Visual Analytics Definition
Visual analytics is a multidisciplinary field in which interactive visual interfaces are used to support analytical reasoning.
What is Visual Analytics?
Visual analytics is essentially the marriage of data analytics and visualizations. This approach to solving problems is concerned with integrating interactive visual representations with underlying analytical processes to effectively facilitate high-level, complex activities, such as reasoning and data-driven decision making. Visual analytics falls under the category of visual business intelligence and visual business analytics, employing practices such as data mining and statistical work to visualize information in a format that is easy for humans to understand.
Data analytics visualization specifically focuses on analytical reasoning techniques that enable users to gain greater insights that will directly support decision making and planning; visual representations and interaction techniques that exploit the human eye’s perceptual processes; data representations and transformations that format data to support visualization and analytics; and techniques that support the presentation and distribution of analysis results in the appropriate context.
Information visualization, scientific visualization, computational transformation, and analytical and visualization tools work together to create an interface that amplifies human cognitive capabilities. An effective visual analytics interface should provide interactive data visualizations, which promote exploration; use a visual resource to increase cognitive resources and expand human memory; reduce search with perceptual monitoring of large amounts of data in a small space; organize information to enhance pattern recognition; and support the easy perceptual inference of relationships between data points.
Essential visual analytics tools include dashboards, data visualization tools, data source integration software, and collaboration tools.
Visual Analytics for Big Data
Visual analytics is particularly useful in business analytics applications that involve large amounts of complex data sets and analytical processes that require a great deal of interaction and monitoring. Increasing demand for the integration of visual analytics software is driven by the generation of more and more data of high volume, complexity, and velocity.
Once restricted to science and government, big data issues now permeate most areas of human endeavor, which makes the marriage of business analytics and data visualization all the more critical and challenging. Big data analytics visualization tools help transform cryptic, tedious big data into a visually colorful, interactive data visualization from which users can track trends, patterns, and anomalies, and make better, data-driven decisions.
In order to successfully analyze and understand a single big data problem, visual analytics systems are often used in tandem with multiple analysis approaches, such as machine learning algorithms and intelligence value estimation algorithms. Some effective big data analytics visualization strategies and approaches include good semantic mapping, abstraction, aggregation, incremental approximate database queries, and the transformation of data into a functional or procedural model.
The visual analytics process typically follows the same steps: data transformation, data mapping, contribution selecting, ranking, interaction, model visualization, and knowledge processing. Choosing the best visual analytics software to accomplish this depends on the needs of the visual analytics project. In reviewing visualization tools for data analytics, consider licensing, scalability, universal data access, security, compatibility with Microsoft suites, sharing capabilities, user training, IT support needs, and capability to develop efficient SQL for DBMS.
Difference Between Data Visualization and Data Analytics
Data visualization comprises the tools and techniques used to present data as a graphic or image to make it easier to identify patterns and understand difficult concepts. Data visualization is one of the steps of the data science process, preceded by collection, processing, and modeling of data. It is a central component to advanced data analytics.
Data analytics is a broad umbrella term that refers to the science of analyzing raw data in order to transform that data into useful information from which trends and metrics can be revealed. Data scientists, analysts, and engineers work together to collect, integrate, and prepare data for the development, testing, and revision of analytical models.
While both data analytics and data visualization help make sense of the data, a data visualization is only as good as the data that it is fed. Data analytics goes a step deeper, deriving insights and identifying meaningful correlations with the application of algorithmic or mechanical processes.
What is Data Visualization in Business Analytics?
Business Analytics is the process by which businesses use statistical methods and technologies for analyzing historical data in order to gain new insight and improve strategic decision-making. Data visualization is a core component in a typical business analytics dashboard, providing visual representations such as charts and graphs for easy and quick data analysis.
Visualizing data aids in finding correlations between business operations and long-term outcomes. Visual analytics applications must, like any business intelligence or business analytics initiative, adopt an effective data management strategy in order to integrate and standardize data from disparate source systems.
Benefits of Visual Analytics
Businesses are implementing data analytics and visualization tools with increasing frequency in order to speed up their business performance and improve their business decisions making process. Some key benefits of visualization in data analytics include:
- Improved data exploration and data analysis, and minimized overall cost
- Faster and better understanding of data for faster, better decision-making
- Consumption of greater volumes of data in less time, which improves operational efficiency
- Early detection of otherwise overlooked trends, outliers, and correlations between data sets, which may result in a competitive edge
- Instant feedback and real time updates, which keep data current and accurate
Does OmniSci Offer a Visual Analytics Solution?
OmniSci Immerse works seamlessly with the server-side power of OmniSci to enable analysts and data scientists to easily visualize and instantly interact with massive data sets. Immerse reduces the time to insights and dramatically expands data analysts’ ability to find previously hidden insights with a powerful combination of features, resulting in a truly immersive, unbounded visual analytics experience.