Visual Analytics for High Dimensional Data Sets 24 Oct 2017

Visual Analytics for High Dimensional Data Sets

When dealing with data, being able to read and understand it in a relatable way is integral to business success and competition. In the current world of Big Data, we have to process massive amounts of data and find patterns in order to use it for further analysis. It’s crucial, but challenging, to find any type of insights in the data if the data has multiple dimensions.

High Dimensional data is found in numerous industries such as Science, Engineering, Biology, Chemistry, and Medicine, and is characterized by the following:

  • A large quantity of variables of observation in relation to the sample size.
  • Is generally complex and can exist in hundreds or thousands of dimensions.
  • All objects within High Dimensional Data can appear to be dispersed and dissimilar, which causes traditional data analysis strategies to be inefficient. 

Predictive Analytics World Event in London

The Predictive Analytics World Event in London in October of 2017 was a conference discussing the use of deployed predictive analytics and its impact on businesses. Experts from around the world attended to discuss major predictive analytics applications, impacts, and the latest industry trends.

Visual Analytics for High Dimensional data was a leading topic of discussion at the event. During his keynote speech at the London event, Professor of Mathematical Sciences, Alfred Inselberg talked about the importance of visual analytics in High Dimensional data by stating that “with a good data display and interactivity… [data visualization]… defeats this combinatorial explosion by extracting insights from the visual patterns. This is the core reason for data visualization.”

Visual Analytics enables the interpretation of High Dimensional data via interactive and streamlined visualizations, so that insight can be gained from multidimensional data patterns.

High Dimensional Data Visualization for Data-driven Decisions

You have to be able to identify patterns in data so that you can ask the right questions that lead to informed, data-driven decisions. It’s integral to optimize metrics that lead to business results. For example, product recommendation engines push items to maximize a customer’s purchases, or fraud detection algorithms flag transactions to minimize losses.

At the event in London, Head of Elder Research, John Elder, states in his keynote that “as modeling and classification (optimization) algorithms improve over time, one could imagine obtaining a solution merely by defining the guided metric.” If we use the right tools we can optimize metrics that help businesses ask the right questions, and find solutions to businesses queries.

Data Visualization is an ideal approach when handling High Dimensional data sets because it gives analysts the means to understand what the data is revealing and where to target attention.

Because of the expanding dimensions of data sets and physical constraints of the 2D or 3D screen displays, there are certain visualization challenges that can be addressed with Data Visualization. This is a vital tool for uncovering hidden relationships in complex High Dimensional data, and understanding the insights it contains.

Visual Analytics overcomes the challenges in High Dimensional pattern recognition by extracting insights from visual patterns via data visualization. This can lead to actionable applications and the ability to answer critical business questions.

Visualization in Big Data

Data Visualization makes Big Data more valuable, as well. It can act as the guide to facilitate decision-making capabilities and a tool to convey critical information in data analysis.

Google Maps is currently considered by many authorities to be the most versatile, all-inclusive, and successful set of visualizations on Earth. Due to its complexity, Google Maps requires a 400 person team to manage and ensure it functions to the best of its capabilities.

  • It offers comprehensive data sets in multiple forms.
  • It’s constantly updated and easy to use.
  • The display offers a variety of diverse views on the data to accommodate individual needs and questions.
  • It’s also available across numerous devices.
  • It features a powerful API.

We need the right tools to visualize Big Data in interactive ways, much like the Google Maps example. Charts and graphs aren’t adequate enough to offer meaning beyond the traditional one or two dimensions. Big Data visualization tools have to be functional and adaptable in order to properly convey the true depth of Big Data.

Big Data and Visualization is still an evolving process. By utilizing versatile data visualization structures, analysts can leverage data to fit constantly changing needs and questions. Big Data Visualizations vary depending on specific objectives and the questions that need answering; therefore, the visualizations themselves will always be unique.

Conclusion and Upcoming Data Analytics Events

Data Visualization is critical when analyzing High Dimensional data sets because it helps analysts discover complex relationships and derive unique insights. Because of the massive scale and variety of data being generated, it is critical to discuss and develop ways to support better methods of understanding complex data sets, like High Dimensional data.

There is lot more things to be discussed and validated during the upcoming events in the data and analytics world:

  • Premier Machine Learning Conference in New York on October 29th through November 2nd 2017, where you can learn about Machine Learning applications in businesses, healthcare, and finance.
  • Big Data & Analytics Innovation Summit London November 2nd through November 3rd 2017 to discuss how data can be applied to increase competitive advantage.
  • Chief Data Officer Summit in New York December 11-12 and discuss the advantages of promoting a data driven culture within your organization.

– Research Optimus

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