Main Characteristics of Analysis and Process Big data

Big data analytics is too much famous these days and if you want to be the part of the movements I need to know the quality and features of Big data analytics. Companies and entrepreneurs are excited to be able to access and analyze the big data that they’ve been collecting and want to gain insight from, but have not been able to manage or analyze in desired manner. It might involve visualizing lot of amounts of disparate data, or it might involve advanced analyzed streaming at you in real time. It is evolutionary in some respects and revolutionary in others.

So, what’s different when your organization is pushing the envelope with data analysis? The infrastructure supporting big data analysis is different and algorithms have been changed to be infrastructure aware.

Big data analysis should be viewed from two perspectives:

• Decision-oriented

• Action-oriented

Decision-oriented big data analysis is more similar to traditional business intelligence. See at selective subsets and representations of bigger data sources and try to apply the outcomes to the process of getting business decisions. Certainly these decisions might result in some kind of action or process change, but the purpose of the analysis is to increase decision making.

Action-oriented big data analysis is used for fast response, when a pattern emerges or specific kinds of data are move out of and action is required. Taking benefit of big data through process, analysis and causing reactive behavior changes offer great capacity for early adopters.

Searching and make use of big data by creating analysis applications can hold the key to extracting value sooner rather than later. To complete this task, it is more effective to build these custom applications by leveraging components.

First, see some of the characteristics of big data analysis that make it different from kind of traditional analysis aside from the three Vs of volume, variety and velocity.

• Can be programmatic. One of the greatest changes in analysis is that in the past you were dealing with data sets you could manually load into an application and then explore. With big data analysis, you may be faced with a situation where you might start with raw data that often needs to be handled pro-grammatically to do any type of exploration because of the scalability.

• Can be data driven. While many data scientists use a hypothesis-driven approach to data analysis, you can also use the data to drive the analysis — especially if you’ve collected huge amounts of it. For example, you can use a machine-learning algorithm to do this kind of hypothesis-free analysis.

• Can use a lot of attributes. In the past, you might have been dealing with hundreds of attributes or characteristics of that data source. Now you might be dealing with hundreds of gigabytes of data that consist of thousands of attributes and millions of observations. Everything is now happening on a larger scale.

• It can be iterative. More compute power means that you can iterate on your models until you get them how you want them. Here’s an example. Assume you’re building a model that is trying to find the predictors for certain customer behaviors associated. You might start off extracting a reasonable sample of data or connecting to where the data resides. You might build a model to test a hypothesis.

  Whereas in the past you might not have had that much memory to make your model work effectively, you will need a large amount of physical memory to go through the essential utterance required to train the algorithm. It may also be essential to use advanced computing techniques like natural language processing or neural networks that automatically evolve the model based on learning as more data is added.

• It can be quick to get the compute cycles you need by leveraging a cloud-based Infrastructure as a Service.

  With Infrastructure as a Service (IaaS) platforms such Amazon Cloud Services (ACS), you can fastly provision a cluster of machines to ingest huge data sets and analyze them in fast speed.