Overview

Data is often considered to be the crown jewels of an organization. It can be used in myriad ways to run the business, market to customers, forecast sales, measure performance, gain competitive advantage, and discover new business opportunities. The data generated by different sources is in different formats. For the purpose of knowledge discovery, this data needs to be collected, stored and analyzed. The extracted insights from the analysis need to be visualized for easy and effective understanding.

The challenge gets even tougher when data needs to be collected and analyzed in real time. Then with the time, volume of data and scope of analysis is expected to increase. In order to respond to the above mentioned challenges, a highly scalable and flexible data analysis platform is required that can automate the whole process. This platform needs to be very cost effective for global adaptation. In the scope of this research we provide a model for Big Data analytics platform that can provide the solution to meet these requirements.

The proposed platform can be scaled according to data requirements and additional functional components can be integrated as per the scope of analysis. The data analysis within our platform also provides advance analytics models to extract the information based on efficiency use cases from large volumes of data.

In order to approach Big Data and analytics holistically, it is important to consider what that means. The strategy used to develop this reference architecture includes three key points to set the context:

  1. Any data, Any source. Rather than differentiate Big Data from everything else (small data?), we want to view data in terms of its qualities. This includes its degree of structure, volume, method of acquisition, historical significance, quality, value, and relationship to other forms of data. These qualities will determine how it is managed, processed, used, and integrated.
  2. Full range of analytics. There are many types of analysis that can be performed, by different types of users (or systems), using many different tools, and through a variety of channels. Some types of analysis require current information and others work mostly with historical information. Some are performed proactively and others are reactive. The architecture design must be universal and extensible to support a full range of analytics.
  3. Integrated analytic applications. Intelligence must be integrated with the applications that knowledge workers use to perform their jobs. Likewise, applications must integrate with information and analysis components in a manner that produces consistent results. There must be consistency from one application to another, as well as consistency between applications, reports, and analysis tools.

Conceptual Reference Architecture

The conceptual view for the reference architecture, shown below, uses capabilities to provide a high-level description of the Big Data and Analytics solution.

Whether the need is data storage, archival, movement, management, testing, analytics, or security, ERPA is the big data partner that delivers speed to value and data solutions that make a real impact to your business.

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