Big Data seems to be quite the buzzword these days. So, what is Big Data exactly?
Basically, it refers to a collection of data sets or information too large and complex to be processed by standard tools. It is about the art and science of combining enterprise data, social data and machine data to derive new insights, which are otherwise not possible. It is also about combining past data with real-time data to predict outcomes.
Big Data is often associated with large volumes of data when viewed from a popular science perspective. For a technologist, it adds the dimensions of velocity and variety in data streams. We are familiar with traditional data analytics where the data is largely transactional, structured and commonly used to predict future trends based on past data. The rise of social data volumes is driving consumer choice, both online and offline. This implies the analysis of high volume and a variety of data streams in real time to expand consumer choices based on their current context. The purpose is to assist in decision support system (DSS), both at the consumer level and enterprise level. The next trend is to use a lot of machine-generated data to combine it with enterprise data to drive an operations support system (OSS) for process automation.
Big Data is giving rise to an interesting collaboration among diverse disciplines of computer science, communication networks and devices, and behavioural science. The advent of data science as a mainstream subject is the outcome of these cross-domain efforts.
Applying Big Data solutions, enterprises can now translate mountains of digital data into effective business insights in real time. They can avoid risks, cut costs, analyse patterns to follow trends and customers’ preferences and suggest better choices for the customers and increase revenue. [Read More]