The huge volume of structured or unstructured data we produce and consume everyday across different digital interfaces like computers, phones, televisions, billing machines, surveillance monitors can yield huge insights when put under sophisticated data analytics. Here we define, explain and exemplify this new frontier of data analytics called Big Data.
From sharing a comment over internet to text message on your handheld device to audio and video files stored on your disk space to the transaction data in the server of a shopping arena to the live telecast of an event - all are data and thanks to rapidly proliferating handheld devices and internet use all over the world the volume of data is growing in electrifying speed. According to the latest estimates we create nearby 2.5 quintillion bytes of data everyday and it is more than surprising that in the last two years only we created more than 90% of the volume of data in the world. This huge chunk of unclassified and classified data is commonly referred as Big Data. Growth of data in the digital and cyber space is an everyday reality that we cannot do anything with, but the question is why this gigantic volume of data is so important, what this fuss is all about or so to ask, what is the significance of Big Data.
Importance of Big Data
Just think of a situation when as a strategic marketer you have come across a huge volume of consumer transaction records for the particular product segment across your area. Generally it seems huge, complex and futile to search this gigantic volume of data to put your research effort, but on the other hand with sophisticated data analytics this same volume of data can yield a lot of insights into consumer behavior, buying habits and levels of appreciation for various products which for a marketing or business strategist are invaluable raw materials for accurate decision making. So while seemingly unclassified junk of a huge volume of data looks as futile numbers, figures and alphabets without any material output, with data analytics they are transformed into valuable insights and decision making tools. So the significance or importance of Big Data lies more in data analytics than simply in volumes of raw data.
3 Dimensions of Big Data
The processing, use and analytics of Big Data can be summed up in 3 major dimensions, respectively as Volume, Velocity and Variety. While Volume of data is the quintessential aspect with the rapid growth of data every instant, Velocity refers to the speed of real time streaming data for further analysis and finally Variety stands for multiplicity of various types of data for further analysis to classify and draw insights. Let us exemplify these 3 dimensions of Big Data as per their significant in data analytics.
- Volume:Analyzing billions of social networking posts on different topics and in different regions one can draw business insight regarding consumer sensitivity on products, socio-political insights regarding anti-incumbency sentiments and so on or mood disorder that can potentially trigger social unrest in a region. Similarly, analyzing tons of telephone bills and call records paid in a month or year can reveal a lot of insights in the areas of personal finance, lifestyle and health, mood disorder and obviously consumer psychology. Thus accumulating volume under analytical scanner and classifying them in different axis can reveal array of insights from different chunks of data.
- Velocity: For some analytical purposes where one needs to be constantly vigilant and be ready to get into action as soon as the situation arises, time and speed of data is more important than anything. This time sensitive data explains the Velocity. For instance scrutinizing real time surveillance video output inside different airport terminals potential security threats can be detected. Similarly, analyzing tons of real time transaction details in stock market potential frauds can be detected.
- Variety: Variety or amalgamation of different types of data within a big volume offers the analytical scope to come out with insights for different areas or purposes. While any type of data is referred as Big Data, their classification and breaking them into types or sub-types yields lot of signs and insights for array of purposeful uses and decision making. For instance, millions social networking posts or internet chat room messages or text messages on phones can be classified into different areas of interest and focus and can be analyzed to gain further insights in different areas. Click streams across different websites on internet can be categorized into different interest areas and thus can be further analyzed.
Big Data is the most promising frontier of technology geared towards data analytics. While ever increasing, constantly multiplying and rapidly proliferating data volume, real time data speed and variety of data have an overwhelming effect in our life, the real challenge lies for sophisticated data analytics to produce actionable insights and acumen for decision making process in different fields.