Forbes | Big Data and Big Data analytics have become hot topics in recent
years. Unlike traditional methods of cause and effect deduction, Big
Data analytics generate predictions based on such enormous volumes of
data, that only the tools of association and inference are useful for
finding relevance or meaning.
An interesting case study on the use of Big Data analytics was the prediction of a flu pandemic in the United States by Google GOOGL +1.6%. The
Internet giant detected the spread of a flu virus before any medical
organization or national agency based on search results data that showed
people researching flu symptoms and remedies. Google’s findings were
completely aligned with the health authority reports filed after the flu
pandemic occurred.
Big Data analytics enables us to generate reliable analyses, even in the absence of clear links or causes.
So why so long before we could begin to leverage the value of Big
Data? For one, the processing and analysis of large volumes of data
required advanced computing and storage resources not yet available.
New types of database management systems have also needed to be
devised. Traditional databases use data synchronization techniques to
determine causality and, while Big Data analytics do not require the use
of synchronization for the same purpose, it gives rise to other
challenges in the areas of networking, storage, and computational
architecture.
