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Watson, Hugh J. "All About Analytics." International Journal of Business Intelligence Research 4, no. 1 (January 2013): 13–28. http://dx.doi.org/10.4018/jbir.2013010102.
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To understand and be successful with analytics, it is important to be precise in understanding what analytics means, the different targets or approaches that companies can take to using analytics, and the drivers that lead to the use of analytics. For companies that use advanced analytics, the keys to success include a clear business need; strong, committed sponsorship; a fact-based decision making culture; a strong data infrastructure; the right analytic tools; and strong analytical personnel in an appropriate organizational structure. These are the same factors for success for business intelligence in general, but there are important nuances when implementing advanced analytics, such as with the data infrastructure, analytical tools, and personnel. Companies like Amazon.com, Overstock.com, Harrah’s Entertainment, and First American Corporation are exemplars that illustrate concepts and best practices.
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Hnatiuk, Yaroslav. "CONTINENTAL ANALYTICS AND ANALYTIC PHILOSOPHY." Visnyk of the Lviv University, no. 54 (2024): 22–37. http://dx.doi.org/10.30970/pps.2024.54.3.
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Oddleifson, Evan. "The Effects of Modern Data Analytics in Electoral Politics." Political Science Undergraduate Review 5, no. 1 (April 1, 2020): 46–52. http://dx.doi.org/10.29173/psur130.
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New implementations of data analytical processes in democratic politics deeply affect voter-representative relationships and constitute a substantive challenge to voter agency. This paper examines the effects of social media driven data analytics on voter microtargeting and electoral politics using Cambridge Analytica’s (CA) involvement in the 2016 US Presidential election and the 2010 Trinidad and Tobago General election. It finds that data-driven voter targeting strategies developed by Cambridge Analytica from 2014-2015 are substantially more effective than previously employed strategies. Moreover, these strategies undermine rational choice and consequently impede a country's ability to conduct democratic politics.
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Kulikowski, Konrad. "Defining analytical skills for human resources analytics: A call for standardization." Journal of Entrepreneurship, Management and Innovation 20, no. 4 (2024): 88–103. https://doi.org/10.7341/20242045.
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PURPOSE: Human resources (HR) analytics systems, powered by big data, AI algorithms, and information technology, are increasingly adopted by organizations to enhance HR’s impact on business performance. However, despite the widespread acknowledgment of the importance of “analytical skills” among HR practitioners in successfully implementing HR analytics systems, the specific nature of these skills remains unclear. This paper aims to address this ambiguity by firstly clarifying the concept of “analytical skills,” secondly identifying skill gaps that may hinder the effective utilization of computer-assisted analytics among HR practitioners, and thirdly advocating for standardization in the understanding of “analytical skills” within the business context, particularly within HR. METHODOLOGY: We examine business “analytical skills” through the theoretical framework of the knowledge, skills, and abilities (KSA) included in the Occupational Information Network (O*NET) content model. Using data from the O*NET database, occupations were classified into Human Resource Management (HRM) and Analytical occupations. Then, we identified the top highly required KSAs in analytical occupations and compared their levels with those of HRM occupations to pinpoint potential gaps hindering the effective utilization of HR analytics. FINDINGS: Using the O*NET database, which describes work and worker characteristics, we establish the highly required analytical KSAs in the business analytics context that might be labeled “analytical skills”. Then, the gap analyses reveal that important analytical KSAs, such as knowledge of sales and marketing, skills in operations analysis, and abilities in mathematical and inductive reasoning, are not expected from HR occupations, creating serious barriers to HR analytics development. In general, we have found that while HR practitioners possess some of the necessary analytical KSAs, they often lack in areas such as mathematics, computers, and complex problem-solving. IMPLICATIONS: Our findings underscore the need for standardization in HR analytics definitions, advocating for the adoption of the O*NET content model as a universal framework for understanding HR analytical knowledge, skills, and abilities (KSAs). By identifying critical analytical KSAs, our research can assist HR departments in improving training, recruitment, and development processes to better integrate HR analytics. Furthermore, we identify significant gaps in analytical skills among HR practitioners, offering potential solutions to bridge these gaps. From a theoretical perspective, our precise definition of HR “analytical skills” in terms of analytic KSAs can enhance research on the effects of HR analytics on organizational performance. This refined understanding can lead to more nuanced and impactful studies, providing deeper insights into how HR analytics contributes to achieving strategic business goals. ORIGINALITY AND VALUE: Our research offers three original insights. First, we establish a standard for HR analyst skills based on the O*NET content model, providing a clear framework for the essential knowledge, skills, and abilities required in HR analytics. Second, we identify significant analytical gaps among HR professionals, highlighting areas that need development and attention. Third, we recognize the necessity for closer cooperation between HR and professional analysts, emphasizing that such collaboration is crucial for maximizing the benefits of computer-assisted HR analytics. These insights ensure that HR analytics can move beyond being a management fad and have a real, lasting impact on business outcomes.
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Hwan (Mark) Lee, Seung. "EDITORIAL: ANALYTICS FOR ANALYTICS." Marketing Education Review 33, no. 3 (July 3, 2023): 177. http://dx.doi.org/10.1080/10528008.2023.2238495.
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Ponkin, I. V. "School of Practical Analytics: Analytical writing." Voprosy kul'turologii (Issues of Cultural Studies), no. 5 (May 29, 2023): 384–92. http://dx.doi.org/10.33920/nik-01-2305-03.
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This article is devoted to explaining the concept and meaning of applied analytical writing. The author outlines the requirements for analytical writing and explains some of them, including: analytical metacognitive distance; analytical insight; analytics maturity; evaluative and interpretive certainty; analytical fluency; analytical flexibility; analytical originality, analytical precision.
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Handfield, Robert, Seongkyoon Jeong, and Thomas Choi. "Emerging procurement technology: data analytics and cognitive analytics." International Journal of Physical Distribution & Logistics Management 49, no. 10 (December 10, 2019): 972–1002. http://dx.doi.org/10.1108/ijpdlm-11-2017-0348.
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Purpose The purpose of this paper is to elucidate the emerging landscape of procurement analytics. This paper focuses on the following questions: what are the current and future state of procurement analytics?; what changes in the procurement process will be required to enable integration of analytical solutions?; and what future areas of research arise when considering the future state of procurement analytics? Design/methodology/approach This paper employs a qualitative approach that relies on three sources of information: executive interviews, a review of current and emerging technology platforms and a small survey of subject matter experts in the field. Findings The procurement analytics landscape developed in this research suggests that the authors will continue to see major shifts in the sourcing and supply chain technology environment in the next five years. However, there currently exists a low usage of advanced procurement analytics, and data integrity and quality issues are preventing significant advances in analytics. This study identifies the need for organizations to establish a coherent approach to collection and storage of trusted organizational data that build on internal sources of spend analysis and contract databases. In addition, current ad hoc approaches to capturing unstructured data must be replaced by a systematic data governance strategy. An important element for organizations in this evolution is managing change and the need to nourish an analytic culture. Originality/value While the majority of forward-looking research and reports merely project broad technological impact of cognitive analytics and big data, much of it does not provide specific insights into functional impacts such as the impact on procurement. The analysis of this study provides us with a clear view of the potential for business analytics and cognitive analytics to be employed in procurement processes, and contributes to development of related research topics for future study. In addition, this study suggests detailed implementation strategies of emerging procurement technologies, contributing to the existing body of the literature and industry reports.
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Husamaldin, Laden, and Nagham Saeed. "Big Data Analytics Correlation Taxonomy." Information 11, no. 1 (December 25, 2019): 17. http://dx.doi.org/10.3390/info11010017.
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Big data analytics (BDA) is an increasingly popular research area for both organisations and academia due to its usefulness in facilitating human understanding and communication. In the literature, researchers have focused on classifying big data according to data type, data security or level of difficulty, and many research papers reveal that there is a lack of information on evidence of a real-world link of big data analytics methods and its associated techniques. Thus, many organisations are still struggling to realise the actual value of big data analytic methods and its associated techniques. Therefore, this paper gives a design research account for formulating and proposing a step ahead to understand the relation between the analytical methods and its associated techniques. Furthermore, this paper is an attempt to clarify this uncertainty and identify the difference between analytics methods and techniques by giving clear definitions for each method and its associated techniques to integrate them later in a new correlation taxonomy based on the research approaches. Thus, the primary outcome of this research is to achieve for the first time a correlation taxonomy combining analytic methods used for big data and its recommended techniques that are compatible for various sectors. This investigation was done through studying various descriptive articles of big data analytics methods and its associated techniques in different industries.
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McMullen, Anthony. "Google Analytics http://www.google.com/analytics/." Public Services Quarterly 6, no. 1 (February 8, 2010): 21–22. http://dx.doi.org/10.1080/15228950903517415.
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Köhrle, Josef, and Keith H. Richards. "Mass Spectrometry-Based Determination of Thyroid Hormones and Their Metabolites in Endocrine Diagnostics and Biomedical Research – Implications for Human Serum Diagnostics." Experimental and Clinical Endocrinology & Diabetes 128, no. 06/07 (June 2020): 358–74. http://dx.doi.org/10.1055/a-1175-4610.
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AbstractThe wide spectrum of novel applications for the LC-MS/MS-based analysis of thyroid hormone metabolites (THM) in blood samples and other biological specimen highlights the perspectives of this novel technology. However, thorough development of pre-analytical sample workup and careful validation of both pre-analytics and LC-MS/MS analytics, is needed, to allow for quantitative detection of the thyronome, which spans a broad concentration range in these biological samples.This minireview summarizes recent developments in advancing LC-MS/MS-based analytics and measurement of total concentrations of THM in blood specimen of humans, methods in part further refined in the context of previous achievements analyzing samples derived from cell-culture or tissues. Challenges and solutions to tackle efficient pre-analytic sample extraction and elimination of matrix interferences are compared. Options for automatization of pre-analytic sample-preparation and comprehensive coverage of the wide thyronome concentration range are presented. Conventional immunoassay versus LC-MS/MS-based determination of total and free THM concentrations are briefly compared.
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Bokatenko, I. Yu, and N. V. Sidorov. "HR Analytics, People Analytics, Workforce Analytics and Talent Analytics. What is the difference between them." Normirovanie i oplata truda v promyshlennosti (Rationing and remuneration of labor in industry), no. 8 (August 1, 2021): 63–68. http://dx.doi.org/10.33920/pro-3-2108-06.
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This article presents the results of a study aimed at analyzing the concepts in the field of human resource management. An attempt is made to identify and establish differences between the concepts of HR Analytics, People Analytics, Workforce Analytics and Talent Analytics. The aim of the work is to identify the differences between modern concepts in human resource management. The relevance of this work is based on the emergence of a large number of similar concepts in the field of personnel management, which many employees and employers use as synonyms. When preparing the scientific article, the following tasks were set and solved: the essence of the concepts of HR Analytics, People Analytics, Workforce Analytics and Talent Analytics was investigated, the relationship between these concepts was analyzed, the dynamics of people»s interest in the concepts of HR Analytics, People Analytics, Workforce Analytics, Talent Analytics was revealed, and the differences between these concepts were established.
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Wang, Shouhong, and Hai Wang. "Knowledge Analytics." International Journal of Business Analytics 7, no. 4 (October 2020): 14–23. http://dx.doi.org/10.4018/ijban.2020100102.
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Big data has raised challenges and opportunities for the education sector. Educational analytics encompass a variety of computational techniques to process educational big data for effective teaching, learning, research, service, and administrative decision making. Learning analytics and academic analytics have been widely discussed in the literature of education; however, knowledge analytics have not been discussed in the educational analytics field. Knowledge analytics are a relatively new subject in the knowledge management area. Knowledge analytics lie outside of the definitions of learning analytics and academic analytics, and encompass analytical activities for knowledge management among educators in teaching, research, and services. This paper discusses potential applications of knowledge analytics in educational institutions and issues related to implementation of knowledge analytics in the educational environment.
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Kumar, S. Senthil, and Ms V. Kirthika. "Big Data Analytics Architecture and Challenges, Issues of Big Data Analytics." International Journal of Trend in Scientific Research and Development Volume-1, Issue-6 (October 31, 2017): 669–73. http://dx.doi.org/10.31142/ijtsrd4673.
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Drye, Tim. "Applied predictive analytics and developing analytical talent." Journal of Direct, Data and Digital Marketing Practice 16, no. 1 (July 2014): 66–67. http://dx.doi.org/10.1057/dddmp.2014.39.
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LINSCHEID, M. "ChemInform Abstract: Analytical Chemistry: Organic Analytics 1990." ChemInform 22, no. 20 (August 23, 2010): no. http://dx.doi.org/10.1002/chin.199120325.
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Dai, Tinglong, Kelly Gleason, Chao‐Wei Hwang, and Patricia Davidson. "Heart analytics: Analytical modeling of cardiovascular care." Naval Research Logistics (NRL) 68, no. 1 (November 27, 2019): 30–43. http://dx.doi.org/10.1002/nav.21880.
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Mirzaalian, Farshid, and Elizabeth Halpenny. "Social media analytics in hospitality and tourism." Journal of Hospitality and Tourism Technology 10, no. 4 (October 28, 2019): 764–90. http://dx.doi.org/10.1108/jhtt-08-2018-0078.
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Purpose The purpose of this paper is to provide a review of hospitality and tourism studies that have used social media analytics to collect, examine, summarize and interpret “big data” derived from social media. It proposes improved approaches by documenting past and current analytic practice addressed by the selected studies in social media analytics. Design/methodology/approach Studies from the past 18 years were identified and collected from five international electronic bibliographic databases. Social media analytics-related terms and keywords in the titles, keywords or abstracts were used to identify relevant articles. Book chapters, conference papers and articles not written in English were excluded from analysis. The preferred reporting items for systematic reviews and meta-analyses (PRISMA) guided the search, and Stieglitz and Dang-Xuan’s (2013) social media analytics framework was adapted to categorize methods reported in each article. Findings The research purpose of each study was identified and categorized to better understand the questions social media analytics were being used to address, as well as the frequency of each method’s use. Since 2014, rapid growth of social media analytics was observed, along with an expanded use of multiple analytic methods, including accuracy testing. These factors suggest an increased commitment to and competency in conducting comprehensive and robust social media data analyses. Improved use of methods such as social network analysis, comparative analysis and trend analysis is recommended. Consumer-review networks and social networking sites were the main social media platforms from which data were gathered; simultaneous analysis of multi-platform/sources of data is recommended to improve validity and comprehensive understanding. Originality/value This is the first systematic literature review of the application of social media analytics in hospitality and tourism research. The study highlights advancements in social media analytics and recommends an expansion of approaches; common analytical methods such as text analysis and sentiment analysis should be supplemented by infrequently used approaches such as comparative analysis and spatial analysis.
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Chiaburu, Dan S. "Analytics." Journal of Management Inquiry 25, no. 1 (August 24, 2015): 111–15. http://dx.doi.org/10.1177/1056492615601342.
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Chinchor, N. A., J. J. Thomas, P. C. Wong, M. G. Christel, and W. Ribarsky. "Multimedia Analysis + Visual Analytics = Multimedia Analytics." IEEE Computer Graphics and Applications 30, no. 5 (September 2010): 52–60. http://dx.doi.org/10.1109/mcg.2010.92.
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Duan, Lian, and Ye Xiong. "Big data analytics and business analytics." Journal of Management Analytics 2, no. 1 (January 2, 2015): 1–21. http://dx.doi.org/10.1080/23270012.2015.1020891.
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., Yogesh R. Jadhav. "GAP-ANALYTICS (GEOLOCATION AND PLACES-ANALYTICS)." International Journal of Research in Engineering and Technology 05, no. 01 (January 25, 2016): 39–43. http://dx.doi.org/10.15623/ijret.2016.0501007.
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Thomas, Deepa Mary. "A Survey on the Evolution of Data Analytics and the Future in Data Science." Volume 5 - 2020, Issue 8 - August 5, no. 8 (September 17, 2020): 1699–703. http://dx.doi.org/10.38124/ijisrt20aug827.
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This paper cornerstone on the concepts of Data Analytics, different types of Data Analytics and the evolution of Data Analytics into the latest Prescriptive Data Analytic model. I hope this paper will be very helpful for everyone who wants to get a clear idea about the concepts of Data Analysis and the Data Analytics
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Васильева, Наталья Витальевна, and Людмила Андреевна Селиванова. "Predictive Analytics as Essential Level in Analytical Hierarchy Process." ЖУРНАЛ ПРАВОВЫХ И ЭКОНОМИЧЕСКИХ ИССЛЕДОВАНИЙ, no. 4 (December 15, 2021): 159–62. http://dx.doi.org/10.26163/gief.2021.61.13.023.
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В статье обоснована необходимость внедрения предиктивной аналитики, которая является современным трендом развития компаний. Описаны ключевые составляющие процесса предиктивной аналитики. Дается оценка результатов внедрения предиктивной аналитики в некоторых областях национальной экономики. We substantiate the need to introduce predictive analytics that is seen as a modern trend of business development. We describe the key components of predictive analytics process. The results of the introduction of predictive analytics in certain areas of national economy are assessed.
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Shahril Khuzairi, Nur Maisarah, Manjit Singh Sidhu, and Zaihisma Che Cob. "Learning Analytics and Teaching Analytics: The Similarities and Differences." International Journal of Humanities, Management and Social Science 3, no. 2 (December 17, 2020): 52–58. http://dx.doi.org/10.36079/lamintang.ij-humass-0302.135.
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Analytics in education which constitutes of Learning Analytics and Teaching Analytics arouses great attention among researchers and practitioners in the current climate. The use of analytics in education enables educational data to be collected and analysed to serve the needs of all stakeholders to improve the educational process. The present paper gives an overview of Learning Analytics and Teaching Analytics and explores its similarities and differences, as well as the confusion that has been raised between the two defined terms. Alongside, the analytics selection flowchart presented in this paper provides a breakdown on the analytics research direction for Learning Analytics and Teaching Analytics. A deeper and varied understanding of Learning Analytics and Teaching Analytics is imperative for establishing effective and accurate analytical tools alongside with recommendations for improvement in the future.
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Iyer, Lakshmi S., and Rajeshwari M. Raman. "Intelligent Analytics." International Journal of Business Intelligence Research 2, no. 1 (January 2011): 31–45. http://dx.doi.org/10.4018/jbir.2011010103.
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Organizations use web analytic tools and technologies to measure, collect, analyze, and report web usage data to help optimize websites. Traditionally, most of this data tends to be non-transactional and non-identifiable. In this regard, there has not been much integration with transactional data that is collected, stored, analyzed, and reported through Business Intelligence (BI). Emerging trends in web analytics provide organizations the ability to aggregate and analyze web analytics data with transactional data to provide valuable insights for building better customer relationship strategies. In this paper, the authors give an overview of web analytics tools, key players, new technology trends and capabilities to integrate web analytics with BI so organizations can leverage intelligent analytics for new marketing initiatives. While the benefits are significant, there are some challenges associated with the integration and a few possible solutions to address.
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Faiyaad, Chisis Mohammed, and Abayomi Bin Hakim Sadiki. "How healthcare industry in Arabs can use data science for sustainable healthcare practices." Business & IT XII, no. 1 (2022): 184–92. http://dx.doi.org/10.14311/bit.2022.01.22.
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To date, the healthcare business hasn't fully understood the prospective bene?ts to be acquired from big data analytics. Although the continuously growing body of academic investigation on large data analytics is mainly technology-oriented, a clear understanding of the strategic implications of big data is urgently needed. To handle the absence, this particular analysis examines the historical development, architectural style, and portion functionalities of big data analytics. From content evaluation of twenty six BDA implementation instances in healthcare, we could determine five big data analytics capabilities: analytical capability for patterns of attention, unstructured details, analytical capability, choice support capability, predictive capability, then traceability. We additionally mapped the benefits driven by big data analytics in terminology of info technology infrastructure, organizational, operational, strategic and managerial locations. Additionally, we recommend five approaches for healthcare organizations contemplating adopting big data analytics solutions. Our findings will help healthcare organizations understand the big data analytics capabilities and potential benefits and support them in drafting more effective data-driven analytics strategies.
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Thirathon, Usarat, Bernhard Wieder, and Maria-Luise Ossimitz. "Determinants of analytics-based managerial decisionmaking." International Journal of Information Systems and Project Management 6, no. 1 (January 31, 2022): 27–40. http://dx.doi.org/10.12821/ijispm060102.
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This study investigates how managerial decision-making is influenced by Big Data analytics, analysts’ interaction skills and quantitative skills of senior and middle managers. The results of a cross-sectional survey of senior IT managers reveal that Big Data analytics (BDA) creates an incentive for managers to base more of their decisions on analytic insights. However, we also find that interaction skills of analysts and – even more so – managers’ quantitative skills are stronger drivers of analytics-based decision-making. Finally, our analysis reveals that, contrary to mainstream perceptions, managers in smaller organizations are more capable in terms of quantitative skills, and they are significantly more likely to base their decisions on analytics than managers in large organizations. Considering the important role of managers’ quantitative skills in leveraging analytic decision support, our findings suggest that smaller firms may owe some of their analytic advantages to the fact that they have managers who are closer to their analysts – and analytics more generally.
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Houtmeyers, Kobe C., Arne Jaspers, and Pedro Figueiredo. "Managing the Training Process in Elite Sports: From Descriptive to Prescriptive Data Analytics." International Journal of Sports Physiology and Performance 16, no. 11 (November 1, 2021): 1719–23. http://dx.doi.org/10.1123/ijspp.2020-0958.
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Elite sport practitioners increasingly use data to support training process decisions related to athletes’ health and performance. A careful application of data analytics is essential to gain valuable insights and recommendations that can guide decision making. In business organizations, data analytics are developed based on conceptual data analytics frameworks. The translation of such a framework to elite sport may benefit the use of data to support training process decisions. Purpose: The authors aim to present and discuss a conceptual data analytics framework, based on a taxonomy used in business analytics literature to help develop data analytics within elite sport organizations. Conclusions: The presented framework consists of 4 analytical steps structured by value and difficulty/complexity. While descriptive (step 1) and diagnostic analytics (step 2) focus on understanding the past training process, predictive (step 3) and prescriptive analytics (step 4) provide more guidance in planning the future. Although descriptive, diagnostic, and predictive analytics generate insights to inform decisions, prescriptive analytics can be used to drive decisions. However, the application of this type of advanced analytics is still challenging in elite sport. Thus, the current use of data in elite sport is more focused on informing decisions rather than driving them. The presented conceptual framework may help practitioners develop their analytical reasoning by providing new insights and guidance and may stimulate future collaborations between practitioners, researchers, and analytics experts.
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Kurniawan, Candra. "A Survey on Big Data Analytics Model." ITEJ (Information Technology Engineering Journals) 4, no. 1 (July 22, 2019): 1–13. http://dx.doi.org/10.24235/itej.v4i1.46.
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Topic about big data analytics have received a lot of attention and interest at this time. There are many topics can be discussed related to the analytical model, tools, and technology used. Big data analytics model involves many processes with various technologies used. Skills in handling big data, extracting mining, and developing insight are needed in applying big data analytics. Suitable analytical hardware and software also needed in decision making. Big data analytics is a key to a business strategy, but only a small portion of big data is currently used to support their business strategy. Big data analitycs can answer many questions about how to manage costs, time, and development or optimization strategies, and other decision making choices. However, there are many challenges in big data analytics technology. This survey paper addresses topics related to the analytical model, tools, and technology used. This paper also discusses the application of big data analytics in various fields.
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Sharma, Manu, and Sudhanshu Joshi. "Online Advertisement Using Web Analytics Software." International Journal of Business Analytics 7, no. 2 (April 2020): 13–33. http://dx.doi.org/10.4018/ijban.2020040102.
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This article describes a analytic-hierarchy-process (AHP) application to identify and evaluate the best online advertising analytics software. This technique is multi-criteria and used in this study by comparing the top four web advertising analytics software. AHP uses pair-wise comparison of matrices. There are six criteria identified for evaluation: Ad scheduling, ad targeting, creative banner rotation, features, performance, cost and for each criterion, a matrix of pair-wise comparison with web-analytics software i.e. Google analytics, Accenture Analytics, Funnel and, Moat Analytics were evaluated. AHP is an effective method for multi-objective decision-making, and optimization. Thus, it helps web advertisers to evaluate the existing web advertising analytics software for posting their web advertisements.
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Chahal, Ayushi, Preeti Gulia, and Nasib Singh Gill. "Different analytical frameworks and bigdata model for internet of things." Indonesian Journal of Electrical Engineering and Computer Science 25, no. 2 (February 1, 2022): 1159. http://dx.doi.org/10.11591/ijeecs.v25.i2.pp1159-1166.
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Sensor devices used in internet of things (IoT) enabled environment produce large amount of data. This data plays a major role in bigdata landscape. In recent years, correlation, and implementation of bigdata and IoT is being extrapolated. Nowadays, predictive analytics is gaining attention of many researchers for big IoT data analytics. This paper summarizes different sort of IoT analytical platforms which consist in-built features for further use in machine learning, MATLAB, and data security. It emphasizes on different machine learning algorithms that plays important role in big IoT data analytics. Besides different analytical frameworks, this paper highlights the proposed model for bigdata in IoT domain and elaborates different forms of data analytical methods. Proposed model comprises different phases i.e., data storing, data cleaning, data analytics, and data visualization. These phases cover the basic characteristics of bigdata V’s model and most important phase is data analytics or big IoT analytics. This model is implemented using an IoT dataset and results are presented in graphical and tabular form using different machine learning techniques. This study enhances researchers’ knowledge about various IoT analytical platforms and usability of these platforms in their respective problem domains.
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Varenko, Volodymyr. "OPERATIONAL (ONLINE) ANALYTICS: REALITIES AND PROSPECTS." Scientific journal “Library Science. Record Studies. Informology”, no. 1 (April 23, 2021): 35–41. http://dx.doi.org/10.32461/2409-9805.1.2021.229852.
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The goal of the work. The scientific problem is covered in order to systematize, generalize new knowledge about modern analytical technologies in terms of the present and prospects for their development. The research scientific novelty of the work lies in the generalization and systematization of new knowledge about operational analytics in terms of the present and prospects for its development within one study. Conclusions. The article highlights the current views of researchers on operational analytics and its information technology - Big Data technology. The works of famous experts - Bill Franks, Tom Davenport and Joanna Harris, Victor Mayer-Schoenberger and Kenneth Cookier, Karl Anderson, Brian Clifton, Sergei Kovalev, Oleg Veres - are analyzed.Their modern views on operational analytics formed the basis of the presentation of the main material. It is emphasized that we have an objective process of society development. And it depends on our choice whether we will have a competitive advantage at the start. The essence of operational analytics, its advantages and possibilities of application in the Ukrainian realities are described. In addition, the American electronic corporation IBM («Blue Giant»), which in the «Analytics» section offers seven categories of analytical products that will satisfy the most demanding consumers because they meet the world’s standards. The paper focuses on the fact that in modern management, operational analytics is carrying out an «electronic revolution». Ittakes analytics beyond the traditional limits of application. It takes analytics beyond the traditional limits of application. It turned out that we have a qualitatively new tool, a new level of evolution of analytical technologies. At the same time, it is emphasized that traditional analytical methods and technologies should not be thoughtlessly rejected, as they are basic, ie the foundation on which operational analytics is built and maintained. Emphasis is placed on the fact that operational analytics opens wide possibilities for integration with various sources of information (for example, the Internet of Things) through appropriate applications, communications and electronics, while providing a single, joint provision and presentation of informationbased on Big Data.Keywords: operational analytics, technology, management decision.
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Vorobets, Yevhen, Alona Khmeliuk, Olena Moshkovska, Vali Isa Valiyev, and Oksana Marukhlenko. "THE ROLE OF DATA ANALYTICS IN MAKING MANAGEMENT DECISIONS BY THE LOGISTICS INTERMEDIARIES." Financial and credit activity problems of theory and practice 4, no. 57 (August 31, 2024): 185–96. http://dx.doi.org/10.55643/fcaptp.4.57.2024.4422.
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Data analytics plays a crucial role in increasing the effectiveness of management decisions by the logistics of intermediaries. The aim of the article is to identify the extent to which the quality of data analytics affects the effectiveness of decision-making in the logistics intermediaries, in particular, the speed of delivery of the studied companies.The study employed regression and correlation analysis to identify key influencing factors in terms of data analytics on the effectiveness of management decisions of the logistics intermediaries. The significance of investment in the qualification of analysts (with a coefficient of -1.6754), analytical tools (with a coefficient of -1.2575), and integration of analytics in decision-making processes (with a coefficient of -3.2511) directly affect the reduction of delivery time.It is emphasized that each analytical project contributes to the reduction of delivery time by 0.48 hours. Correlation analysis confirmed the relationship between the efficiency of logistics and the level of qualification of analysts (-0.283617), investment in analytical tools (-0.257322), the number of analytical projects (-0.343792), the level of integration of analytics (-0.712058). The strongest correlation was observed for the integration of analytics in management decision-making.It is recommended to focus on the development of analytical competencies, increase of investment in tools, intensification of projects, and integration of analytics in strategic management. Further research is planned on the use of artificial intelligence to optimize management decisions in logistics as part of ensuring the company’s sustainable development.
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Godbless Ocran, Samuel Omokhafe Yusuf, Peprah Owusu, Enis Agyeman Boateng, Sylvester Obeng Krampah, and Adedamola Hadassah Paul-Adeleye. "AI-driven business analytics for SMES: Unlocking value through predictive and prescriptive analytic." International Journal of Science and Research Archive 13, no. 1 (October 30, 2024): 3009–22. http://dx.doi.org/10.30574/ijsra.2024.13.1.2001.
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Introduction: With an emphasis on predictive and prescriptive analytics, this study examines the revolutionary implications of AI-driven analytics on small and medium-sized organizations (SMEs). SMEs play a crucial role in the global economy and require advanced solutions to improve decision-making and operational efficiency. The research aims to explore how AI-powered analytics, particularly in predictive and prescriptive forms, can add value to SMEs by enhancing demand forecasting, customer behavior insights, and financial planning. To determine how AI-driven analytics might affect SMEs, a thorough assessment of the literature was undertaken. The study reveals that SMEs implementing predictive analytics experience notable improvements in areas such as inventory management, revenue generation, and overall operational efficiency. Furthermore, businesses that leverage prescriptive analytics benefit from optimized resource allocation, enhanced marketing strategies, and better risk management practices. These findings highlight the potential for AI to overcome key challenges faced by SMEs, including budget constraints and limited data availability. AI-driven analytics can provide valuable insights that allow SMEs to streamline operations and foster growth. With future trends pointing to greater accessibility and developments in machine learning, natural language processing, and the integration of AI with other cutting-edge technologies, like blockchain, AI-powered analytics offers substantial prospects for small and medium-sized enterprises.
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Voronova, E. Yu, and A. A. Vekshina. "Improving the organization of cost analysis in a digital environment." Economics and Management 29, no. 2 (March 7, 2023): 141–49. http://dx.doi.org/10.35854/1998-1627-2023-2-141-149.
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Aim. The presented study aims to substantiate the need to use digital technologies in analytics to make effective management decisions and ensure cost reduction.Tasks. The authors determine the place of a digital analytics platform in cost analysis; identify key factors in the implementation of the platform; describe the capabilities of the tools provided by the analytics platform and how they can be used in the cost management process.Methods. This study uses general scientific methods; the results are summarized through tabular and graphical presentation; four groups of data analysis methods (descriptive and diagnostic analytics, predictive and prescriptive analytics) are identified; within each group, special methods of cost analysis (management analysis methods and advanced analytics methods) are indicated, which can be automated using an analytics platform.Results. It is found that the introduction of an analytics platform transforms the structure of time costs associated with performing analytical work, freeing up working time for solving other management tasks. It is established that analytics platforms are flexible and can find highly specialized solutions due to a wide range of tools and methods implemented on the basis of the platform. The selection of tools and methods depends on the tasks set as part of the cost analysis. Acceptable options for using advanced analytics tools within the framework of cost management are formulated.Conclusions. The analytical platform serves as a tool that improves the quality of data processing necessary for making effective management decisions. Comprehensive application of traditional cost analysis tools and advanced analytics facilitates the effective search for new opportunities for optimizing business processes, which is an urgent task in the context of uncertainty and limited resources. Considering the importance of the functions performed by the tools of the analytics platform and in connection with the withdrawal of foreign leaders in the field of business analytics from the Russian market due to sanctions, domestic vendors of analytical systems are faced with the challenge of further improving software products that should be on par with their foreign analogs in terms of functionality.
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ZENKINA, Irina V. "Business analysis and business analytics: Development in the context of digitalization." Economic Analysis: Theory and Practice 22, no. 4 (April 27, 2023): 646–71. http://dx.doi.org/10.24891/ea.22.4.646.
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Subject. The article addresses the analytical support for change management in conditions of orientation of economic entities to sustainable development and digital transformation of business. Objectives. The purpose is to identify modern features of business analysis, the content of the main types of business analytics, and the impact of digitalization processes on analytical activities development. Methods. The study draws on research methods, like analysis, synthesis, comparison, generalization, abstraction, systemic, strategic, and risk-oriented approaches. Results. The paper reveals the content, areas and tasks of business analysis in accordance with the standard of business analysis adopted by the International Institute of Business Analysis in November 2022. It assesses innovations in the field of regulation of business analysis at the global level; defines the specifics of business analysis and underpins prerequisites for its improvement under new challenges and large-scale digitalization; considers the interrelation of business analysis and business analytics, presents the systematization of the main types of analytics. Conclusions. The analysis of trends in the development of analytical activities of business entities enabled to reflect priority areas of business analytics, and perform their comparative assessment. The paper unveiled the emerging trend of divergence of business analysis and business analytics as separate types of analytical activity, and identified Business Intelligence, Big Data Analytics, and Data Science as the most promising areas of business analytics.
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Cho, Minseok, Michael Cottingham, Billy Hawkins, and Don Lee. "Sport Analytics Business: Exploring Fan Engagement on Analytical Content." IJASS(International Journal of Applied Sports Sciences) 35, no. 2 (December 31, 2023): 201–14. http://dx.doi.org/10.24985/ijass.2023.35.2.201.
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With the emerging trend of technological advancement in sport media production, sport analytics has provided fan-oriented game contents through multimedia platforms. This study aims to empirically investigate sport fans’ perceptions toward sport analytics contents (i.e., statistics, video, data visualization, decision-aid) by exploring their impact on fan engagement (i.e., information, entertainment, personal identity, social interaction) and employing the uses and gratifications theory. Using multiple regressions, this study found that sport fans are more likely to consume statistics and video contents for information search and entertainment. Moreover, data visualization and decision-aid contents showed a positive impact on subjects’ personal identity and social interaction. The present study highlights key aspects of sport analytics in its application to sport media production as well as sport management at large.
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Yadav, Neeta, and Neelendra Badal. "Analytical Study of Data Analytics and its Challenges." International Journal of Computer Applications 186, no. 44 (October 25, 2024): 43–46. http://dx.doi.org/10.5120/ijca2024924081.
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Papadaki, Maria-Evangelia, Nicolas Spyratos, and Yannis Tzitzikas. "Towards Interactive Analytics over RDF Graphs." Algorithms 14, no. 2 (January 25, 2021): 34. http://dx.doi.org/10.3390/a14020034.
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The continuous accumulation of multi-dimensional data and the development of Semantic Web and Linked Data published in the Resource Description Framework (RDF) bring new requirements for data analytics tools. Such tools should take into account the special features of RDF graphs, exploit the semantics of RDF and support flexible aggregate queries. In this paper, we present an approach for applying analytics to RDF data based on a high-level functional query language, called HIFUN. According to that language, each analytical query is considered to be a well-formed expression of a functional algebra and its definition is independent of the nature and structure of the data. In this paper, we investigate how HIFUN can be used for easing the formulation of analytic queries over RDF data. We detail the applicability of HIFUN over RDF, as well as the transformations of data that may be required, we introduce the translation rules of HIFUN queries to SPARQL and we describe a first implementation of the proposed model.
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Attaran, Mohsen, and Sharmin Attaran. "The Rise of Embedded Analytics." International Journal of Business Intelligence Research 9, no. 1 (January 2018): 16–37. http://dx.doi.org/10.4018/ijbir.2018010102.
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This article describes how in today's hyper-competitive environment, business leaders around the world are using analytics technologies to create business values, and to gain a better understanding of their customer's needs and wants. However, traditional business analytics are undergoing major changes. The Internet revolution, cloud computing, and the evolution to self-service analytics have all contributed to the changing dimensions of business intelligence. To compete effectively in a digitally driven world, business leaders must understand and address the critical shifts taking place in the field of analytics, and how these shifts impact their overall strategy. The key objective of this article is to propose a conceptual model for successful implementation of Embedded Analytics in organizations. This article also covers some of the potential benefits of analytics, explores the changing dimensions of analytics, and provides a guide to some of the opportunities that are available for using embedded analytics in business. Furthermore, this study reviews key attributes of a successful modern analytics platform and illustrates how to overcome some of the key challenges of incorporating embedded analytics into an analytic strategy in business. Finally, this article highlights successful implementation of analytics solutions in manufacturing and service industry.
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Brock, Timothy R. "Performance Analytics: The Missing Big Data Link Between Learning Analytics and Business Analytics." Performance Improvement 56, no. 7 (August 2017): 6–16. http://dx.doi.org/10.1002/pfi.21701.
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Wise, Alyssa, Yuting Zhao, and Simone Hausknecht. "Learning Analytics for Online Discussions: Embedded and Extracted Approaches." Journal of Learning Analytics 1, no. 2 (August 7, 2014): 48–71. http://dx.doi.org/10.18608/jla.2014.12.4.
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This paper describes an application of learning analytics that builds on an existing research program investigating how students contribute and attend to the messages of others in asynchronous online discussions. We first overview the E-Listening research program and then explain how this work was translated into analytics that students and instructors could use to reflect on their discussion participation. Two kinds of analytics were designed: some embedded in the learning environment to provide students with real-time information on their activity in-progress; and some extracted from the learning environment and presented to students in a separate digital space for reflection. In addition, we describe the design of an intervention though which use of the analytics can be introduced as an integral course activity. Findings from an initial implementation of the application indicated that the learning analytics intervention supported changes in students’ discussion participation. Five issues for future work on learning analytics in online discussions are presented. One, unintentional versus purposeful change; two, differing changes prompted by the same analytic; three, importance of theoretical buy-in and calculation transparency for perceived analytic value; four, affective components of students’ reactions; and five, support for students in the process of enacting analytics-driven changes.
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Agrawal, Deepak. "Analytics based decision making." Journal of Indian Business Research 6, no. 4 (November 11, 2014): 332–40. http://dx.doi.org/10.1108/jibr-09-2014-0062.
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Purpose – This paper aims to trace the history, application areas and users of Classical Analytics and Big Data Analytics. Design/methodology/approach – The paper discusses different types of Classical and Big Data Analytical techniques and application areas from the early days to present day. Findings – Businesses can benefit from a deeper understanding of Classical and Big Data Analytics to make better and more informed decisions. Originality/value – This is a historical perspective from the early days of analytics to present day use of analytics.
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Lemsa, Santa. "READINESS OF LATVIA’S ORGANIZATIONS FOR ADVANCED ANALYTICS." ENVIRONMENT. TECHNOLOGIES. RESOURCES. Proceedings of the International Scientific and Practical Conference 2 (June 13, 2023): 61–66. http://dx.doi.org/10.17770/etr2023vol2.7256.
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The advanced analytics is one of the core tools to provide competitive advantage, sustainable development and foster productivity of the organization. Digital transformation and advanced analytics are two key trends in the emerging age of data, analytics, and automation. Digital transformation is the process of transforming how businesses operate when faced with digital disruption. Advanced analytics is the application of predictive and prescriptive models to analyse large, complex datasets in order to make critical business decisions. The focus of the paper is to assess the maturity level of advanced analytics in the organizations of Latvia by region, size and industry. Assessment was done by several domains like Organization, People, Data, Analytics, Technologies. The quantitative online survey was performed to assess the readiness of Latvia’s organizations for advanced analytics. The questionnaire was developed based on an academic literature review, reports and publications by researchers, analytical sector, industry experts and Author’s professionals experience in advanced analytics industry. The overall readiness level of Latvia’s organizations is 2.4 in 5 points scale. It differs by region, size of the organization and industry. Most of organizations do not have Analytics strategy, majority use spreadsheets based analytical tools, half of organizations use mostly only internal data, more than third part of organizations do not have any analytical resources. It leads to conclusion that majority of Latvia’s organizations are far from ability to improve productivity, be able to maximize the potential of the digital environment, to exploit data to make data-driven and automated decisions and are far from 21st century digital opportunities. Thus, puts under danger the sustainability of the organizations itself.
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Pechenizkiy, Mykola, and Dragan Gasevic. "Introduction into Sparks of the Learning Analytics Future." Journal of Learning Analytics 1, no. 3 (February 6, 2015): 145–49. http://dx.doi.org/10.18608/jla.2014.13.8.
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This section offers a compilation of 16 extended abstracts summarizing research of the doctoral students who participated in the Second Learning Analytics Summer Institute (LASI 2014) held at Harvard University in July 2014. The abstracts highlight the motivation, main goals and expected contributions to the field from the ongoing learning analytics doctoral research around the globe. These works cover several major topics in learning analytics including novel methods for automated annotations, longitudinal analytic studies, networking analytics, multi-modal analytics, dashboards, and data-driven feedback and personalization. The assumed settings include the traditional classroom, online and mobile learning, blended learning, and massive open online course education models.
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Zheng, Nan, Meng Sun, and Ye Yang. "Visual Analysis of College Sports Performance Based on Multimodal Knowledge Graph Optimization Neural Network." Computational Intelligence and Neuroscience 2022 (July 1, 2022): 1–12. http://dx.doi.org/10.1155/2022/5398932.
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In this paper, through data analysis of multimodal knowledge graph optimized neural network and visual analysis of college students’ sports performance, we use huge graph, a graph database supporting distributed storage, to store domain knowledge in the form of the knowledge graph, use Spring Boot to build a server-side framework, use Vue framework combined with vis.js to visualize relational network graphs, and design and implement a knowledge-oriented. This paper proposes a visual analytics system based on the theory of visual analytics. Based on the idea of visual analytics, this paper presents a visual analytics framework combining predictive models. This framework combines the automated analysis capability of predictive models with interactive visualization as a new idea to explore the visual analysis of student behavior and performance changes. Using relevant predictive algorithms in machine learning, corresponding models are built to refine the importance of features for visual analysis and correlate behavioral data with achievement data. In this process, multiple prediction algorithms are used to build prediction models. The model effects are analyzed and compared to select the optimal model for use in the visual analytics framework. The graphical analytic view is integrated. EduRedar, an optical analytical system for sports data based on the performance prediction model, is designed and implemented to support multidimensional and multiangle data analysis and visualize the changes in college students’ sports and performance based on accurate campus exercise data.
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Knobbout, Justian, and Esther Van der Stappen. "A Capability Model for Learning Analytics Adoption: Identifying Organizational Capabilities from Literature on Learning Analytics, Big Data Analytics, and Business Analytics." International Journal of Learning Analytics and Artificial Intelligence for Education (iJAI) 2, no. 1 (March 20, 2020): 47. http://dx.doi.org/10.3991/ijai.v2i1.12793.
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Despite the promises of learning analytics and the existence of several learning analytics implementation frameworks, the large-scale adoption of learning analytics within higher educational institutions remains low. Extant frameworks either focus on a specific element of learning analytics implementation, for example, policy or privacy, or lack operationalization of the organizational capabilities necessary for successful deployment. Therefore, this literature review addresses the research question “<em>What capabilities for the successful adoption of learning analytics can be identified in existing literature on big data analytics, business analytics, and learning analytics?”</em> Our research is grounded in resource-based view theory and we extend the scope beyond the field of learning analytics and include capability frameworks for the more mature research fields of big data analytics and business analytics. This paper’s contribution is twofold: 1) it provides a literature review on known capabilities for big data analytics, business analytics, and learning analytics and 2) it introduces a capability model to support the implementation and uptake of learning analytics. During our study, we identified and analyzed 15 key studies. By synthesizing the results, we found 34 organizational capabilities important to the adoption of analytical activities within an institution and provide 461 ways to operationalize these capabilities. Five categories of capabilities can be distinguished – <em>Data, Management, People, Technology</em>, and <em>Privacy & Ethics.</em> Capabilities presently absent from existing learning analytics frameworks concern <em>sourcing and integration, market, knowledge, training, automation, </em>and <em>connectivity</em>. Based on the results of the review, we present the Learning Analytics Capability Model: a model that provides senior management and policymakers with concrete operationalizations to build the necessary capabilities for successful learning analytics adoption.
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Shreejaa, N., and Dr V. Sudha. "HARNESSING GENERATIVE AI: INNOVATING DATA ANALYTICS IN THE ANALYTICAL ERA." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 01 (January 8, 2025): 1–9. https://doi.org/10.55041/ijsrem40578.
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The field of data analytics is being transformed by the use of generative artificial intelligence (AI) in today's rapidly changing digital landscape. This article explores the innovative applications and implications of generative AI in enhancing data analytics capabilities, with a focus on its impact in the analytical era. Generative AI refers to algorithms that can create new content, such as images, text, or entire datasets, based on patterns and examples it has been trained on. This technology has revolutionized traditional data analytics by allowing organizations to gain deeper insights, create predictive models, and automate complex decision-making processes with unprecedented accuracy and efficiency. One of the main advantages of generative AI in data analytics is its ability to handle large amounts of data and identify meaningful patterns that may not be obvious to human analysts. By using advanced machine learning techniques like neural networks, generative AI can analyze massive datasets to find correlations, anomalies, and trends that lead to actionable insights. Furthermore, generative AI enables organizations to simulate scenarios and predict outcomes with greater precision. This is particularly valuable in industries like finance, healthcare, and manufacturing, where accurate forecasting can result in significant cost savings, improved operational efficiency, and enhanced customer satisfaction. In addition to its predictive capabilities, generative AI enhances data analytics by allowing the creation of synthetic data. This synthetic data can be used to supplement existing datasets, address privacy concerns related to real-world data, and train machine learning models more effectively. Additionally, generative models enable data scientists to explore hypothetical scenarios and test hypotheses in a controlled environment, speeding up the pace of innovation and discovery. However, the widespread adoption of generative AI in data analytics also raises ethical and regulatory considerations. Issues such as data privacy, bias in generated content, and the potential misuse of synthetic data must be carefully addressed to ensure responsible deployment and mitigate risks. Looking ahead, the future of data analytics in the analytical era will undoubtedly be influenced by advancements in generative AI. As this technology continues to evolve, organizations will need to adapt by investing in strong infrastructure, training their workforce, and fostering a culture of responsible innovation.
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R Azmi, Putri Azmira, Marina Yusoff, and Mohamad Taufik Mohd Sallehud-din. "A Review of Predictive Analytics Models in the Oil and Gas Industries." Sensors 24, no. 12 (June 20, 2024): 4013. http://dx.doi.org/10.3390/s24124013.
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Enhancing the management and monitoring of oil and gas processes demands the development of precise predictive analytic techniques. Over the past two years, oil and its prediction have advanced significantly using conventional and modern machine learning techniques. Several review articles detail the developments in predictive maintenance and the technical and non-technical aspects of influencing the uptake of big data. The absence of references for machine learning techniques impacts the effective optimization of predictive analytics in the oil and gas sectors. This review paper offers readers thorough information on the latest machine learning methods utilized in this industry’s predictive analytical modeling. This review covers different forms of machine learning techniques used in predictive analytical modeling from 2021 to 2023 (91 articles). It provides an overview of the details of the papers that were reviewed, describing the model’s categories, the data’s temporality, field, and name, the dataset’s type, predictive analytics (classification, clustering, or prediction), the models’ input and output parameters, the performance metrics, the optimal model, and the model’s benefits and drawbacks. In addition, suggestions for future research directions to provide insights into the potential applications of the associated knowledge. This review can serve as a guide to enhance the effectiveness of predictive analytics models in the oil and gas industries.
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Dev, Jayati. "Predictive analytics." XRDS: Crossroads, The ACM Magazine for Students 27, no. 3 (March 2021): 58. http://dx.doi.org/10.1145/3453123.
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