Complete data exploration

Unlike other methods of data analysis, AnVil's technology approach creates global data and metastatistical overviews of your complete data set-no matter how large. In this way, major data patterns are revealed as well as aberrant samples that may cause bias. And it's only after full data exploration that the data is dimensionally reduced for finer discrimination.

The problem with traditional data analysis

Traditional analysis and visualization methods are unable to work with complex data sets due to the limitations of existing software. The standard approach gathers massive datasets and reduces them prior to clustering to find useful relationships. But this essentially discards much of the data, leading to a lack of or incorrect conclusions.

AnVil turns your data into decision-quality information

To guide the data exploration process, AnVil develops a discovery strategy for your complex, high-dimensional data set.

Using traditional statistical methods, we start by assessing the quality of the data. For example, are the data uniformly spread or clustered? Widely spaced or concentrated? Are there holes or voids in the data? Are there significant outliers?

Next, innovative visualization techniques are employed. AnVil enables you to see and interpret multiple data sets in integrated, information-rich views. And with RadViz™ (patent pending) visualization tools, you can see hundreds of data points, each having thousands of attributes or descriptors.

A smart clustering algorithm then positions the data in a statistically rational manner. This leads to predictor development using analytical and visual methods. And finally, multiple analytical methods are used to score the efficiency of different classifiers.

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