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Big data analytics offers data management opportunity in almost every aspect of life. Like many other disciplines, business systems and management researchers have also embraced big data analytics and data science. However, in the rush to exploit the popularity of this latest trend, the areas of big data analytics and data science that are most relevant to the business systems and management researchers’ field are not made clear.

In this article, some of the critical aspects of the big data problem are discussed, and their importance as well as relevance to information systems research is shown.

The discussion includes guidelines for operationalizing analytics, optimal organizational structure, and using analytic insights throughout your organization's user experience to customers and frontend employees alike. You'll learn to “think like a data scientist” as you build upon the decisions your business is trying to make, the hypotheses you need to test, and the predictions you need to produce.

In this article, it will be endeavored to study how business strategies will be made and driven successful with the help of big data.

The inherent complexities associated with natural language processing (NLP) have made many natural language responsible roles are manipulated for mathematically precise algorithmic solutions. In order to address this problem in applied business management, statistical machine learning approaches are developed to present NLP tasks. The emergence of Big Data enables a new paradigm for solving NLP problems - dealing with the issues associated with the NLP harnessing the power of data for building high quality models.

In this article, it is attempted to discuss the validity of the statistical machine learning tools and algorithms that are being used to perform NLP tasks.

New modeling technology integrates reservoir engineering analytical techniques with Artificial Intelligence, Machine Learning & Data Mining in order to formulate an empirical and spatiotemporally calibrated full field model. Data Driven subsurface modeling technology has been proven, for the past few years, to yield technical and commercial success in several oil fields worldwide.

In this paper, it is leveraged with other conventional reservoir modeling and management tools such as streamline modeling, isobaric maps and flooding conformance to analyze the efficiency of Numerical Modeling for Optimizing Oil Field Development and Management.

The emergence of big data has stimulated enormous investments into business analytics solutions, but largescale and reliable empirical evidence about the business value of data-driven decision making (DDD) remains scarce. This paper presents the results of an econometric study that analyzes the direction, sign, and magnitude of the relationship between DDD and firm performance based on objective measurements of DDD assets.

In this study, a relationship between DDD assets and firm productivity is estimated. Furthermore, the relationship between DDD and performance also appears in other performance measures such as asset utilization, return on equity and market value.

In business, data exploitation promises to create value in a variety of operations, from the optimization of value chains in global manufacturing and services more efficient use of labor and tailored customer relationships. To achieve the maximum benefits from data-driven innovation, business policymakers must take into account the possibility that regulation could impede economic and societal benefits. The proposed framework for business policy considerations examines three main areas of policy interest: privacy and security, ownership and transfer, and infrastructure and data civics.

This study is conducted with an aim to establish a relationship between the aforementioned areas of business policies.