Building Business Analytic Tool using Dark Data and Big Data

The data is turning into the fundamental resource in the present science and innovation. Tragically, a lot of accessible and put away information isn't utilized today. This information is known as dull information. Big data is said to offer not just phenomenal degrees of business knowledge concerning the propensities for buyers and opponents, yet in addition to proclaim an upset in the manner by which business are coordinated and run. Organizations strive to achieve a competitive edge through, big data and business analytics tool. In this paper we have discussed about how dark data is used in organizations and the technologies evolved in business model. We have explored awareness in dark data and how we can implement them in business model. Keywords: Big Data, Dark Data, Business Intelligence (BI), etc.

Introduction to Big Data and Business Analytics

The term big data has come due to rapid generation of data in various organizations. In big data, the big is the buzzword. Here the data are so large and complex that the traditional database applications are not able to process (i.e., they are inadequate to deal with such volume of data). Usually the big data are described by 5Vs (volume, velocity, variety, variability, veracity). The big data can be structured, semi-structured, or unstructured. Big data analytics is the process to uncover hidden patterns, unknown correlations, predict the future values from large and complex data sets. In this chapter, the following topics will be covered more in detail. History of big data and business analytics, big data analytics technologies and tools, and big data analytics uses and challenges.

The Effect of Education on Information Systems Success

Research into the relationship between education and information systems (IS) success (use, satisfaction, and impact) has produced mixed results. Such results seem counterintuitive, given the many benefits that education brings to the workplace. However, workplace research from Human Resources (HR) has similarly found that education has little direct effect on job performance. Instead, education has indirect effects on job performance through job expertise, which is what drives behavior and job performance. The present research integrated the Delone & McLean IS Success Model with the Job Performance Model, and found similar results: in a survey of 465 professionals working in business analytics (BA), user education level had no direct effect on IS success (BA tool use, satisfaction, and impact). Instead, education level had a positive effect on expertise with the BA tool, which in turn positively affected BA tool use. These results build upon those from HR, and suggest that education has an indirect effect on IS success, rather than a direct effect.

A Service-Oriented Foundation for Big Data

This paper provides a service-oriented foundation for big data. The foundation has two parts. Part 1 reveals 10 big characteristics of big data. Part 2 presents a service-oriented framework for big data. The framework has fundamental, technological, and socio-economic levels. The fundamental level has four big fundamental characteristics of big data: big volume, big velocity, big variety, and big veracity. The technological level consists of three big technological characteristics of big data: Big intelligence, big analytics, big infrastructure. The socioeconomic level has three big socioeconomic characteristics of big data: big service, big value, and big market. The article looks at each level of the proposed framework from a service-oriented perspective. The multi-level framework will help organizations and researchers understand how the 10 big characteristics relate to big opportunities, big challenges, and big impacts arising from big data. The proposed approach in this paper might facilitate the research and development of big data, big data analytics, business intelligence, and business analytics.

The effect of individuals’ analytical orientation and capabilities on decision quality and regret

Decision makers are exposed to an increasing amount of information. Algorithms can help people make better data-driven decisions. Previous research has focused on both companies’ orientation towards analytics use and the required skills of individual decision makers. However, each individual can make either analytically based or intuitive decisions. We investigated the characteristics that influence the likelihood of making analytical decisions, focusing on both analytical orientation and capabilities of individuals. We conducted a survey using 462 business students as proxies for decision makers and used partial least squares path modeling to show that analytical capabilities and analytical orientation influence each other and affect analytical decision-making, thereby impacting decision quality and decision regret. Our findings suggest that when implementing business analytics solutions, companies should focus on the development not only of technological capabilities and individuals’ skills but also of individuals’ analytical orientation.

Big Data Analytics in Higher Education

This chapter reviews the literature on the use of business analytics in higher education. Universities have large datasets available to predict future direction and generate actionable information. An important type of analytics used to improve management processes and to make informed decisions is big data business analytics. State university executive leaders may improve the effectiveness of their decisions by integrating business analytics in the decision-making models. However, there is a need to examine the use of big data business analytics in the decision-making process at the executive leadership level of the selected state universities. Especially in the context of how descriptive, predictive, prescriptive, decisive and basic analytics, and data collection influence the decision-making process at the executive leadership level of the state universities in terms of student retention and graduation rates.