Big Data analytics – Definition, Concept and Tools

Definition of Big Data analytics:

The definition of big data gives an idea of what big data analysis is all about. In the words of the Gartner IT Glossary, Big Data is nothing but an information resource that demands information processing that is not only innovative but also cost-effective and thus provides an enhanced insight for decision-making. It should be noted that big data offer information that is high in speed, volume and variety.

Volume: This parameter indicates the quantity of data. High volume is an effect of various factors like sensor and machine-developed data, sensor, data from network and social media, etc. Terabyte and Petabyte data are from the major proportion.

Variety: This is an indicator of the proportion of the various types of data available.

Velocity: This feature gives an indication of the speed at which the data processing occurs. The speed of data streams is huge and extremely fast and is different for mobile devices and machine-to-machine process etc.

Merits of big data analytics tools explained:

A survey comprising of 540 decision makers cites that in order to enhance the client demands, enhance the product and thus fit into the competition, they used big data by purchasing it for Webopedia’s host organization QuinStreet. The merits of big data analytical tools are enormous like optimization of efficiency in operation, enhanced marketing strategies leading to boost in sales, discovering new revenue making options involving refused risk, and the most importance is the revamped customer care service offered.

Use cases for big data analysis:

Below is a glimpse through the various use cases available for big data analysis:

  • Marketing:
    Here it is used for managing and optimising the various marketing campaigns, cross and up-selling as well as location-based and one to one marketing, and a complete review of customer requirements etc.
  • Finance:
    I used big data analysis, for the sake of fraudulent check and prevention, risk management, how to increase money, trade surveillance, anti-money laundering etc. It also prevents credit risks.
  • Insurance:
    To avoid risk and fraud, then to analyse the workload bore by the customer care executives, analysis of customer value etc.
  • Retail:
  1. Analysis of merchandise
  2. supply chain management and analysis
  3. cross-consumer optimisation
  • Telecommunications:
    For networking optimisation and planning, sufficing various customer demands, location of mobile users and for the research of the newly launched product.

Analytics Engines:
With the help of these, you can provide connectors for simultaneously processing databases.

  • Ways to support big data analytics: In order to support the many big data cases, enormous solutions are a must. Integration with ease can be provided in both plain and advanced analytics so that it is easy for you to centrally analyze not only your data but also the required marketing solutions essential for analytical engines, on a single platform.
  • Analytics and Spreadsheets Tools: ODBC connectors will help your clients connect with Microsoft office,  Excel and tools that are offer from superb analytics sellers like Qlik, MicroStrategy, TIBCO, Jaspersoft, Tableau etc. Added to the plate is the coordination of the R statistical programming language with your organization with the help of ODBC/REST APIs.
  • CRM and Online Marketing Solutions: Renowned CRM’s and online marketing solutions like offer the solution that suits your business. Also, web analytics solutions provide the essential help.

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