Data Dimensionality Reduction in the Age of Machine Learning

Machine Learning is all the rage as companies try to make sense of the mountains of data they are collecting. Data is everywhere and proliferating at unprecedented speed. But, more data is not always better. In fact, large amounts of data can not only considerably slow down the system execution but can sometimes even produce worse performances in Data Analytics applications. We have found, through years of formal and informal testing, that data dimensionality reduction -- or the process of reducing the number of attributes under consideration when running analytics -- is useful not only for speeding up algorithm execution but also for improving overall model performance. This doesn't mean minimizing the volume of data being analyzed per se but rather being smarter about how data sets are constructed.