What is Big Data and why it so critical today?

“Big data” is the information whose scale, distribution, range, and/or timeliness require the use of latest technical architectures and analytics to allow insights that unlock new assets of enterprise value.
Major Software giants and other MNCs are deriving commercial enterprise benefit from analyzing ever expanding and extra complicated information sets that increasingly require real-time or close to-actual time capabilities.

There are many traits of big data, but majorly three stand out as defining Characteristics:
High magnitude of data  (for instance, tools that can process billions of rows and columns)
Intricacy of data types and structures, with an increasing volume of unstructured data (80-90% of the data in existence is unstructured)….part of the Digital Shadow or “Data Exhaust”
Momentum of new data creation
In addition, the data, due to its size or level of structure, cannot be efficiently analyzed using only traditional databases or methods.
There are numerous cases of rising big data opportunities and solutions. Here are a couple: Netflix recommending your next film rental, dynamic checking of installed sensors in architectural structures to recognize continuous burden spots and longer-term disintegration, and retailers examining computerized video streams to enhance item sales and show formats and highlighted spaces on a store-by-store premise are a couple of genuine cases of how big data is associated with our lives today.
These sorts of big data issues require new tools and innovations to store, oversee and understand the business advantage. The new designs it requires are bolstered by new tools, procedures and methodology that empower business associations to create, control and deal with these expansive data collections and the storage environments that house them
Why Big Data will be the most blasting segment for forthcoming years?
I. Data Magnitude
• 44x increase from 2010 to 2020 talking numbers (1.2zettabytes to 35.2zb)
II. Processing Intricacy
• Evolving  data structures
• Use cases requiring additional transformations, new data models and analytical techniques
III. Data Structure
• Increased variety of data structures to mine and analyze

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