Big Data — The 4 V’s
Big data analytics is the process of examining large and varied data sets to uncover hidden patterns, unknown correlations, market trends, customer preferences and other useful information that can help organizations make more-informed business decisions. Big data is also defined by four Vs:
Volume. Big data requires processing high volumes of low-density, unstructured data. It is the task of big data to convert such data into valuable information. For some organizations, this might be tens of terabytes, for others it may be hundreds of petabytes.
Velocity. The highest velocity data normally streams directly into memory versus being written to disk. Some Internet of Things (IoT) applications have health and safety implications that require real-time evaluation and action. Other internet-enabled smart products operate in real time or near real time.
Variety. Unstructured and semi-structured data types, such as text, audio, and video require additional processing to both derive meaning and the supporting metadata. Once understood, unstructured data has many of the same requirements as structured data, such as summarization, lineage, audit ability, and privacy.
Value. Data has intrinsic value but it must be discovered. There are a range of quantitative and investigative techniques to derive value from data; from discovering a consumer preference or sentiment, to making a relevant offer by location, or for identifying a piece of equipment that is about to fail.
Why big data is important?
It doesn’t matter how much data you have, but it is important that what you do with the data. The data can be taken from any source but helps to reduce cost, reduce time, new product development & optimized offering and smart decision making. When you combine big data with high-powered analytics, you can accomplish business-related tasks such as:
· Determining root causes of failures, issues and defects in near-real time.
· Generating coupons at the point of sale based on the customer’s buying habits.
· Recalculating entire risk portfolios in minutes.
· Detecting fraudulent behavior before it affects your organization.