The information age is in full swing, and with it comes an enormous amount of data. Not only does each business transaction generate new data, social media, mobile devices, smart utility meters, and the "Internet of things" contribute to the sheer volume of data being generated at any given moment. From social mentions of a brand name to in-store location data to the number of calories burned -- and beyond -- data is rapidly accumulating. Companies that can tap into that data can understand their markets better, make smarter decisions, and respond to changing market conditions faster (Source: http://www.informationweek.com/big-data/big-data-analytics/10-powerful-facts-about-big-data/d/d-id/1269522).
But what is big data? The term has come to mean data sets that have grown in size and complexity to the point where they can no longer be processed using standard database management tools. The most important characteristics of big data are the "four Vs": velocity, volume, variety, and value.
- Velocity refers to the need for data to be processed quickly.
- Volume refers to the sheer size of the data that needs to be processed as well as its ability to grow.
- Variety refers to big data's diversity. For example, big data can come from multiple sources and in a variety of formats
- Value refers to the potential insights that big data contains.
Thus, simply having a lot of data doesn't necessarily mean it's "big data." If that data doesn't need to be processed quickly, isn't necessarily growing exponentially, doesn't necessarily come from a variety of sources, or doesn't necessarily contain any valuable insights, it's may just be standard data that can be handled using traditional data management tools (Source: http://blogs.cio.com/big-data/18595/what-exactly-big-data).
On the other hand, if three or four of the "Vs" are present, you may need more advanced data management and analytical tools to understand it. According to InetSoft, advances have been made both in data mining software tools and hardware, allowing for more robust handling of big data (Source: http://www.inetsoft.com/business/solutions/talking_about_big_data/). At the same time, when analyzing big data or mining insights from it, you can only see a "snapshot" because "data is always coming into an organization at a constant rate."