Hyperspeed EDA: The Next Data Analytics Frontier Posted on : Aug 04 - 2021

Human beings are predisposed to visual cues. Shapes, dimensions, colors, lines, points and angles deliver instant meaning. For anyone who needs to explore huge amounts of numerical data, visual cues can effectively redefine metadata by enabling analysts to assign meaning to thousands of rows and columns of data in mere seconds.

This capability can be of immense value in the age of big data. Most repositories of information today are simply too large, complex and diverse to explore by rote numerical analysis. These collections often expand to multiple terabytes in size — and are often combined ad hoc from multiple sources.

Moreover, much of the world's data is now location-based. This means analysts are faced with rapidly increasing volumes of geospatial data, and when datasets include changes over time (a class known as spatial-temporal data), challenges pile up quickly.

The ability to characterize and quickly gain perspective on raw data is an essential step when analysts are faced with billions of mapped points (e.g., in disaster response). The problem, until now, has been how to achieve visual analysis of data instantly and at scale.

EDA At Scale

Exploratory data analysis (EDA), the primary means of data exploration, will need to be transformed for a new era in big data analytics.

EDA isn't new, but the need for EDA at a massive scale certainly is. EDA is an initial step in data analytics in which statistical techniques are used to describe dataset characterizations in order to better understand their nature and perhaps to generate initial hypotheses.

EDA allows users to understand and develop a comprehensive view of a particular set of data before further analysis. In effect, EDA enables specialists to kick the tires on billions of data points and gain initial impressions before diving deep into analysis.

Many data analysts try to perform EDA on big data through spreadsheets using manual operations. However, identifying correlations using spreadsheet software involves hands-on filtering — which is also a time-consuming process. There's a better way to adapt EDA to the big data landscape — through visualization.

The New Explorers

A new generation of visualization tools enables data professionals to interact with raw datasets, giving them increased visibility into the patterns and relationships within the data. This is accomplished using the parallel processing capabilities of computers to run high-performance analytics software. Such capabilities deliver super-fast communications between processing elements. View More