Big data is a popular terms used to describe exponential growth and availability of data, both structured and unstructured. And big data may be as important to business - and society - as the internet has become. Why? More data may lead to more accurate analysis.
More accurate analysis leads to more confident decision making. And better decisions can mean greater operational efficiencies, cost reductions and reduced risk.
The three Vs of big data are:
Volume: Volume denotes the data volume. There are many sources for data, transaction - based data stored for many years, unstructured data from social media networks, data collected from sensors. Recently, the data storage costs have been reduced and storing large amount of data can be done now with less cost. Even though decreasing the storage cost, the other issue emerging is how to make out meaningful information from these data.
Velocity :data is streaming in an unprecedented speed and must be dealt with in timely manner. RFID tags, sensors and smart metering are driving the need to deal with torrents of data in near real time. Reacting quickly enough to deal with the data velocity is a challenge for many organisations.
Variety: Data today comes in all types of formats. Structured numeric data in traditional databases. Info created from line of business apps. Unstructured text documents such as email video, audio, stock ticker data and financial transactions. Managing, merging and governing different varieties of data is something many organisations still grapple with.
What is matters with the Big Data?
The real issue is not about collecting the data, it is about how to interpret the data. For instance: by combining big data and high powered analytics, it is possible to
1. Determine root causes of failures, issues and defects in near-real time, potentially saving millions of dollars annually.
2. Optimise routes for many thousands of package delivery vehicles while they are on the road.
3. Analyse millions of SKUs to determine prices that maximise profit and clear inventory.
4. Generate retain coupons at the point of sale based on customers current and past purchases.
5. Send tailored recommendations to mobile devices while customers are in the right area to take advantage of offers.
6. Recalculate entire risk portfolios in minutes
7. Quickly identify customers who matter the most
8. Use clickstream and data mining to detect the fraudulent behaviour.
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