Loading presentation...

Present Remotely

Send the link below via email or IM


Present to your audience

Start remote presentation

  • Invited audience members will follow you as you navigate and present
  • People invited to a presentation do not need a Prezi account
  • This link expires 10 minutes after you close the presentation
  • A maximum of 30 users can follow your presentation
  • Learn more about this feature in our knowledge base article

Do you really want to delete this prezi?

Neither you, nor the coeditors you shared it with will be able to recover it again.


Big Data

An Analysis

gunhad singh

on 10 March 2013

Comments (0)

Please log in to add your comment.

Report abuse

Transcript of Big Data

BIG DATA INTRODUCTION Big data analytics is the process of examining large amounts of data of a variety of types to uncover hidden patterns, unknown correlations and other useful information. Such information can provide competitive advantages over rival organizations and result in business benefits, such as more effective marketing and increased revenue. IMPACT It has increased the demand of information management specialists in that IBM, Microsoft, Facebook, HP have spent more than $15 billion on software firms only specializing in data management and analytics. In 2010, this industry on its own was worth more than $100 billion and was growing at almost 10 percent a year: about twice as fast as the software business as a whole. USE OF BIG DATA The primary goal of big data analytics is to help companies make better business decisions by enabling data scientists and other users to analyze huge volumes of transaction data as well as other data sources that may be left untapped by conventional business intelligence programs. BIG DATA LANDSCAPE The Big Data Landscape includes over 100 companies and Big Data vendors of all sizes, public and private market investors, and technology buyers. DATA MINING Data mining is sorting through data to identify patterns and establish relationships. Data mining parameters include:
1)Association - looking for patterns where one event is connected to another event
2)Sequence or path analysis - looking for patterns where one event leads to another later event
3)Classification - looking for new patterns
4)Clustering - finding and visually documenting groups of facts not previously known
5)Forecasting - discovering patterns in data that can lead to reasonable predictions about the future

Data mining techniques are used in a many research areas, including mathematics, cybernetics, genetics and marketing. Web mining takes advantage of the huge amount of information gathered by a Web site to look for patterns in user behavior. BIG DATA VOLUME VELOCITY VARIETY Big data are high volume, high velocity, and/or high variety information assets that require new forms of processing to enable enhanced decision making, insight discovery and process optimization MAJOR FEATURE Predictive Analytics This is the branch of data mining concerned with the prediction of future probabilities and trends. The central element of predictive analytics is the predictor, a variable that can be measured for an individual or other entity to predict future behavior.
Multiple predictors are combined into a predictive model, which can be used to forecast future probabilities with an acceptable level of reliability. In predictive modeling, data is collected, a statistical model is formulated, predictions are made and the model is validated.
Big data analytics can be done with the software tools commonly used as part of advanced analytics disciplines such as predictive analytics BIG DATA IS THE FUTURE OF THE “INFORMATION AND TECHNOLOGY” INDUSTRY
Full transcript