Big Data Analytics

by 17:59 0 comments BIG DATA ANALYTICS

Article By- Neeti Malhotra – BCA-VIth sem, 1st shift

The availability of low-cost commodity hardware, big data, and new information management and analytic software have produced a different point in the history of big data analytics. The merging   of these trends means that we have the capabilities required to analyze blindsiding data sets quickly and cost effectively for the first time in history.
These capabilities are neither hypothetical nor inconsiderable. They represent an actual vault forward and a clear opportunity to realize in terms of productivity, efficiency, revenue and profitability. The age of big data is here and these are truly revolutionary times if both technology and business professionals continue to work together and deliver on the promise.
It will surely help companies to better understand customers and to find the hidden opportunities. Even it will help our government to better serve citizens and alleviate fraud, it will inspire hundreds, thousands and millions of new start-ups. It will alter the landscape across virtually every industry and finally answer the question that will come every CEO’s head like how can my business use the big data? What problems can it solve? In every revolution, there are many more opportunities, opportunities that will behold only by those armed with the right tools and the right strategy.

From the different people perspective, big data means a data neither having enough RAM nor having the complete set, at the level of complexity in data that will be simply usual to seeing. Big data analytics are no longer estimate they are observed. Sometimes middle-level management does not understand analytics, so big data analytics initiates loose the support.
There are many firms that are bogged down with investments & IT rather than spending time actually looking at the data. In today’s, data analytics is disruptive in the sense that all models are wrong and increasingly you can succeed without them. We all are at beginning of the big data revolution. A year ago, the internet revolutionized the conjugations of people. But now big data analytics revolutionizes the conjugations of data. 
Big data analytics, in general, is unrestrained, it changes the problem fundamentally. It increases Complexity as it brings more and more data from outside firms. We as a business needs to create a new department with new analytics, need to serve as an internal consulting head to rest of the business assigned to clients, to set with them and ensure analytical integration. Firms need a big data Analytics sanction.
 Analytics is very valuable as in hardware, we need analytics data person we need analytics and if a chief strategy officer, we need analytics, in fact, we need analytics in our organization that struggle is efficient so chief analytics organization (CAO) must rise. Talent in big data analytics plays a vital role as it shifts to more computational skills, softer skills, teamwork and leadership because of all these factors unemployment rate is off approximately zero and the real world internships are hit the ground running.
Thousands and millions of company emerge with our company to figure out massive sense of data but if we talk about production operation in analytics of the data is of high velocity and complexity whatever the environment is you cannot really extract it ,it is tough to put here and do something with it figure out and put it back over that does not really work that is not the big data environment it hardly provides classical consulting services on big data .In Telecom services add network data created in the massive amount so analytics as a product begins.

In 2001, industry analyst defines big data in terms of 3 v’s i.e. volume, velocity and variety.
Big data has a large volume which means transaction based data stored in relational databases since here make the part of the volume. Unstructured data that is being stringent from social media also plays a role. Sensor and machine to machine generate data is increasing with time. In the past, storage was an issue however this time the storage costs has decreased.
On the other hand, big data flows at very speed, sensor and smarter videoing spill out large data within short period reacting fast enough, today with high-velocity data is one of the challenges the company face speed of data could be highly inconsistent with period weeks this is especially true in social media when something trends daily, seasonal and event prompt leak data loads sometimes difficult to manage specially when there is unstructured data involved
Variety means data today comes in different formats where structured data resides in traditional relational databases and found unstructured data examples include text documents, email, video, audio, log files etc.  ,big data comes from various sources, he challenge comes in managing ,merging and governing different varieties  of data the big data hast to be connected and correlated during the analysis phase in order to extract information out of it.
In 2001, industry analyst defines big data in terms of 3 v’s i.e. volume, velocity and variety.
Big data has a large volume which means transaction based data stored in relational databases since here make the part of the volume. Unstructured data that is being stringent from social media also plays a role. Sensor and machine to machine generate data is increasing with time. In the past, storage was an issue however this time the storage costs has decreased.
On the other hand, big data flows at very speed, sensor and smarter videoing spill out large data within short period reacting fast enough, today with high-velocity data is one of the challenges the company face speed of data could be highly inconsistent with period weeks this is especially true in social media when something trends daily, seasonal and event prompt leak data loads sometimes difficult to manage specially when there is unstructured data involved
Variety means data today comes in different formats where structured data resides in traditional relational databases and found unstructured data examples include text documents, email, video, audio, log files etc., big data comes from various sources, he challenge comes in managing, merging and governing different varieties of data the big data hast to be connected and correlated during the analysis phase in order to extract information out of it.