The world has been taken over by the digitalization of data. New techniques on how to process big data are being made in order to gain deeper insight into improving operative and business functions. This brings us to the question more about big data analytics. However, to understand big data analytics it is important to have thorough knowledge of big data, how it is made and how it operates.
Big data analytics operates on large amounts of data sets in the form of clusters. When these clusters are in big data form only then can big data analytics be produced. It was recently observed that big data can be highlighted through three fundamental principles.
1 - Volume: A recent research conducted by the International Data Corp. (IDC) pointed out a massive increase in digital in the next half a decade. The research showed that the digital data will increased from 130 exabytes to 40,000 exabytes by 2020.
A quick look at a few big organizations will give plenty of proof for this theory. Wallmart alone collects more than 2.5 petabytes of transaction data at an hourly rate from its clients. Now imagine this, a petabyte is one quadrillion byte, which means that more than 90% of the data we have today was only created during the past two years. Here we are talked about only one organization. Imagine the benefits of big data analytics once the true potential of this data is harvested.
2 - Velocity: Speed of data is considered to be more important than volume. This means that real-time or almost real time data accessibility helps organizations make quicker decisions and execute operations before competitors. The quicker the data is accessed the quicker big data analytics can be brought into play.
Recent research from MIT Media Lab used location data from mobile phones to track the number of people in the parking lot of Macy on the eve of a Black Friday, which is the beginning of Christmas shopping in the USA. This idea allowed the researchers to calculate an estimated amount of the sales made by the retail company even before Macy had the sales registered on their server. This in essence is a truly remarkable benefit of big data analytics.
3 - Variety: The big data for big data analytics comes in many forms such as images, emails, text messages, GPS signals, tweets, social media updates to any other digitized data form. These forms are categorized as unstructured data. It should also be noted that structured databases which store corporate information are generally not suited for storing and processing big data.
Meanwhile, problems such as storage, memory, processing capabilities and bandwidth are becoming cheaper and better making it more economical for organizations to fully utilize the potential of big data analytics.
Big data analytics is entirely dependent upon the type of big data, which can be described in terms of volume, velocity and variety. This technique has single handedly revolutionized the business sector.