More Data ≠ Better Decisions: Wolfram and the Multiparadigm Approach

Collecting more data doesn’t lead to better decisions — asking and answering the right questions does. And the wrong choice of technology or methods can severely limit the scope of your insights. In this half-hour presentation, Jon McLoone of Wolfram Research describes how unified computation with the Wolfram Language, Wolfram Notebooks and other Wolfram technologies provides the ideal system for a question-driven approach to data science.

At Wolfram, we have spent years curating and connecting the vast collection of algorithms, knowledge and AI that now comprises the computational intelligence of products like Mathematica, Wolfram|One and Wolfram|Alpha. But we didn’t accomplish such high-level results through standard data collection and analysis; we instead pioneered a new multiparadigm approach driven by natural curiosity, exploring all kinds of data, using advanced methods from a range of areas and automating as much as possible. Throughout the video, McLoone touches on some key aspects of this approach and the supporting technology, including:

• Wolfram’s unification of different paradigms and systems
• Why most data science is really just counting
• How unified computation helps you find data, add context and change viewpoints
• The role of automation in computation, analysis and deployment
• Multiparadigm interfaces for simplifying exploration

Combining a universal symbolic representation, high-level automation and human readability — as well as broad built-in computation and knowledge — the Wolfram System enables a scalable, start-to-finish workflow for discovering and presenting insights from data. And with a range of deployment options, you can create scalable, reusable applications (like Wolfram|Alpha) that optimize your data science processes. Ultimately, the best insights come from augmenting human curiosity with intelligent computation — and that’s exactly what multiparadigm data science with the Wolfram System achieves.

Making high-level computation more accessible leads to the democratization of data science, giving anyone with questions immediate access to answers. The multiparadigm approach is more than just a new method for data science; creating and sharing high-level tools for interactive exploration make possible a new generation of data science.