We are a part of the information society and in the last few decades, people of our society focused on digitalized sharing of information. The amount of data that is produced with each transaction, click, or event generated from our smart devices is increasing every moment. It is extremely difficult to process or even scale such amount of data available in the digital format. This has created the requirement of a technology that can store and process large amounts of data sets. This necessity has given the birth of Big Data and Data Analytics
Why Data Analytics has become very essential?
In the earlier days, the statistical and analytical operations were performed with the help of database management software such as DBMS and RDBMS, but they were effective only when data resources were limited. The exponential increase in the amount of data has generated several critical challenges for data analysis. The increase in the amount of data produced has reduced the efficacy of the data analysis process for retrieving information and insights using the traditional techniques. Additionally, the scope of traditional techniques was limited to a specific schema, architecture, relationship, and types of data. But data analytics and big data come up with several advantages such as:
· Scaling: The traditional approach of analysis was more centralized and used only a single computing device for carrying out the required operations. In other words, all the operations were carried out on a single server, which usually limited the scalability of the system. But the techniques of data analytics use a distributed approach to process data sets. This allows the user to add new computational machines and devices to the network whenever a new requirement is generated.
· Data schema: An evident limitation associated with the traditional analysis techniques is the ability to process only static data schema. RDBMS can only process data and retrieve information from resources that are stored in a structured format. In other words, the schema of data is created before a query is generated. Data analytics allows users to process resources using dynamic schemas. Here, a schema of data is created after the query is generated.
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- Higher Cost: The traditional data analysis systems use dedicated resources to process the data. The requirement of hardware resources increases with an increase in the amount of data resources. This increases the operating cost of the system. Whereas, data analytics technique uses commodities and shared resources to process large data sets. The main problem is divided into multiple sub-problems and then processed with the help of multiple devices. This reduces the requirement of dedicated resources for the operation, thereby reducing the cost of operations.
- Accuracy: The traditional data analysis techniques can only handle a limited size of data, that is, the sample size for analysis is small. The ability to handle only limited data resources affects the overall efficacy of the data analysis results. Therefore, the accuracy of the results is always questionable. Whereas, data analytics can handle significantly higher data amount, which results in more precise points of correlation.