Articles

Machine Learning as Managerial Tool: A Case Study in ADNOC

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In the current business environment, managers are facing challenges in managing different kinds of people. They find it difficult to track, evaluate, and manage employees in a fast-paced work setting. Machine learning is an emerging concept that deals with unsupervised and supervised learning of a machine to provide a usable system. In this matter, this paper aims to investigate how companies can leverage the use of machine learning in people management and in improving the performance, productivity, and motivation of employees and managers. Thus, the research used both qualitative and quantitative research approaches to examine the impact of machine learning in an organizational setting.

A Generic Approach to Entity Resolution Mechanisms for Big Data on Real World Match Problems in the Global Oil and Gas Sector

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Complex challenges are facing the global oil and gas industry. Oil prices are dropping due to OPEC production level, US oil boom, and other factors. Many experts believe that prices of oil will remain low for years at equilibrium of around $40-50 (Blumberg, 2018; Walls and Zheng 2018; Azar, 2019). Although 2019 oil price is expected to average at $65 with a further decline at $62 by 2020 (Amadeo, 2019; Kasim, 2019). Also, newly commercial resources are extremely expensive to develop, as massive capital investments are required. This research intends to develop a comprehensive entity resolution framework that has the ability to search across multiple databases with disparate forms, tame large amounts of data very quickly, efficiently resolving multiple entities into one, as well as finding hidden connections without human intervention. Putting in place a system to manage these entities will not only help to better assign resources, but to do so in a more expedient fashion. Although the necessary information is mostly already available within the oil and gas companies, it is spread around different company areas and application. Entity resolution will helps to aggregate these data, identify and exploit connection between entities and offer holistic all-in-one information that can helps to identify and deal with potential risk. We therefore present such an evaluation of existing implementations on challenging real-world match tasks. We consider approaches both with and without using machine learning to find suitable parameterization and combination of similarity functions. In addition to approaches from the research community we also consider a state-of-the-art commercial entity resolution implementation. Our results indicate significant quality and efficiency differences between different approaches. We also find that some challenging resolution tasks such as matching product entities from Opec database are not sufficiently solved with conventional approaches based on the similarity of attribute values.

A Comprehensive Study on Integration of Big Data and AI in Financial Industry and its Effect on Present and Future Opportunities

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This study evaluates the substantial influence of AI-technologies in the finance industry, with advancement expected to accelerate in the next few years. It also forecasts the expansion of AI adoption across various business sectors and the integration of AI-based operational networks with existing commercial systems to meet consumer demands. The financial industry will blend AI-based transaction channels with established systems, enhancing the customer experience and efficiency. This integration will streamline and advance transaction processes, making them more responsive to customer demands. The standard change in customer dealings is expected to be a significant transformation in the financial sector. The transformative potential of Big Data and AI in the financial sector goes beyond operational improvements; these technologies will create new opportunities for growth and development, giving financial institutions a modest point in operational efficiency and innovative product and service offerings.
This study aims to investigate the overall impact of the convergence of Big Data and AI on the financial industry. It anticipates increased revolution, diversification of commercial applications, and smooth AI integration into existing systems. These technologies will shape the financial environment in the future, offering new opportunities for industry participants and consumers.

Are The Independent Area of Beef Cattle Development Ready for Big Data to Reduce the Volume of Imports?

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Stakeholders find it difficult to make decisions for both breeders and supporting sectors due to unorganized agribusiness data on beef cattle, it seems very limited information on it. The activities related to this business have not been recorded well, from male selection, feed management, cultivation, as well as marketing and traceability—these factors have caused price disparities in meat to become commonplace. Thus, a model of breeder empowerment is needed through big data maturity. Data play a crucial role in the planning and development of agriculture and agribusiness. The results of the analysis on efforts to digitalize and integrate data on beef cattle business confirm that the progress stops at the Nascent phase. Data digitalization at the Agency for Agriculture Extension of Kediri is at the Nascent phase, while at the Department of Food Security and Livestock Service is in between the Nascent and pre-adoption phase. Data integration in other agencies, such as the Regional Central Bureau of Statistics of Kediri, the Regional Information and Communication Office of Kediri, and the Directorate General of Livestock, fall into the corporate adoption and mature phase. As can be seen, data have not been well-integrated within one interconnected system. The availability of such a model of data integration will be a good alternative in empowering breeders of beef cattle and the public sector, it will also find communication easier with the existence of the model. The government eventually will be able to better improve performance based on the digital data available.