Value Of Big Data Analytics Research Articles

Mathematical analysis of big data analytics under bipolar complex fuzzy soft information

The aim of this study is to develop new and efficient theories for handling complex and unreliable data in real-world scenarios. The proposed approach integrates two distinct theories: the Bipolar Complex Fuzzy Set (BCFS) and the Soft Set (SS), resulting in a novel and superior method compared to existing solutions. Furthermore, the value of big data analytics cannot be overstated as it provides businesses with the opportunity to use their data to find areas for development and progress. To gain insights from raw data, it must first be collected and organized. Data modeling represents these large, complex data sets in a visual format, such as a chart or diagram. However, the visualization process often results in uncertainties and inaccuracies, highlighting the need for a more robust approach to data analysis. To address this issue, this paper introduces the concept of Bipolar Complex Fuzzy Soft Relations (BCFSRs), a fuzzy decision-making method within a complex bipolar fuzzy environment that can have the advantages of both a Bipolar Complex fuzzy relation and a soft relation at the same time. In BCFSRs, an element can have degrees of membership in both the positive and negative directions, reflecting a more nuanced and versatile representation of uncertainty or ambiguity. These mathematical ideas are explained through the Cartesian product of two Bipolar Complex Fuzzy Soft Sets (BCFSSs) and serve to simplify the decision-making process by presenting the BCFSSs in a clear and concise manner. The inventive notion of the BCFSRs elucidates the combined positive and negative effects of anything with parameterization. To support organizations in making data-driven choices and obtain insights for strategic planning and inventiveness, the analysis of big data is essential in several industries, including financial services, marketing, and medical treatment, among others. To choose the optimal big data analytics for efficient operation, this research presents modeling methodologies based on BCFSRs, including the formulation and analysis of scoring functions. The validity of the proposed work is demonstrated through a comparison study with existing methods, providing valuable insights for effective data analysis.

Unpacking task-technology fit to explore the business value of big data analytics

Understanding how the application of big data analytics (BDA) generates business value is a persistent challenge in information systems (IS) research. Improving understanding of how BDA realizes business value requires unpacking theories to study the phenomenon. This study unpacks the task-technology fit (TTF) theory toward generating new and improved insights into the business value of BDA. Extant studies on TTF have mainly focused on traditional IT which is different from digital technologies like BDA that are malleable and dynamic. While TTF has primarily focused on how the technology meets task requirements, this study contends that tasks can also be structured to fit the functionality of technology. This study proposes a 2 × 2 matrix framework to explain how BDA and tasks interact. The framework indicates how the reconfigurability of tasks and the editability of BDA impact the fit between tasks and BDA. Future research should explore how the fit between tasks and BDA changes over time.

Electricity Consumption using Big Data Analytics

This paper focuses on using big data analytics to analyze the electricity consumption patterns of 500 consumers(residential) in a particular area over 24 months. The data was obtained from MESB and analyzed to understand the trends in consumption better. The analysis was used to create visualizations and graphs that helped to identify the consumption patterns of individual users as well as the overall trends in the area. Using the Power BI tool, a dashboard is created to showcase the results. The dashboard provides valuable information such as the top 10 consumers and average consumption values by user. This information can be used by utility companies to identify areas where consumption is high and to develop strategies to reduce overall electricity consumption. This study demonstrates the value of big data analytics in understanding complex patterns and identifying opportunities for energy savings

Aligning Tasks, Technology, People, and Structures to Leverage the Value of Big Data Analytics

This study provides an understanding of how task, technology, people and structures (TTPS) can be used by developing countries as a baseline for future alignment evaluations to leverage big data analytics (BDA) in healthcare. Enabling the adoption of patient-empowering technologies is being used to cultivate a more patient-centric and collaborative healthcare ecosystem. Creating simple and reliable technology then integrating it into the existing public healthcare infrastructure has generated new challenges for big data analytics. Challenges arise because of complex interactions among the tasks that need to be supported by the technology, people using the technology and the distinct contextual characteristics of the organization. Therefore, TTPS are organisational elements that affect the value generated by BDA. However, little is still known about how these interacting organizational elements can be aligned to obtain maximum BDA value. A study involving participants from different industrial sectors in South Africa was conducted to determine what level of alignment of TTPS yields the best value from BDA. We adopted the notion of alignment as Gestalts. Results show strong alignment between TTPS where IT experts work closely with financial planners. Adequate training, coupled with structures encouraging usage of BDA results in higher organisational performance when technology is in sync with the tasks.

Application of big data for distribution and consumption of power

The exponentially growing and tremendous collection of data stored in the power sector, combined with the need for data analysis, has produced an urgent need for powerful tools to extract hidden data as to effectively distribute the power for proper consumption for the household. This research work was embarked on to show the business value of big data analytics in Energy and utilities with a focus on how analytics can help solve problems of inefficiency and wastages in electricity generation, production and distribution and how raw energy datasets can be converted into insights that can be used by energy policy makers to make major business decisions. To explicitly show how raw data can be turned into insights, the study deploys the use of the Hadoop on Hortonworks’ open-source apache-Hive licensed data warehousing framework run on a windows operating system to turn raw datasets (in excel formats converted to .csv format) gotten from the prepaid meters of 196,000 consumers (households and businesses) in 11 business units of Ikeja Electricity Distribution Company (IKEDC, Nigeria) to analyze the distribution and consumption of power.

Organizational performance and capabilities to analyze big data: do the ambidexterity and business value of big data analytics matter?

PurposeThe aim of the study is to examine the impact of the big data analytics capabilities (BDAC) on the organizational performance. The study also examines the mediating role of ambidexterity and the moderating role of business value of big data (BVBD) analytics in the relationship between the big data analytics capabilities and the organizational performance.Design/methodology/approachThis study collected primary data based on a questionnaire survey among the large manufacturing firms operating in UAE. A total of 650 questionnaires were distributed among the manufacturing firms and 295 samples were used for final data analysis. The survey was conducted from September to November in 2019, and data were analyzed based on partial least squares structural equation modeling (PLS-SEM).FindingsThe big data analysis (BDA) scalability is supported by the findings on the performance of firm and its determinants such as system, value of business and quality of information. The roles of business value as a moderator and ambidexterity as mediator are found significant. The results reveal that there is a need for managers to consider the business value and quality dynamics as crucial strategic objectives to achieve high performance of the firm.Research limitations/implicationsThe study has significant policy implication for practitioners and researchers for understanding the issues related to big data analytics.Originality/valueThis is an original study based on primary data from UAE manufacturing firms.

Leveraging big data analytics capabilities in making reverse logistics decisions and improving remanufacturing performance

PurposeThe study investigated the effect of big data analytics capabilities (BDACs) on reverse logistics (strategic and tactical) decisions and finally on remanufacturing performance.Design/methodology/approachThe primary data were collected using a structured questionnaire and an online survey sent to South African manufacturing companies. The data were analysed using partial least squares based structural equation modelling (PLS–SEM) based WarpPLS 6.0 software.FindingsThe results indicate that data generation capabilities (DGCs) have a strong association with strategic reverse logistics decisions (SRLDs). Data integration and management capabilities (DIMCs) show a positive relationship with tactical reverse logistics decisions (TRLDs). Advanced analytics capabilities (AACs), data visualisation capabilities (DVCs) and data-driven culture (DDC) show a positive association with both SRLDs and TRLDs. SRLDs and TRLDs were found to have a positive link with remanufacturing performance.Practical implicationsThe theoretical guided results can help managers to understand the value of big data analytics (BDA) in making better quality judgement of reverse logistics and enhance remanufacturing processes for achieving sustainability.Originality/valueThis research explored the relationship between BDA, reverse logistics decisions and remanufacturing performance. The study was practice oriented, and according to the authors’ knowledge, it is the first study to be conducted in the South African context.

The effect of data strategy on competitive advantage

PurposeIn the age of analytical competition, business models and gaining competitive advantage (CA) are directly related to organizations’ data strategy. Incorporating data strategy enables the organization to unlock the potential value of big data analytics. Therefore, this study aims to analyze how data strategy affects the achievement of CA.Design/methodology/approachThrough survey research, data were collected from 103 respondents. Model testing and evaluation, partial least squares structural equation modeling and analysis of mediating effects were performed.FindingsThe supported hypotheses confirm the direct effects between the defensive and offensive positioning of the data strategy on CA, as well as the mediating effect of the offensive data strategy (ODS).Originality/valueThis study is groundbreaking in terms of empirically analyzing how defensive data strategy (DDS) or ODS affects CA. In conclusion, to extract value from their data, organizations need to establish their data strategy based on their strategic positioning, the potential of their resources and their analytical capabilities.Research implicationsThe defensive and offensive positioning of the data strategy are closely related to each other and both, in isolation, directly and positively, impact CA. It is also noted that the role of defensive positioning is to strengthen the offensive positioning of the data strategy, which in turn, plays a central role in achieving CA.Practical implicationsThe results can assist managers in effectively incorporating analytical resources and capabilities, as well as helping them understand the dynamics and advantages of data strategy, so they can drive strategic objectives according to the organization’s positioning.Originality/valueThis study is groundbreaking in terms of empirically analyzing how DDS or ODS affects CA. In conclusion, to extract value from their data, organizations need to establish their data strategy based on their strategic positioning, the potential of their resources and their analytical capabilities.

The Pursuit of Pleasure

Many component processes of reward require appropriate serotonin (5HT) and dopamine (DA) neurotransmission within key limbic brain regions. Evidence suggests that dysregulation of 5HT and DA transmission can precipitate reward dysfunction and major depressive disorder (MDD) symptoms in genetically predisposed individuals. Various neurobiological indicators (biomarkers) of MDD have been proposed, including changes in signal transduction pathways, protein phosphorylation, and gene expression in subcortical, reward-related structures. However, these insights have yielded limited clinically relevant benefits for diagnosis, treatment, or prognosis. In addition, clinical application of identified biomarkers is often hindered by multiple factors including disease heterogeneity and symptom variability between patients. Innovative approaches including big data analytics, methodical collaboration between research programs, and reverse-translational strategies are now required to understand if particular biomarkers can be used to predict disease onset and treatment response, to stratify treatments for patient subgroups, and develop novel pharmacotherapies. This review briefly summarizes the predictive value of big data analytics in parsing the neurobiological underpinnings of MDD, with a focus on potential clinically-viable biomarkers for predictive therapies.

Perceptions of independent financial advisors on the usefulness of big data in the context of decision making in the UK

Big data analytics (BDA) are based on massive amounts of structured and unstructured data and should extract meaningful, accurate, and relevant data. However, what is considered as meaningful and relevant is a perception of the analyst. In our paper we focused on perceptions and not on the technical implementation of BDA. For this we looked into one of the most advanced services - the financial services market in the UK. We chose eight independent financial advisors who have had first-hand experiences with large amounts of structured and unstructured data. Results show that there is moderate enthusiasm about the new possibilities of BDA such as better decision making and better market segmentation. This implies the necessity for easy to use tools and training on the side of the financial advisor as well as on the client's side. The paper fills a research gap by analysing the actual impact of BDA on a real company and trying to measure the value of BDA for a financial organisation in form of improved customer relationship through prescriptive analytics.

Business value of big data analytics: A systems-theoretic approach and empirical test

Although big data analytics have been widely considered a key driver of marketing and innovation processes, whether and how big data analytics create business value has not been fully understood and empirically validated at a large scale. Taking social media analytics as an example, this paper is among the first attempts to theoretically explain and empirically test the market performance impact of big data analytics. Drawing on the systems theory, we explain how and why social media analytics create super-additive value through the synergies in functional complementarity between social media diversity for gathering big data from diverse social media channels and big data analytics for analyzing the gathered big data. Furthermore, we deepen our theorizing by considering the difference between small and medium enterprises (SMEs) and large firms in the required integration effort that enables the synergies of social media diversity and big data analytics. In line with this theorizing, we empirically test the synergistic effect of social media diversity and big data analytics by using a recent large-scale survey data set from 18,816 firms in Italy. We find that social media diversity and big data analytics have a positive interaction effect on market performance, which is more salient for SMEs than for large firms.

The Strategic Value of Big Data Analytics in Health Care Policy-Making

This article describes how the metrics that are used to gauge acceptable versus inadequate care have spurred debates among health care administrators and scholars. Specifically, they argue that the use of readmissions as a quality-of-care metric may reduce patients' safety. Consequently, the new well-intended policies may prove ineffective, or even worse, yield disappointing results. While the discussions over the advantages and disadvantages of the new policies are based more on conjectures rather than on evidence, analytics provides a vehicle to measure the effectiveness of such overarching strategies. In this effort, the authors analyze large volumes of hospital encounters data before and after the implementation of the Patient Protection and Affordable Care Act (PPACA) to show how overlooking some aspects of a problem may lead to unexpected outcomes. The authors conclude that the feedback provided by big data analytics can be used by the government and organization policymakers to obtain a better understanding of loopholes and to propose more effective policies in prospective endeavors.

Smart upstream to LNG

With today’s current technologies, it is possible to answer the question, ‘What is the most profitable mode of operation for the next few hours, for the rest of today, tomorrow and beyond?’ With ‘lower for longer’ oil prices, the need for enterprise wide optimisation in the upstream and midstream oil and gas industry is greater than ever. The terms ‘Digital Oil Field’, ‘Digital Gas Field’, ‘Digital Twin’ are being utilised to extol the virtue and value of big data analytics and model-based asset optimisation and supply chain optimisation by enabling this in efficient ways not previously possible with multiple unintegrated systems, processes and procedures. Multiple different point solutions rolled out across multiple sites become difficult to integrate horizontally for management of safe and optimised operations and vertically up to business enterprise resource planning level to give useful and timely business insights. With careful planning, coupled with deep operational technology and operations knowledge, an integrated enterprise wide supply chain and asset operations and management system can unlock significant hidden value. This is enabled by the correct and efficient integration of many systems and the efficient collection, management and use of huge amounts of data. As the industry needs to ‘do more with less’, getting this integration right is critical to enable desired outcomes and functionality. This paper summarises the current state-of-the-art solutions that enable optimised well to liquefied natural gas plant process, asset and business management to deliver desired functionality and outcomes.

Review on Big Data & Analytics – Concepts, Philosophy, Process and Applications

Abstract Big Data analytics has been the main focus in all the industries today. It is not overstating that if an enterprise is not using Big Data analytics, it will be a stray and incompetent in their businesses against their Big Data enabled competitors. Big Data analytics enables business to take proactive measure and create a competitive edge in their industry by highlighting the business insights from the past data and trends. The main aim of this review article is to quickly view the cutting-edge and state of art work being done in Big Data analytics area by different industries. Since there is an overwhelming interest from many of the academicians, researchers and practitioners, this review would quickly refresh and emphasize on how Big Data analytics can be adopted with available technologies, frameworks, methods and models to exploit the value of Big Data analytics.

Assessing business value of Big Data Analytics in European firms

In the strategic management field, dynamic capabilities (DC) such as organizational agility are considered to be paramount in the search for competitive advantage. Recent research claims that IT business value research needs a more dynamic perspective. In particular, the Big Data Analytics (BDA) value chain remains unexplored. To assess BDA value, a conceptual model is proposed based on a knowledge-based view and DC theories. To empirically test this model, the study addresses a survey to a wide range of 500 European firms and their IT and business executives. Results show that BDA can provide business value to several stages of the value chain. BDA can create organizational agility through knowledge management and its impact on process and competitive advantage. Also, this paper demonstrates that agility can partially mediate the effect between knowledge assets and performance (process level and competitive advantage). The model explains 77.8% of the variation in competitive advantage. The current paper also presents theoretical and practical implications of this study, and the study's limitations.

Tutorial: Big Data Analytics: Concepts, Technologies, and Applications

We have entered the big data era. Organizations are capturing, storing, and analyzing data that has high volume, velocity, and variety and comes from a variety of new sources, including social media, machines, log files, video, text, image, RFID, and GPS. These sources have strained the capabilities of traditional relational database management systems and spawned a host of new technologies, approaches, and platforms. The potential value of big data analytics is great and is clearly established by a growing number of studies. The keys to success with big data analytics include a clear business need, strong committed sponsorship, alignment between the business and IT strategies, a fact-based decision-making culture, a strong data infrastructure, the right analytical tools, and people skilled in the use of analytics. Because of the paradigm shift in the kinds of data being analyzed and how this data is used, big data can be considered to be a new, fourth generation of decision support data management. Though the business value from big data is great, especially for online companies like Google and Facebook, how it is being used is raising significant privacy concerns.

Examining the Impact of Multicollinearity in Discovering Higher-Order Factor Models

We have entered the big data era. Organizations are capturing, storing, and analyzing data that has high volume, velocity, and variety and comes from a variety of new sources, including social media, machines, log files, video, text, image, RFID, and GPS. These sources have strained the capabilities of traditional relational database management systems and spawned a host of new technologies, approaches, and platforms. The potential value of big data analytics is great and is clearly established by a growing number of studies. The keys to success with big data analytics include a clear business need, strong committed sponsorship, alignment between the business and IT strategies, a fact-based decision-making culture, a strong data infrastructure, the right analytical tools, and people skilled in the use of analytics. Because of the paradigm shift in the kinds of data being analyzed and how this data is used, big data can be considered to be a new, fourth generation of decision support data management. Though the business value from big data is great, especially for online companies like Google and Facebook, how it is being used is raising significant privacy concerns.

Big data analytics: turning big data into big money

Unique insights to implement big data analytics and reap big returns to your bottom lineFocusing on the business and financial value of big data analytics, respected technology journalist Frank J. Ohlhorst shares his insights on the newly emerging field of big data analytics in Big Data Analytics. This breakthrough book demonstrates the importance of analytics, defines the processes, highlights the tangible and intangible values and discusses how you can turn a business liability into actionable material that can be used to redefine markets, improve profits and identify new business opportunities. Reveals big data analytics as the next wave for businesses looking for competitive advantageTakes an in-depth look at the financial value of big data analyticsOffers tools and best practices for working with big dataOnce the domain of large on-line retailers such as eBay and Amazon, big data is now accessible by businesses of all sizes and across industries. From how to mine the data your company collects, to the data that is available on the outside, Big Data Analytics shows how you can leverage big data into a key component in your business's growth strategy.