Big Data

Big data describes large volume of both structured and unstructured data. This large volume of data overwhelms business on day-to-day basis. In many of the cases amount of data is not important, thing matter is what organization do with that data. Big data can be examined for insights that lead to the better decisions and strategic business moves.

“Big Data” concept is relatively new but the act of collecting and storing large amount of data for eventual analysis is quite old. The concept boosted in the early 2000s when industry analyst Doug Laney segmented definition of big data as the three Vs: Volume: Organizations gather information from a variety of sources, including business transactions, social media and information from sensor or machine-to-machine data. In the past, storing it would’ve been a problem – but new technologies (such as Hadoop) have eased the pressure.

Velocity: Data streams in at an unprecedented speed and must be dealt with in a timely manner. RFID tags, sensors and smart metering are driving the need to deal with torrents of data in near-real time.

Variety: Data comes in all types of formats – from structured, numeric data in traditional databases to unstructured text documents, email, video, audio, stock ticker data and financial transactions.

While most modern projects use a distributed model, any of these methods can get the job done if used correctly. It is important to remember, however, that no matter what version control model you chose, all contributions to the project must be made using it or it serves little purpose.

Importance of Big Data:

The importance of big data does revolve around what you do with the data rather than how much data you have. You can take data from any source and examine it to find answers that enable
1) cost reductions,
2) time reductions,
3) new product development and optimized offerings, and
4) 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 behaviour before it affects your organization.

Applications of Big Data:

Banking

Government

Government

Health Care

Manufacturing