What is Big Data? – How it Affects the IT Industry
What is big data? According to Wikipedia, “The term ‘big data’ is a colloquialism that applies to statistical analysis of large-scale data, such as that gathered through scientific research.” Big data is a new field that studies ways to study, efficiently extract valuable information from, or else deal with extremely large and complex data sets which are beyond the reach of conventional statistical data processing programs. In other words, big data analytics is the application of statistical methods to the analysis of large-scale data sets. It was the outcome of advances in information technology and computer science that made possible the development of large-quantitative analysis (MLA), which in turn enabled companies to exploit the full potentials of existing data sets.
Nowadays, big data platforms dominate the scientific research arena. The rapid spread and impact of the digital revolution made possible this new development. The primary benefits of using big data analytics are cost reduction, enhanced insights, faster decision making, and better management. To put it simply, traditional approaches to science and statistical analysis are being challenged by the availability of huge and often unstructured data sets generated by complex and connected devices. In order to stay competitive and ensure long-term sustainability, science researchers and institutions are seeking better ways to deal with this new paradigm shift.
The main tools and techniques used to deal with this paradigm shift are Extract-Transform-Load (ETL), Decision Trees, and neural network processing. ETL uses a technique called domain tagging, which allows analysts to conveniently extract domain knowledge from large data sets. By the use of decision trees, an analyst can efficiently group and classify domains or “problem domains” into manageable parts. Networks which are loosely connected will make the job of a decision tree more manageable. Finally, the most advanced technique, the neural network, is used by what is commonly known as big data experts to problem solve or create new intelligence from large data sets.
The challenge faced by data scientists and other IT professionals is to design methodologies that are sustainable, and that can easily adapt to new, unstructured data sets. While all three are important to any effective method, social media is currently the hot property. Data scientist must therefore be able to easily leverage all three in a unique fashion to address problems and opportunities. It is no wonder then that so many IT professionals are interested in talking about big data platforms and what is big data?
One must take stock of today’s data and see where it is stored, aggregated, analyzed, and stored. As far as storing data goes, traditional data warehousing approaches work well. However, unstructured and semi-structured social media data sets pose a significant challenge for traditional data warehouse approaches.
In today’s data warehouse approaches, terabytes and petabytes of information must be processed on a daily basis to support both customer requirements and business strategy. In a social media management scenario, storing and analyzing such massive amounts of data becomes an almost insurmountable challenge. In order to deal effectively with this type of data, an IT professional needs to design solutions that can efficiently store, manage, and analyze the massive amount of data coming into their data warehouse. A traditional data warehouse approach stores all data in file formats that are readable by application users. File formats such as Excel, CSV, and RDF have been tried and tested to effectively store large amounts of unstructured and semi-structured data. In fact, some of the large databases used by major corporations are stored in such formats.
However, many analysts argue that file formats alone are not enough to store data sets of terabytes and petabytes in a meaningful way. This is precisely why IT professionals need to consider leveraging other forms of ETL tools such as software that can leverage complex data sets such as unstructured or semi-structured data sets via what is known as a transformational database management system (TBMD). Such tools can help an IT professional store and analyze huge amounts of unstructured and semi-structured data sets in a functional manner that is easily accessed by applications.
In a nutshell, answering the question, what is big data? is not straightforward. IT professionals must incorporate streaming data analysis, terabytes and petabytes of storage capacity along with expert knowledge of data warehouse design in order to deliver a robust data management platform.