The Platform for data-driven business value
Data discovery, visualization and advanced analytics
Discover and extract value in big data using
experiment-driven development on a cluster.
The nD cluster provides a natural math language that makes it easy to create a full range of data-driven visualizations and analytics — from creating a simple chart or adding a calculated column to building a 3D visualization or a machine learning model. Quickly and naturally move from understanding what is happening and why to predicting future outcomes and prescribing action.
As analysts, scientists and engineers explore, interact and play with data, every asset they create captures invaluable domain expertise. All assets can be shared, versioned and released instantly as building blocks for future work. Achieve analytic agility as you improve productivity and operationalize how you deliver analytics.
Get curious about your data. Play with it. That’s where innovation comes from.
Powerful tools to transform how you create, share and deliver analytics.
Scientists, engineers and analysts iterate, innovate and collaborate on one interactive platform.
Create interactive data-driven
representations of big data.
2D & 3D
Create stunning representations of big data using 2D and 3D shapes.
Tell captivating stories with data
using motion and time.
Access dozens of web design widgets to go beyond dashboards and storyboards.
Use profiling to jump start data discovery with automatic visualizations.
Solve problems using sophisticated
Describe and understand data using proven statistical methods.
Analyze financial data and build trading signals with backtesting.
Layer any data-driven visualization on interactive maps, including 3D.
Identify patterns within the dynamics of time series data.
Embrace big data and business complexity
to deliver innovation.
Clustering & dimensionality reduction
Find patterns and explore correlations in big data.
Leverage a committee of models to improve predictive accuracy.
Neural networks & deep learning
Automatically discover the relationships in data. Disentangle the complexity.
Use pipelines for efficient and cost-effective support of complex calculations.