PSIORI Visualizer

Mission

Data analysis is the key to improving the performance of complex processes. However, as datasets become ever larger with millions of rows and hundreds or thousands of columns, the analysis becomes exponentially more difficult and time-consuming -- even for skilled analysts. Rapid exploration of such data sets, for example process data from large-scale industrial installations, or genetic sequence data generated in life science and biomedical research, is rapidly approaching a stage where manual inspection using conventional analysis and charting methods is becoming impossible. At PSIORI, we want to enable a broad range of people to regain understanding of their data by visualizing it in an intuitive way.

Product

With PSIORI Visualizer, we reduce the complexity of Big Data sets and let you directly see and interact with all of your data - not only a subset, sample, or approximation, but down to every single datapoint. PSIORI Visualizer works by embedding the high dimensional data with Deep Autoencoders into a three dimensional datapoint cloud and applying color maps based on any of your data’s parameters. You will immediately see patterns, clusters, and discover relationships and exceptions from expectations in your large, high-dimensional datasets. Rotate, zoom, pan, and interactively explore the data and all their values or select data for detailed investigation and export.

Benefit

The industry-leading Machine Learning based approach underlying PSIORI Visualizer rapidly uncovers the information contained in your Big Data sets: draw conclusions and discover important relationships in seconds, while you get to know your data from different perspectives. The tool is easy to learn, provides high computational power in the background and makes data analysis at the same time more efficient and more intuitive to carry out. Now it is not necessary anymore to undergo years of training in Data Science, because the tool for data exploration is right here.


Volker Voß Managing Sales Director
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