Sep 20, 2011

What's the fastest growing use case for big data analytics?



The fastest growing use case for big data analytics is Visual Data Analytics, basically making it easier to understand what all the captured data means. Many firms grasp the need to make sense of the data, and presenting it visually using tools like Sybase ESP (the Aleri Event Stream Processor) can allow knowledge workers (management, bankers, stock traders, etc.) to better analyze trends, outliers and perhaps even upcoming situations to guard against. There is some good information regarding multiple use scenarios on Sybase's website.



Here's a helpful article on Visual Analytics that expands on your post a bit.

Visual analytics

"Visual analytics is "the science of analytical reasoning facilitated by visual interactive interfaces." [2] It can attack certain problems whose size, complexity, and need for closely coupled human and machine analysis may make them otherwise intractable.[3] Visual analytics advances science and technology developments in analytical reasoning, interaction, data transformations and representations for computation and visualization, analytic reporting, and technology transition. [4] 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.

Visual analytics integrates new computational and theory-based tools with innovative interactive techniques and visual representations to enable human-information discourse. The design of the tools and techniques is based on cognitive, design, and perceptual principles. This science of analytical reasoning provides the reasoning framework upon which one can build both strategic and tactical visual analytics technologies for threat analysis, prevention, and response. Analytical reasoning is central to the analyst’s task of applying human judgments to reach conclusions from a combination of evidence and assumptions.[2]

Visual analytics has some overlapping goals and techniques with information visualization and scientific visualization. There is currently no clear consensus on the boundaries between these fields, but broadly speaking the three areas can be distinguished as follows:

Scientific visualization deals with data that has a natural geometric structure (e.g., MRI data, wind flows).
Information visualization handles abstract data structures such as trees or graphs.

Visual analytics is especially concerned with sensemaking and reasoning.

Visual analytics seeks to marry techniques from information visualization with techniques from computational transformation and analysis of data.

Information visualization forms part of the direct interface between user and machine, amplifying human cognitive capabilities in six basic ways:[2][5]

by increasing cognitive resources, such as by using a visual resource to expand human working memory,
by reducing search, such as by representing a large amount of data in a small space,
by enhancing the recognition of patterns, such as when information is organized in space by its time relationships,
by supporting the easy perceptual inference of relationships that are otherwise more difficult to induce,
by perceptual monitoring of a large number of potential events, and
by providing a manipulable medium that, unlike static diagrams, enables the exploration of a space of parameter values.

These capabilities of information visualization, combined with computational data analysis, can be applied to analytic reasoning to support the sense-making process."
Answer this