Tableau Consultant
What is Visual Analytics?
Visual Analytics
Visual Analytics may be the science of analytical reasoning sustained by interactive visual interfaces. Today, info is produced in an incredible rate and also the ability to collect and store the data is increasing faster compared to capacity to analyze it. Throughout the last decades, a lot of automatic data analysis methods are already developed. However, the complex nature of numerous problems helps it be indispensable to include human intelligence at an early on within the data analysis process. Visual Analytics methods allow decision makers to blend their human ?exibility, creativity, and background knowledge together with the enormous storage and processing capacities of today�s computers to get understanding of complex problems. Using advanced visual interfaces, humans may directly talk with the data analysis capabilities of today�s computer, enabling them to make well-informed decisions in complex situations.- Tableau Consultants
Related Research Areas
Visual Analytics can be viewed as a possible integral approach combining visualization, human factors, and data analysis. The figure illustrates the study areas associated with Visual Analytics. Besides visualization and data analysis, especially human factors, including the aspects of cognition and perception, play a huge role from the communication relating to the human as well as the computer, along with the decision-making process. When it comes to visualization, Visual Analytics pertains to areas of data Visualization and Computer Graphics, sufficient reason for respect to data analysis, it pro?ts from methodologies created in the ?elds of info retrieval, data management & knowledge representation in addition to
data mining.
The Visual Analytics Process
The Visual Analytics Process combines automatic and visual analysis methods with a tight coupling through human interaction in order to gain knowledge from data. The figure shows an abstract breakdown of the several stages (represented through ovals) and their transitions (arrows) from the Visual Analytics Process.
In lots of application scenarios, heterogeneous data sources have to be integrated before visual or automatic analysis methods is true. Therefore, the ?rst step is frequently to preprocess and transform the information to derive different representations for further exploration (as indicated by the Transformation arrow in the figure). Other typical preprocessing tasks include data cleaning, normalization, grouping, or integration of heterogeneous data sources.
Following your transformation, the analyst may choose from applying visual or automatic analysis methods. If an automated analysis is used ?rst, data mining methods are placed on generate types of the initial data. Once a model is produced the analyst has to evaluate and refine the models, which may best be done by getting together with the data. Visualizations enable the analysts to interact with all the automatic methods by modifying parameters or selecting other analysis algorithms. Model visualization may then be employed to measure the findings in the generated models. Alternating between visual and automatic methods is characteristic for your Visual Analytics process and results in a continuous refinement and verification of preliminary results. Misleading brings about an intermediate step can thus be found with an early stage, resulting in better results and a higher confidence. If your visual data exploration is completed first, the consumer has got to what is generated hypotheses by a mechanical analysis. User interaction with all the visualization is necessary to reveal insightful information, for instance by zooming in on several data areas or by considering different visual opinion of the data. Findings in the visualizations may be used to steer model building within the automatic analysis. In summary, within the Visual Analytics Process knowledge can be gained from visualization, automatic analysis, and also the preceding interactions between visualizations, models, along with the human analysts.- Tableau Consultants
Perceptive Analytics specializes in creating custom data visualizations. Conveying meaning in data quickly is the focal point of analytics. Visual analytics helps you discover new relationships in data, prompts you to ask new questions, and helps you convey what you see to others. Join us for this webinar to learn how to unlock the potential of your data using data visualizations.