Scientific Computing Blog: Accessibility of Data and Application of Algorithms to Provide Insights in Predictive Analytics

Accessibility of Data and Application of Algorithms to Provide Insights in Predictive Analytics

Michael Boruta, Optical Spectroscopy Product Manager (ACD/Labs)

Published online at Scientific Computing Blog, December 2014.

Michael Boruta discusses the accessibility of data and the application of algorithms to provide insights

Although there are a diverse range of applications for predictive analytics in R&D, two common basic requirements are data and insight. Data may be generated by running experiments/analyses, or re-applied from previous work when available. Insights come from application of knowledge both explicit (read/formally accumulated) and tacit (accumulated over time/experience).

The traditional accumulation of knowledge involved mentoring from an experienced colleague whose years working in their field and skill at pattern recognition enabled them to pick components out of a complex mixture spectrum with a quick glance. Today, laboratories want more immediate access to this tacit knowledge by wider audiences. Algorithms that aid data analysis can help to deliver insight along with a searchable knowledgebase of contextual data. Since prediction algorithms are only as good as the data behind them, it is important they are able to be trained to be adaptable to future needs.