The rise of the dot com industry brought in a huge surge of data. The beginning of millennium brought in the intense requirement to manage huge amount of transactional data associated with ecommerce and other online businesses. Computers took over the working of departments of several industries bringing in the concept of enterprise wide data. Many forerunners came up with enterprise based solutions to tackle the terabytes of data, but the scalability was limited due the use of relational databases.
Big data is defined as collection of datasets, so vast and assorted, that a single database following relational storage mechanism is unable to store them. The traditional database management tools strike to be incompetent, when it comes to handle the complex data covering the different horizontals of the mentioned industry. To manage this special kind of data, we have Big Data Analytics.
Big Data analytics includes a process of analyzing large amounts of data with an intention to find useful patterns, usable correlations and other hidden opportunities that are not visible directly. Here are the 3 basic requirements which Big data analytics fulfills to make any business survive.
Big data analytics helps in scrutinizing data from different sections irrespective of category or department. This helps in finding hidden relationships which could seldom be found using analytics for typical relational databases. This assists the top management in making decisions lucrative for the business.
2) Capacity to manage industry wide data-
It is proven that all the departments of the industry are said to be inter-related and work in tandem to deliver the final product or service. Big Data allows the stratification of data with specific requirement based storage for individual departments. The IT managers find the implementation of Big Data Analytics feasible and help in providing reports aggregating diverse data sets keeping in congruence with the data relationships.
3) Use of predictive analytics in place of deterministic approach-
Traditional approach of analyzing data included querying of relational databases. This would take a considerable amount of time scrutinizing loads of datasets associated with different horizontals. Further, the portability of data would strike as an issue, when relational databases were used. Big Data Analytics employs the use of NoSQL databases like Hadoop and MapReduce. The queries put are dynamic and the nature of output is versatile and probabilistic as associated with the requirements of user. This gives the liberty to determine patterns and fill in the missing values with the use of complex algorithms available in Big Data Analytics. Thus the decision process becomes time critical with greater accuracy.
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