What is big data analytics?

Big data analytics is the process of observing big data to reveal hidden patterns, unidentified relationships and other useful data to make better decisions. Big data analytics can analyze huge volumes of data that square analytics and business intelligence solutions cannot touch. It is generated mostly from, sensors, video/audio, social media websites networks, log files on a very large scale.

Your organization could add billions of rows of data with numerous data combinations in several data stores.  Big data allows you to drive innovation and make the possible decisions continuously. To process big data to figure out what is important and what is not, high performance analytics are required. You can use high performance analytics for easy and fast processing of only important data. Organizations are shaping the unique properties of machine learning that are preferably fit to addressing their big data requirements in new ways.

Big data is useful if you can do something with it. Amazon and Google companies use the subsequent knowledge to increase their competitive advantage and they are rulers at analyzing big data.

Let’s talk about Amazon, its big data analytics skills have made it successful. It takes all your buying history and the buying patterns of people like you to come up with some good suggestions.

Big data provides unique opportunities for your organization to analyze. Analyzing big data is challenging and use more detailed and broad data to do your analysis.

Analytics driven strategy will provide useful results with big data. When it comes to analytics consider a range of possible kinds:

  1. Simple analytics for insight

It describes about sharing and gambling of data, simple visualizations, basic monitoring, and reporting.

  1. Advanced analytics for insight

It describes about more difficult analysis like predictive modeling and pattern matching techniques.

  1. Operationalized analytics

It describes about analytics that are part of the business process.

  1. Monetized analytics

It describes about analytics that are used to drive revenue directly.

Reactive and Proactive approaches:

Reactive approach:

In reactive approach, Business Intelligence provides ad hoc reports, notifications based on analytics and business reports which are standard. But, when reporting pulls from giant data sets it means that it is performing big data BI.

Proactive approach:

In proactive approach, big analytics like text mining, forecasting, predictive modelling, statistical analysis and optimization allows you to spot weakness or identify trends for making decision about the future. In Big data analytics you can remove only the related data from petabytes, exabytes and terabytes. Becoming practical with big data analytics is not a onetime effort.

Advantages of Big Data Analysis

Big data analysis allows business users and market analysts to develop insights from the available data which results in several business advantages. Make a detailed analysis of the data and the key pointers from this analysis. Some of the standard advantages are as follows:

  1. Ad targeting companies analyzes about wide variety of data or events and provide better recommendations to users, whenever they browse shopping sites, hotels, travel portals, or search flights.
  2. They also provide better recommendations to user regarding discounts, deals and offers based on their product history and browsing history.
  1. If customers are moving from one service provider to another service provider, then by analyzing numerous issues faced by the customers due to huge call data records can be uncovered, in the telecommunications space.
  2. Based on these issues, it will be known that whether a telecom company require to place a new tower in a particular area.