Big Data is a computing term that refers to extremely large data sets that may be analyzed computationally to reveal patterns, trends, and associations, especially relating to human behavior and interactions. Big data can be hard to compute as its volume can be too great for traditional data processing software to deal with. The term ‘big data’ is fairly new and it is related to the act of storing large amounts of information for analysis. In early 2000, an industry analyst, Douglas Laney, defined the three properties or dimensions of big data.
Volume: Typically the attribute that refers to the word “big” in big data; the volume of data that is collected to form big data sets is quite large. When you collect data from a variety of sources and transactions, storing this data becomes a major problem. While Petabyte data sets are common these days, Exabyte data sets are not too far away. Technologies like Hadoop have helped in solving some of these problems of storing big data.
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Velocity: Another property of big data is velocity or the speed at which data is collected and processed. Initially, companies would have incoming data rates that would be slower than the processing rate, and if the result was useful, this process of batch processing was considered a success. Now, however, with social media platforms, the data is streaming in continuously and must be processed in real-time for it to be considered useful.
Variety: Data is going to come in all kinds of formats. It could be structured (for example: data structure, excel tables, GPS data) or it could be in a completely new format. The point is, it’s difficult to control the format of big data due to the immense variety of data sets.
Douglas Laney in his article titled 3D Data Management: Controlling Data Volume, Velocity and Variety, said that “Current business conditions and mediums are pushing traditional data management principles to their limits, giving rise to novel, more formalized approaches.” He predicted the way IT companies would be handling big data to accomplish tasks like:
- Avoiding failures, issues and defects in near real time
- Predicting customer buying habits
- Recalculating entire risk portfolios within minutes
At BlueOshan, we are more interested in seeing how we can leverage big data for digital marketing purposes. Today’s digital marketers have the tools to analyze big data and use it in order to target their customers better. Once you know the preferences of your customers, you can customize your approach with them, ensuring multiple success stories. It’s no wonder that there are so many successful data-driven marketing campaigns. We hope to dive into more detail in future blogs on how to leverage big data for digital marketing.
Topics: Big Data