Whether in human or machine intelligence, one can think of two main categories of solution to problems. The first, is the kind of problem that has a deterministic, rule-based solution. The second, is a problem that a decision, or outcome, cannot be derived by a mathematical formula or a correlation of factors that are both fairly constant in volume and with equal weight to each other. How are those problems solved? By data. lots, and lots of data. While how we go from X to Y (whether Y is a category, a yes/no answer or a prediction) may not be known, we have sufficient sets of (X,Y) to feel confident that we can apply different models and decide which fits the data-set the closest, with the least amount of error or uncertainty.

Current technology, both in hardware processing and in software solutions, has allowed us to design systems that can store and analyze such datasets, in a manner much more economic and scalable than before. Big Data are anything that encompasses those datasets. The data itself, the technology and software solutions to store them in a manner that is efficient at scale, the procedures to unify different data sources and generalize or prepare the data for decision making, the intuition of the Data Scientists that understand the nature of the data, and the choice of tools to be used for a certain application, are all parts of the Big Data revolution.

It would be interesting to discuss with comments from your side, what kind of problem you would categorize in which of the two cases (or possibly a different one). Thank you in advance