Cognitive Analytics Answers the Question: What's Interesting in Your Data?

Dimensionality reduction is a critical component of any solution dealing with massive data collections. Being able to sift through a mountain of data efficiently in order to find the key descriptive, predictive and explanatory features of the collection is a fundamental required capability for coping with the avalanche of data that all organizations are collecting. Identifying the most interesting dimensions of data is especially critical when integrating high-dimensional (high-variety) complex data sources, then attempting to visualize the most insightful patterns in the data, and then telling the data's story to your stakeholders. There is a "good news, bad news" angle here. First, the bad news: the human capacity for visualizing multiple dimensions is very limited: 3 or 4 dimensions are manageable; 5 or 6 dimensions are possible (e.g., with colors and symbols); but more dimensions are difficult-to-impossible to assimilate.