Whenever there is a comparison between data and information, it is evident that data is nothing but raw, unorganized facts that need to be processed. Data is something really simple and seemingly random unless it is organized. When data is processed, organized, structured and/or presented in a contextual manner, so as to make it useful, we call it information.

Speaking of these terms, we come to what is in much in vogue these days, BIG DATA. Now big data is a widely used term these days. It is also often misinterpreted. So, after reading this blog, I hope that the term big data does not scare you! Because big data is just a fancy name for the same old data we have been using since forever.

Big data is larger sets of traditional and digital data used by organizations to achieve specific goals and carry out certain operations. Big data would include a wide variety of data of more than one format.

When we define big data, we cannot disregard the fact that big data is a mix of structured and unstructured data in larger volumes. The structured data comes from interactions between people and machines, such as web applications or social networks. The unstructured data comes from information that is not organized or cannot be easily interpreted.

But basically we can certainly come to one conclusion: Every organization needs a complete understanding of big data. They need to know what big data means, how is adds value to their business, and the potential of data-driven marketing.

Big data can be characterized by the quantum of data, the variety of types of data and the velocity at which the data should be must processed. Since it is a time consuming and an expensive affair to data load into a traditional relational database for analysis, new approaches to preserving and analyzing data have emerged. These methods rely less on data schema and data quality. Instead, the raw data with extended metadata is aggregated in a data lake and machine learning and artificial intelligence (AI) programs use complex algorithms to look for repeatable patterns.

Big data analytics is associated with cloud computing because the analysis of large data sets in real-time requires a platform like Hadoop to store large data sets. Although the demand for big data analytics soaring, we observe that there is currently a shortage of data scientists and analysts who have experience working with big data in a distributed, open source environment. In the enterprise, vendors have responded to this shortage by creating Hadoop appliances to help companies take advantage of the semi-structured and unstructured data they own.

In today's data-centric world, organizations must be willing to make cultural and strategic and changes. Industries are leading the way with new and innovative approaches to collecting and mining vast volumes of information. The payoff is impressive. Retailers are creating highly targeted marketing campaigns that engage customers and increase lifetime value. Scientists and researchers are using data to develop next-generation products at record speed. Pegasus has been actively contributing in terms of Big Data. At Pegasus InfoCorp we provide expert consultants to help clients with their big data needs.

We at Pegasus understand that thorough knowledge of Big Data Analytics is essential for any business to invest in this technology. Every successful implementation of the Big Data Analytics requires three most important elements viz. business understanding, machine learning expertise, and technology.

Pegasus InfoCorp's Analytics experience is currently localized and available in Pegasus InfoCorp locations like Mumbai & Pune. Pegasus InfoCorp plans to build environments in upcoming development centers around the world. To learn more about how Pegasus InfoCorp is making big data work for varied enterprises, see contact us at info@pegasusinfocorp.com