Application of Big Data in Economic Policy

1.1 Introduction

In every moment data is generated from different sources like social networking sites, search and retrieval engines, media sharing sites, stock trading sites and news sources etc. Now-a-days all people use face book in every time. So large amount of data is generated from this type of social networking site[3]. Like Face book, Twitter, Google, YouTube are producing large amount of data in every day. If we consider different sources of collection of data related to human like hospital monitoring system, climate system and insurance policy system etc, then management of data is very necessary for these different systems. In old days, traditional database system was used for data storage, data manipulation and data retrieval. To solve problems of traditional database, different databases like relational, object-oriented, network, object-relational came to the market.

The traditional method of managing structured data includes a relational database and schema to manage the storage and retrieval of the dataset. For managing large datasets in a structured fashion, the primary approaches are data warehouses and data marts[7].

A data warehouse is a relational database system used for storing, analyzing, and reporting functions. The data mart is the layer used to access the data warehouse. A data warehouse focuses on data storage. The main source of the data is cleaned, transformed, catalogued, and made available for data mining and online analytical functions[7]. The data warehouse and marts are Relational databases systems. The two main approaches to storing data in a data warehouse are dimensional and normalization of data.

There are several challenges that the enterprises are faced today owing to the limitations posed by relational databases. Some of these are: a) unstructured data that could provide a real-time business decision support remains unused as they cannot be stored, processed or analyzed. b) Several data islands are created and it becomes difficult to generate meaningful information from those. c) Data models are non-scalable and data becomes unmanageable. D) The cost of managing the data increases exponentially with the growth of data [1].

Data mining consists of five major elements [9]:

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  Extract, transform, and load transaction data onto the data warehouse system.

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  Store and manage the data in a multidimensional database system.

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  Provide data access to business analysts and information technology professionals.

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  Analyze the data by application software.

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  Present the data in a useful format, such as a graph or table.

As now-a-days amount of data is increased, so it is very difficult to manage data in large data producing sources. Big data analytics takes a major role in handling huge amount of data. Now India is currently one of the world's most attractive investment destinations. With the opening up of foreign direct investment (FDI) in several sectors, the country is an eye-catching destination for overseas investors[10]. The relaxation of norms by the government has also created a vast opportunity for foreign players, who are competing for a greater role in the Indian market. Sectors projected to do well in the coming years include automotive, technology, life sciences and consumer products. So we can use big data concepts in economic policy related to government and private sector for making strong decisions. This chapter includes different sections. Section 2 explains details about big data and its working principle. Section 3 provides idea about economic policy. Section 4 explains how big data technique can be applied in economic policy.

1.2 Background