analytics-is-transforming-health-care/

Just another paper that (tries to) defines Visual Analytics (http://hal-lirmm.ccsd.cnrs.fr/docs/00/27/27/79/PDF/VAChapter_final.pdf):

“Visual analytics combines automated analysis techniques with interactive visualizations for an effective understanding, reasoning and decision making on the basis of very large and complex data sets.”

In this paper it was also tried (unfortunately it just seems like a try) to distinguish the terms Visual Analytics (VA) and Information Visualization (InfoVis):

“Visual analytics is more than just visualization. It can rather be seen as an integral approach to decision-making, combining visualization, human factors and data analysis. The challenge is to identify the best automated algorithm for the analysis task at hand, identify its limits which cannot be further automated, and then develop a tightly integrated solution with adequately integrates the best automated analysis algorithms with appropriate visualization and interaction techniques.”

It's incomprehensible why the authors align these characteristics only with VA, because information visualization already covers the mentioned aspects decision-making, combining visualization, human factors and data analysis for a much longer time (see therfore one of the most established InfoVis book “Readings in Information Visualization. Using Vision to Think.: Using Vision to Think” by Card, Mackinlay and Shneiderman from 1999). Overall it seems that Visual Analytics is just another bullet term to define an already existing research topic under a new name. In my opinion VA just try to combine Information Visualization with Data Mining, but I absolutely don’t understand why this will be done under a new fictive research topic.

Thank Lala Behari Sukla and Dirk Burkhardt. This is a good clue.

@Vyacheslav, something more on visual analytics! Good reading!

http://www.visual-analytics.eu/faq/

@Ljubomir Jacić: I hope you can help me to understand some issues regarding this graphic, because I thought about it a long time, but most of these parts in the graphic seeming not logic.

The first is the feedback loop. At visualization there is part “User Interaction”, but what is the difference to the feedback loop? Normally, both things describing the same: the interaction of a user with the (technical) system. In contrast to this Card, McKinlay and Shneiderman defined a more flexible feedback loop, where the user interaction could influence each phase of visualization generation. Why was this great idea not been considered/overtaken in that model too? In this form it is more confusing than helpful.

The second point is the linking of visualization to model (and vice versa). This is totally unspecific and provides no help in integrating this model into a technical system. Why it was not made similar practical as the Card, McKinlay and Shneiderman model? Sure, in each system will be a model that will be used to visualize, there is nothing new or innovative. But in fact, on this generic level it don’t provides any benefits.

The forth aspect is the Transformation arrow on Data. Data is something fixed given. How can I transform for instance an XML document on a server? In each technical system, the transformation is done from the data TO the internal data model, or directly ON the internal data model. To do a transformation on the data itself makes no sense to me?!

And the last point is the Knowledge. Knowledge is not directly an outcome of interaction with a technical/visualization system. Knowledge is generated in the mind of each user, the visualization of data can just provide information, which the human brain can use to generate knowledge (in best case, this not the default case). It makes no sense to draw a data processing pipeline, which really defines the generation of knowledge as a must.

@Dirk, thanks for long comment. I do agree with You about comment on feedback. But, this is only rough scheme of the visual analytics process! Yes, "...more flexible feedback loop, where the user interaction could influence each phase of visualization generation."

My intention was to point to a new book that is available in this field of visual analytics:VISMASTER that can be downloaded. Here is the link, 25 MB!

The scheme that You comment is in second chapter of this book!

Another, please send some other link since the link from your first comment is not operative. Thanks.

http://www.visual-analytics.eu/book/aboutbook/

Thank Lubomir. I really appreciate your help.

i shall try to cover this complicated term in simple words

1. it is an analytic tool of data analysis via data visualization

2. Data visualization is an analytic reasoning supported by interactive visual interfaces

3. Visual interfaces is a Process that combines automatic and visual analysis methods

In short turning numbers or digits data in to graphical or visual data for ease of analysis

Simply put, visual analytics involves turning numbers into pictures that anyone in an organisation can access, understand and interact with to harvest richer insight from vast data sources.

@Richard David Gill.Well said. I agree with your smart arguments, perfect.

It is not just another "sexy name" of a part of statistics wherein data are shown in graphs and diagram. To the best of my knowledge, hard sciences as physics also use this visual analysis to compare and contrast theoretical and experimental curves etc. Its use is universal across all sciences, therefore, there is a term, "Scientific Visualisation" as distinct from "Cartographic" or "Geo-Visualisation".

I think it is better to call it information visualization i.e. visual representation of information.

Perhaps what is new is the computational power/graphical processing, applied to otherwise meaningless data.A kind of visual big data mining.Both in the sense of pattern extraction and generative/projective capabilities. Enter another terminology I stumbled upon computational visualistics.

http://www.ted.com/talks/david_mccandless_the_beauty_of_data_visualization?language=en

http://jasie.de/studium.php

Article Challenging problems of geospatial visual analytics

Certainly there are many who use  visual analytics for marketing buzz, but it is actually  a defined term. It is "the science of analytical reasoning facilitated by interactive visual interfaces" "illuminating the path: an R&D agenda in visual analytics" (electronic version at  http://vis.pnnl.gov/pdf/RD_Agenda_VisualAnalytics.pdf ) that is the subject of Visual Analytics Science and Technology (VAST) a large IEEE conference. 

Visual analytics is an outgrowth of the fields of information visualization and scientific visualization that focuses on analytical reasoning facilitated by interactive visual interfaces.

Dear @Vyacheslav, the recent information about your issue! 

Visual Analytics: Make Smarter Decisions Faster!

The Rise of Cloud-Based Self-Service Visual Analytics

"The ever-increasing interest in analytics is intersecting with technological progress toward easier-to-use yet more-advanced data visualization in tools and applications available in the cloud—making cloud-based self-service visual analytics a major trend..."

http://www.oracle.com/us/dm/visualanalytics-2659271.html?elq_mid=25476&sh=151762512141891726241824150224223&cmid=NAMK15048361MPP058C002

Visual analytics is the science of analytical reasoning facilitated by visual interactive interfaces that focuses on analytical reasoning facilitated by interactive visual interfaces (source: Mapping Scientific Frontiers: The Quest for Knowledge Visualization By Chaomei Chen)

Put simply, visual analytics involves turning numbers into pictures that anyone in the organisation can access, understand and interact with to harvest rich insight from vast data sources.

The science comes into its own when humans need to analyse information that is so huge and complex it’s impossible to process. Visual analytics tools and techniques create an interactive view of data that reveals the patterns within it, enabling everyone to become researchers and analysts.

http://www.atheonanalytics.com/visual-analytics/

Visual analytics representations are based on machine learning algorithms. It is possible to get representations using tools, but not interactive visual analytics (VA) visualizations. VA visualizations are typically custom made.  

Dear @Vyacheslav, this webinar might me interesting for your research.

The Power of Predictive Intelligence!

It is free. Just log in. There are many fine resources available.

http://www.techrepublic.com/resource-library/whitepapers/video-demo-the-power-of-predictive-intelligence/?promo=101&tag=nl.e101.em&ttag=e101&s_cid=e101&ftag=&cval=wit2-fluid&noRedirect=

http://www.techrepublic.com/resource-library/topic/big-data/

Dear @Ljubomir.

Thanks for the interesting link.

Another differentiator within the IEEE VIS community is what Illuminating the Path

called "human-information discourse". VA researchers emphasize fluent interaction with information not just visualizing data. It is also the case that visual analytics as a "science of analytical reasoning" need not involve data at all- it can be applied to issue-based information systems as well