Top 10 Capabilities for Exploring Complex Relationships in Data for Scientific Discovery

With all of the discussion about Big Data these days, there is frequest reference to the 3 V's that represent the top big data challenges: Volume, Velocity, and Variety. These 3 V's generally refer to the size of the dataset (Volume), the rate at which data is flowing into (or out of) your systems (Velocity), and the complexity (dimensionality) of the data (Variety). Most practitioners agree that big data volume is indeed huge, but that is not necessarily big data's biggest challenge, at least not in terms of data storage capacities, which are growing rapidly also and keeping pace with data volume. The velocity of big data is also a very big challenge, though primarily for applications and use cases that specifically demand near-real-time analysis and response to dynamic data streams. However, unlike volume and velocity, most will agree that the variety (complexity) of the data is truly big data's biggest mega-challenge at all scales and in most applications.