The term big data analytics is first utilized to describe increasing data volume in the late 1990s. However, big data analytics actually came into being as a response to the urgent need to respond to this new growth in big data that began in the late 1990s. It was estimated at the time that there would be around six to eight petabytes of data held by end users by the end of 2021.
In the same year, it was estimated that there would be as much as three petabytes of data held by servers by the end of that same year. Today, nearly fifteen years later, this data has grown by well over ten exabytes.
This explosion in size of data has created a situation where big data analytics companies must make use of analytics software in order to extract value from this wealth of information. Common uses for big data analytics are: determining whether current user behavior is enhancing or deteriorating the experience; analyzing real world market trends; identifying customer buying trends; and analyzing location-based services such as finding a restaurant within walking distance of one’s home.
Common examples of these applications are in product marketing wherein a company may want to analyze customer preferences to determine whether they would prefer to purchase a male or female fragrance or how they might travel in order to save money. Mobile app makers have also seen an opportunity to benefit from these trends in technology because it allows them to develop more engaging mobile apps by analyzing user habits, interests, and location.
Big data analytics offers a way for big business processes to leverage the computing power of the cloud in a cost effective manner. Cloud-based business intelligence solutions make it possible to utilize massive amounts of data without the requirement for large investments in data infrastructure. Some of the key features of such services include:
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