Importance of Big data

• Big data helps drive efficiency, quality, and personalize products and  services producing improved levels of customer satisfaction and profit.
• In Big data, so many concepts are associated: basically there were 3 concepts volume, variety, and velocity.
• Driven by specialized analytics systems and software, big data analytics can point the way to various business benefits, including new revenue opportunities, more effective marketing, better customer service, improved operational efficiency and competitive advantages over rivals.
• Companies have searched for decades to make the best use of information to improve their business capabilities.
• Interpretation of Big Data can bring about insights which might not be immediately visible or which would be impossible to find using traditional methods.
• Big data is a term that is used to describe data that is high volume, high velocity, and/or high variety; requires new technologies and techniques to capture, store, and analyze it; and is used to enhance decision making, provide insight and discovery, and support and optimize processes.
• The term big data emphasizes volume or size. Size is a relative term. In the 1960s 20 Megabytes was considered large. Now data is not considered big unless it is several hundred Petabytes. Size is not the only property used to describe big data.
• Image, voice, and audio data can be analyzed for applications such as facial recognition system in security
• Structured data is data whose elements are addressable for effective analysis. It has been organized into a formatted repository that is typically a database.
• Unstructured data is a data that is which is not organized in a pre-defined manner or does not have a pre-defined data model, thus it is not a good fit for a mainstream relational database.
• Semi-structured data is information that does not reside in a relational database but that have some organizational properties that make it easier to analyze.
• It is helpful to recognize that the term analytics is not used consistently; it is used in at least three different yet related ways.
• Data analytics is concerned with extraction of actionable knowledge and insights form big data.
• Descriptive Analytics: This essentially tells what happed in the past and presents it in an easily understandable form.
• Predictive analytics :  It extrapolates form available data and tells what is expected to happen in the near future.
• Exploratory or Discovery analytics. This finds unexpected relationships among parameters in collections of big data.