To see the other types of publications on this topic, follow the link: Descriptive analytics.
Journal articles on the topic 'Descriptive analytics'
Create a spot-on reference in APA, MLA, Chicago, Harvard, and other styles
Consult the top 50 journal articles for your research on the topic 'Descriptive analytics.'
Next to every source in the list of references, there is an 'Add to bibliography' button. Press on it, and we will generate automatically the bibliographic reference to the chosen work in the citation style you need: APA, MLA, Harvard, Chicago, Vancouver, etc.
You can also download the full text of the academic publication as pdf and read online its abstract whenever available in the metadata.
Browse journal articles on a wide variety of disciplines and organise your bibliography correctly.
1
Miranda, Ma Grace P. "ENROLLMENT SYSTEM WITH DESCRIPTIVE ANALYTICS." International Journal of Advanced Research in Computer Science 10, no. 2 (2019): 53–56. http://dx.doi.org/10.26483/ijarcs.v10i2.6383.
Full text
2
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 (2021): 1719–23. http://dx.doi.org/10.1123/ijspp.2020-0958.
Full textAbstract:
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.
3
Sateesh Reddy, Chagam, and Ashlin Nimo J.R. "Analyzing the Impact of Analytics on Organizational Performance." International Journal of All Research Education and Scientific Methods 12, no. 03 (2023): 3383–90. http://dx.doi.org/10.56025/ijaresm.2023.1201243383.
Full textAbstract:
In this study, we explore the interplay between predictive, descriptive, and prescriptive analytics, mediated by decisionmaking processes, and their influence on organizational performance. Our research employs a non-probabilistic sampling method, specifically the snowball sampling technique, with a sample size of 120.The independent variables considered encompass descriptive analytics, prescriptive analytics, and predictive analytics which collectively serve as the analytical backbone shaping decision-making within organizations. Decision-making, in turn, acts as a mediating variable, channelling the impact of analytics towards organizational performance. By scrutinizing the intricate interplay between analytics and decision-making, this research aims to elucidate the extent to which decision-making influences organizational performance, thereby shedding light on the pivotal role analytics play in modern organizational dynamics. The results and conclusions drawn from this research endeavour are anticipated to make a substantial and meaningful contribution to the understanding of how analytics-driven decision-making processes drive organizational effectiveness and efficiency in today’s complex business landscape, with implications for strategic planning, resource allocation, and performance optimization.
4
Sachin, Kumar, Prasad K. Krishna, and S. Aithal P. "Banking and Financial Analytics – An Emerging Big Opportunity Based on Online Big Data." International Journal of Case Studies in Business, IT, and Education (IJCSBE) 4, no. 2 (2021): 293–309. https://doi.org/10.5281/zenodo.4451571.
Full textAbstract:
Business analytics refers to the skills, technology, methods of continuous iterative discovery, and study of past business results. In the banking industry, business analytics can be utilized to the extent that basic banking reporting can be improved with the help of descriptive analytics, predictive analytics, and prescriptive analytics utilizing significant technical developments and the use of big data currently available. The application of business analytics to banking and finance, for both organizations and professionals, is crucial, profitable, and extremely rewarding. Using advanced machine learning technology, combined with analytics, supports banks to research a great deal on customer behavior and preferences, allowing banks to continuously learn and fine tune analytical models to optimize products and services and minimize the cost of offering products across different channels. Cloud-based analytics platforms provide flexibility and elasticity for banks to work at high speed with large data workloads and to gain business value more quickly. In this paper, the major business analytics components - descriptive analytics, predictive analytics, and prescriptive analytics are addressed and their applications in various functions of banks for optimum decision-making as well as for activities such as fraud detection, application screening, custom acquisition and retention, awareness of customer purchasing habits, effective cross selling of different banking products and services, payment collection mechanism, better cash/liquidity planning, marketing optimization, consumer lifetime value, management of customer reviews, etc are analyzed. The effects of these analytics on the banking and financial industry sector's competitive and innovative capabilities are also discussed.
5
Soliman, Karim, Afroze Nazneen, Adina Ambreen, Rasheedul Haque, and Vikramjeet Jeet. "Business analytics as a driver of organizational performance: Evidence from the pharmaceutical industry." International Journal of Innovative Research and Scientific Studies 8, no. 2 (2025): 3857–71. https://doi.org/10.53894/ijirss.v8i2.6114.
Full textAbstract:
This study investigates the role of business analytics in enhancing organizational performance within the pharmaceutical industry. Business analytics, encompassing data collection, analytical tools, technology, human resources, strategic alignment, performance measurement, and compliance, is crucial for driving informed decision-making and strategic planning. A quantitative approach was employed, surveying 162 professionals across various managerial levels in pharmaceutical companies. A structured questionnaire assessed the impact of business analytics on organizational performance, focusing on financial, operational, market, employee, and sustainability metrics. Data were analyzed using SPSS software, with reliability, descriptive, and regression analyses conducted to evaluate the relationship between business analytics and performance outcomes. The results revealed a high Cronbach's Alpha value (0.980), indicating excellent reliability. Descriptive statistics showed moderate agreement with business analytics practices, while regression analysis demonstrated a strong positive correlation (R = 0.762) between business analytics and organizational performance, explaining 58% of the variance. The findings underscore the critical role of business analytics in improving financial performance, operational efficiency, market share, employee productivity, and sustainability within the pharmaceutical industry. Organizations are encouraged to invest in advanced analytics tools and technologies to drive strategic alignment and enhance overall performance.
6
Husamaldin, Laden, and Nagham Saeed. "Big Data Analytics Correlation Taxonomy." Information 11, no. 1 (2019): 17. http://dx.doi.org/10.3390/info11010017.
Full textAbstract:
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.
7
Alghamdi, A., T. Alsubait, A. Baz, and H. Alhakami. "Healthcare Analytics: A Comprehensive Review." Engineering, Technology & Applied Science Research 11, no. 1 (2021): 6650–55. https://doi.org/10.48084/etasr.3965.
Full textAbstract:
Big data have attracted significant attention in recent years, as their hidden potentials that can improve human life, especially when applied in healthcare. Big data is a reasonable collection of useful information allowing new breakthroughs or understandings. This paper reviews the use and effectiveness of data analytics in healthcare, examining secondary data sources such as books, journals, and other reputable publications between 2000 and 2020, utilizing a very strict strategy in keywords. Large scale data have been proven of great importance in healthcare, and therefore there is a need for advanced forms of data analytics, such as diagnostic data and descriptive analysis, for improving healthcare outcomes. The utilization of large-scale data can form the backbone of predictive analytics which is the baseline for future individual outcome prediction.
8
Marzouk, Mohamed, and Mohamed Enaba. "Analyzing project data in BIM with descriptive analytics to improve project performance." Built Environment Project and Asset Management 9, no. 4 (2019): 476–88. http://dx.doi.org/10.1108/bepam-04-2018-0069.
Full textAbstract:
Purpose The purpose of this paper is to expand the benefits of building information modeling (BIM) to include data analytics to analyze construction project performance. BIM is a great tool which improves communication and information flow between construction project parties. This research aims to integrate different types of data within the BIM environment, then, to perform descriptive data analytics. Data analytics helps in identifying hidden patterns and detecting relationships between different attributes in the database. Design/methodology/approach This research is considered to be an inductive research that starts with an observation of integrating BIM and descriptive data analytics. Thus, the project’s correspondence, daily progress reports and inspection requests are integrated within the project 5D BIM model. Subsequently, data mining comprising association analysis, clustering and trend analysis is performed. The research hypothesis is that descriptive data analytics and BIM have a great leverage to analyze construction project performance. Finally, a case study for a construction project is carried out to test the research hypothesis. Findings The research finds that integrating BIM and descriptive data analytics helps in improving project communication performance, in terms of integrating project data in a structured format, efficiently retrieving useful information from project raw data and visualizing analytics results within the BIM environment. Originality/value The research develops a dynamic model that helps in detecting hidden patterns and different progress attributes from construction project raw data.
9
Sivakumar, K. "AI for Business Transformation: on the Target Customers Group and Market." ComFin Research 13, S1-i1-Mar (2025): 220–23. https://doi.org/10.34293/commerce.v13is1-i1-mar.8683.
Full textAbstract:
Today’s trade or business development requires the implementation of various types of analytical methods, including artificial intelligence, i.e. landscape analytics, complexityanalytics, descriptive analytics, predictive analytics, and prescriptive analytics, support consumer services and business market development.Retaining consumers among competing companies;Eliminating fraud and risk; Handling market intelligence;Sense technology in business, applying AI for marketing.The role of artificial intelligence in insurance and financial institutions is becoming essential. Artificial intelligence is bringing great trust to people and others.AI interpretability ensures that stakeholders—be it customers, employees, or regulators—can understand and trust the decisions made by AI systems.
10
Bhati, Rakhi. "Optimizing Business Performance through Human Resource Analytics." INTERNATIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 06 (2025): 1–9. https://doi.org/10.55041/ijsrem49806.
Full textAbstract:
Executive Summary This research explores the transformative potential of Human Resource (HR) analytics in driving organizational success. HR analytics has emerged as a critical tool that shifts decision-making from intuition-based practices to data-driven strategies. Through a comprehensive analysis, the study highlights the adoption of a three-stage maturity model—descriptive, predictive, and prescriptive analytics—demonstrating how organizations evolve in their use of HR data. Key components of the research include a detailed literature review showcasing insights from global thought leaders, an exploration of methodological approaches, and real-world applications across various industries such as IT, retail, healthcare, and manufacturing. Primary data was collected through surveys involving 50 corporate professionals, providing quantitative insights into the adoption and effectiveness of HR analytics. The results indicate that while a significant number of organizations recognize the importance of HR analytics, maturity levels vary, with many still operating at the descriptive stage. Tools like Microsoft Excel, Power BI, and SAP SuccessFactors are widely used. Key HR functions that benefit most include workforce retention, employee engagement, recruitment, and leadership development. This study concludes that the successful implementation of HR analytics depends on data quality, technological infrastructure, and analytical competencies. When effectively leveraged, HR analytics enhances strategic alignment, improves HR processes, and significantly contributes to overall business performance.
11
Wang, Han, Tao Huang, Yuan Zhao, and Shengze Hu. "The Impact of Dashboard Feedback Type on Learning Effectiveness, Focusing on Learner Differences." Sustainability 15, no. 5 (2023): 4474. http://dx.doi.org/10.3390/su15054474.
Full textAbstract:
With the exponential growth of educational data, increasing attention has been given to student learning supported by learning analytics dashboards. Related research has indicated that dashboards relying on descriptive analytics are deficient compared to more advanced analytics. However, there is a lack of empirical data to demonstrate the performance and differences between different types of analytics in dashboards. To investigate these, the study used a controlled, between-groups experimental method to compare the effects of descriptive and prescriptive dashboards on learning outcomes. Based on the learning analytics results, the descriptive dashboard describes the learning state and the prescriptive dashboard provides suggestions for learning paths. The results show that both descriptive and prescriptive dashboards can effectively promote students’ cognitive development. The advantage of prescriptive dashboard over descriptive dashboard is its promotion in learners’ learning strategies. In addition, learners’ prior knowledge and learning strategies determine the extent of the impact of dashboard feedback on learning outcomes.
12
Tyagi, Prashant. "Diagnostic, Descriptive, Predictive and Prescriptive Analytics with Geospatial Data." International Journal of Computer Trends and Technology 69, no. 1 (2021): 18–22. http://dx.doi.org/10.14445/22312803/ijctt-v69i1p104.
Full text
13
Kogdenko, V. G. "Descriptive, predictive, and prescriptive analytics: Data, methods, and algorithms." Economic Analysis: Theory and Practice 18, no. 3 (2019): 447–61. http://dx.doi.org/10.24891/ea.18.3.447.
Full text
14
Campion, Emily D., and Michael A. Campion. "Descriptive statistics and advanced text analytics: A dual extension." Industrial and Organizational Psychology 14, no. 4 (2021): 489–92. http://dx.doi.org/10.1017/iop.2021.112.
Full text
15
Hoyt, Robert Eugene, Dallas Snider, Carla Thompson, and Sarita Mantravadi. "IBM Watson Analytics: Automating Visualization, Descriptive, and Predictive Statistics." JMIR Public Health and Surveillance 2, no. 2 (2016): e157. http://dx.doi.org/10.2196/publichealth.5810.
Full text
16
Yue, Meng, Tao Hong, and Jianhui Wang. "Descriptive Analytics-Based Anomaly Detection for Cybersecure Load Forecasting." IEEE Transactions on Smart Grid 10, no. 6 (2019): 5964–74. http://dx.doi.org/10.1109/tsg.2019.2894334.
Full text
17
Zulkiflee, Imran bin, Su-Cheng Haw, Kok-Why Ng, Palanichamy Naveen, and Lucia Dwi Krisnawati. "Customer Relationship Management for Better Insights with Descriptive Analytics." Advances in Artificial Intelligence and Machine Learning 03, no. 03 (2023): 1482–93. http://dx.doi.org/10.54364/aaiml.2023.1186.
Full text
18
Chek, M. Z. A., I. L. Ismail, E. N. I. Hashim, M. Shah, and M. A. A. A. Aziz. "Descriptive Analytics of Frequency Occupational Fatalities among Socso’s Contributors." International Journal of Research and Innovation in Social Science IX, no. XIV (2025): 715–22. https://doi.org/10.47772/ijriss.2025.914mg0057.
Full textAbstract:
This study presents a comprehensive descriptive analysis of the frequency of claims for SOCSO’s Dependents’ Benefit spanning the years 1972 to 2023. The data set comprises 52 years of recorded claims, highlighting a substantial growth trend from 76 claims in 1972 to 383,101 claims in 2023. The study aims to analyze trends in claim frequencies, assess statistical distributions, and provide insights into the driving factors behind the increasing claim counts. A range of descriptive statistical measures, including the mean (125,347), standard deviation (131,239), variance, and distributional skewness, were applied to interpret claim fluctuations over time. The findings indicate a consistent upward trend in claim frequency, driven by socio-economic factors, legislative amendments, and workforce demographic shifts. Graphical representations, such as time-series plots and histograms, further support the observed trends and their implications. The study recommends periodic policy evaluations, enhancements in contribution structures, and data-driven financial planning to secure the long-term viability of the SOCSO Dependent Benefit scheme. Future research should explore advanced machine-learning models and actuarial simulations to improve predictive accuracy and policy responsiveness.
19
Ronggo, Saputro, and Novani Santi. "Data Analytics for Decision-Making in Evaluating the Top-Performing Product and Developing Sales Forecasting Model in an Oil Service Company." International Journal of Current Science Research and Review 07, no. 02 (2024): 1010–20. https://doi.org/10.5281/zenodo.10622868.
Full textAbstract:
Abstract : This study addresses the strategic challenges faced by a company specialising in the manufacture of oil and gas equipment. Following organisational restructuring, which involved the dissolution of one business unit and the creation of another, the company is navigating complexities in product focus and manpower allocation within the Asia-Pacific region. The research problem centres on identifying the top-performing product, determining potential countries for establishing a support base facility based on sales performance, and developing a method for forecasting future sales. The research involved retrieving and pre-processing historical sales data, then performing a thorough descriptive and predictive analysis. The data was partitioned into training and testing sets to facilitate predictive analytics. Several predictive models were developed and tested, including neural networks, linear regression, gradient-boosted trees, random forests, and ARIMA methods. Tableau Public was utilised for descriptive analytics, whereas RapidMiner Studio was employed for predictive analytics. The study’s results, derived through both descriptive and predictive analytic methods, reveal critical insights. The Blowout Preventer (BOP) emerged as the top-performing product in the Asia-Pacific region. In terms of establishing support base facilities, Malaysia was identified as the ideal location for the BOP, while Indonesia was found suitable for the Manifold product group. Furthermore, the Random Forest model was determined to be the most effective for forecasting future sales. These findings provide strategic guidance for the company in product focus, regional expansion, and resource allocation, contributing significantly to the company’s decision-making process in a competitive industry.
20
Oleg, Mukhutdinov. "“Descriptive Metaphysics”, Descriptive Analytics, Descriptive Aesthetics. The Structure of Cognition in Kant's Critique of Pure Reason." Studies in Transcendental Philosophy, no. 1 (2020): 0. http://dx.doi.org/10.18254/s271326680009383-2.
Full text
21
Tupsakhare, Preeti. "Data Science for Proactive Patient Care: From Descriptive to Prescriptive Analytics." International Journal of Multidisciplinary Research and Growth Evaluation. 5, no. 6 (2024): 1610–17. https://doi.org/10.54660/.ijmrge.2024.5.6.1610-1617.
Full textAbstract:
The healthcare industry is undergoing a transformative shift driven by advancements in data science, fundamentally reshaping how care is delivered. This white paper examines the progressive evolution of analytics in patient care, charting its trajectory from descriptive analytics, which focus on summarizing historical data to understand past trends and outcomes, to predictive analytics, which use statistical models and algorithms to anticipate future health events, and finally to prescriptive analytics, which offer actionable recommendations to optimize clinical and operational decisions [1]. By harnessing multi-modal data—a combination of structured data (such as lab results and patient demographics) and unstructured data (like physician notes, imaging, and genomic information)—healthcare providers are gaining unprecedented insights into patient health. Advanced techniques, including machine learning (ML) and artificial intelligence (AI), are enabling these analytics to go beyond static reporting, providing dynamic, real-time solutions tailored to individual patients. This evolution empowers healthcare providers to not only predict risks, such as the likelihood of disease onset or readmission, but also to personalize treatments by identifying the most effective interventions based on a patient’s unique clinical profile. Additionally, it fosters proactive care models, enabling early intervention and prevention strategies that improve outcomes, reduce costs, and enhance the overall patient experience. By bridging the gap between data and decision-making, data science is ushering in a new era of precision medicine and value-based care, fundamentally transforming the healthcare landscape [9].
22
Durvasula, Srinivas, and Steven Lysonski. "Descriptive analytics: its power to test the applicability of cross-national scales in exploratory studies." Innovative Marketing 12, no. 3 (2016): 34–44. http://dx.doi.org/10.21511/im.12(3).2016.03.
Full textAbstract:
Conventional methodology for validating measures in consumer research relies on structural equation modeling. But, this procedure requires a fairly large sample size and a clear conceptualization of the relationship between individual items and various scale dimensions. Neither of these requirements may be met in exploratory cross-national studies. Hence, this paper addresses scale validation issues in exploratory cross-national research, where sample size is a major concern. Specifically, it uses cross-national data on the vanity measure as an exemplar and a battery of descriptive analytics to show how to assess scaling assumptions, reliability, and dimensionality of consumer behavior measures. The scale validation procedure the authors describe in this paper has implications for researchers who use multi-item rating scales as measures of consumer behavior constructs. Keywords: cross-cultural, scale validation, exploratory research, cross-national, scale applicability JEL Classification: L1, L13, D11, D12, M31
23
Odula, Linda Apondi, and Perris Chege. "Data Analytics and Organizational Performance of Kenya Civil Aviation Authority." International Journal of Social Science and Humanities Research (IJSSHR) ISSN 2959-7056 (o); 2959-7048 (p) 1, no. 1 (2023): 609–32. http://dx.doi.org/10.61108/ijsshr.v1i1.50.
Full textAbstract:
: Organizational performance, a pivotal metric determining its sustainability and standing among stakeholders and shareholders, was the focal point of investigation in this study within the Kenya Civil Aviation Authority (KCAA) and its relationship with data analytics. Four specific objectives were established: to evaluate the impact of descriptive analytics on KCAA's organizational performance; to assess the influence of prescriptive analytics on the same; to understand the relationship between predictive analytics and KCAA's organizational performance; and to scrutinize the effect of diagnostic analytics on KCAA's organizational performance. The study drew upon three established theoretical frameworks: the Resource-Based View (RBV), the Technology Acceptance Model (TAM), and the Schumpeterian Innovation Theory. The research encompassed 1400 technical and operational staff across KCAA's headquarters in Nairobi, Moi International Airport in Mombasa, and Jomo Kenyatta International Airport in Nairobi, along with airline operators and pilots. A pilot study, conducted with 30 respondents, ensured the reliability and validity of the research instrument. Reliability tests yielded a Cronbach alpha coefficient averaging 0.79, indicating strong reliability, while validity tests confirmed the instrument's validity, with Average Variance Extracted (AVE) values surpassing the 0.5 threshold. The primary study involved 300 randomly selected participants, utilizing questionnaires for data collection. Both descriptive and inferential statistics were employed for data analysis, revealing a strong positive correlation among variables. Specifically, various types of data analytics displayed positive significance: Descriptive Analytics (β = 0.133, t = 2.046, p < 0.05), Prescriptive Analytics (β = 0.198, t = 3.146, p < 0.05), Diagnostic Analytics (β = 0.190, t = 3.089, p < 0.05), and Predictive Analytics (β = 0.120, t = 1.961, p = 0.05). Diagnostic tests affirmed the absence of multi-collinearity, data normality, and heterogeneous data. Respondents collectively acknowledged the significant impact of data analytics on KCAA's organizational performance, with the study concluding that KCAA had not fully leveraged data analytics, leading to the recommendation of a policy framework prioritizing their ongoing big data ICT initiatives, and advocating for regular implementation of diagnostic analytics to enhance aviation performance, employee engagement, and overall organizational success. 
 Key Words: Data Analytics, Descriptive Analytics, Prescriptive Analytics, Diagnostic Analytics, Predictive Analytics, Organizational Performance
24
Chidera Victoria Ibeh, Onyeka Franca Asuzu, Temidayo Olorunsogo, Oluwafunmi Adijat Elufioye, Ndubuisi Leonard Nduubuisi, and Andrew Ifesinachi Daraojimba. "Business analytics and decision science: A review of techniques in strategic business decision making." World Journal of Advanced Research and Reviews 21, no. 2 (2024): 1761–69. http://dx.doi.org/10.30574/wjarr.2024.21.2.0247.
Full textAbstract:
Business analytics and decision science have emerged as pivotal domains in enhancing strategic business decision-making processes. This review delves into various techniques that organizations employ to optimize their operations and achieve competitive advantages. At the forefront of strategic decision-making is data analytics, where vast amounts of data are analyzed to extract valuable insights. Descriptive analytics provides a historical perspective by examining past data trends, enabling businesses to understand their performance over time. This retrospective analysis serves as a foundation for predictive analytics, which utilizes statistical models and machine learning algorithms to forecast future trends and outcomes. By leveraging predictive analytics, organizations can anticipate market shifts, customer preferences, and potential risks, thereby making informed decisions. Prescriptive analytics uses predictive models to guide strategic decision-making, utilizing optimization algorithms and simulation tools to identify optimal actions. Decision science integrates analytical techniques with human judgment, focusing on consumer behavior and psychological factors to tailor marketing strategies and product offerings. Additionally, artificial intelligence (AI) and machine learning (ML) technologies are revolutionizing strategic decision-making by automating complex tasks and providing real-time insights. Natural language processing (NLP) algorithms analyze unstructured data sources, such as customer reviews and social media posts, to extract valuable information and sentiment analysis. This enables businesses to gauge customer satisfaction levels and identify areas for improvement promptly. Decision trees, regression analysis, and clustering techniques are widely used in business analytics to segment customers, identify patterns, forecast sales trends, evaluate alternatives, assess risks, and optimize resource allocation. In conclusion, business analytics and decision science offer a plethora of techniques that empower organizations to make informed, data-driven decisions. By leveraging descriptive, predictive, and prescriptive analytics, along with AI and ML technologies, businesses can navigate complex environments, capitalize on opportunities, and mitigate risks effectively. This review underscores the importance of integrating analytical techniques with human expertise to achieve strategic objectives and sustainable growth.
25
Chidera, Victoria Ibeh, Franca Asuzu Onyeka, Olorunsogo Temidayo, Adijat Elufioye Oluwafunmi, Leonard Nduubuisi Ndubuisi, and Ifesinachi Daraojimba Andrew. "Business analytics and decision science: A review of techniques in strategic business decision making." World Journal of Advanced Research and Reviews 21, no. 2 (2024): 1761–69. https://doi.org/10.5281/zenodo.14041802.
Full textAbstract:
Business analytics and decision science have emerged as pivotal domains in enhancing strategic business decision-making processes. This review delves into various techniques that organizations employ to optimize their operations and achieve competitive advantages. At the forefront of strategic decision-making is data analytics, where vast amounts of data are analyzed to extract valuable insights. Descriptive analytics provides a historical perspective by examining past data trends, enabling businesses to understand their performance over time. This retrospective analysis serves as a foundation for predictive analytics, which utilizes statistical models and machine learning algorithms to forecast future trends and outcomes. By leveraging predictive analytics, organizations can anticipate market shifts, customer preferences, and potential risks, thereby making informed decisions. Prescriptive analytics uses predictive models to guide strategic decision-making, utilizing optimization algorithms and simulation tools to identify optimal actions. Decision science integrates analytical techniques with human judgment, focusing on consumer behavior and psychological factors to tailor marketing strategies and product offerings. Additionally, artificial intelligence (AI) and machine learning (ML) technologies are revolutionizing strategic decision-making by automating complex tasks and providing real-time insights. Natural language processing (NLP) algorithms analyze unstructured data sources, such as customer reviews and social media posts, to extract valuable information and sentiment analysis. This enables businesses to gauge customer satisfaction levels and identify areas for improvement promptly. Decision trees, regression analysis, and clustering techniques are widely used in business analytics to segment customers, identify patterns, forecast sales trends, evaluate alternatives, assess risks, and optimize resource allocation. In conclusion, business analytics and decision science offer a plethora of techniques that empower organizations to make informed, data-driven decisions. By leveraging descriptive, predictive, and prescriptive analytics, along with AI and ML technologies, businesses can navigate complex environments, capitalize on opportunities, and mitigate risks effectively. This review underscores the importance of integrating analytical techniques with human expertise to achieve strategic objectives and sustainable growth.
26
Suraj, Bhosale, and Ray Samrat. "A review paper on the emerging trends in sports analytics in India." World Journal of Advanced Research and Reviews 19, no. 2 (2023): 461–70. https://doi.org/10.5281/zenodo.10839485.
Full textAbstract:
Sports analytics is the field which deals with application of data analysis techniques to improve the performance and decision-making of sports teams, players, coaches, and managers. It is one of the rapidly growing fields and it has attracted significant attention from the sports industry, academia, and the media. In this paper, we have reviewed the current state of sports analytics in India.Our country has a large and diverse sports culture. We have also identified and analyzed the emerging trends in sports analytics in India. These include the use of artificial intelligence, machine learning, computer vision, natural language processing, and cloud computing. We have also discussed the challenges and opportunities for sports analytics in India, such as the availability and quality of data, the adoption and acceptance of analytics by stakeholders, the ethical and legal implications of data collection and analysis, and the potential for social impact and innovation.
27
Olakunle, Shakur. "Assessing big data analytics and characteristics in tourism: Agodi Gardens, Ibadan, Nigeria." Turizam 28, no. 3 (2024): 154–76. https://doi.org/10.5937/turizam28-47088.
Full textAbstract:
There is a plethora of both organized and haphazard data in tourism destinations. Analyzing this data appropriately is crucial for optimal engagement. This study focuses on the connection between big data analytics and big data's characteristics in Agodi Gardens, Ibadan, Nigeria. Specific objectives were to examine the characteristics of big data; as well as to examine Descriptive and predictive data analytics. Respondents were chosen purposively. Survey instrument (questionnaire) was used to elicit data. Data was collected using structured questionnaire. The collected data were analyzed descriptively and inferentially. The study revealed that significant relationship exists between the prescriptive/descriptive big data analytics and the characteristics of big data. Precisely, there is a significant relationship between prescriptive data analytics and velocity, veracity, volume as well as value. Similarly, there is a significant relationship between descriptive data analytics and volume, variety, value as well as veracity. Likewise, variety and veracity of big data could influence big data analytics. The study therefore recommends that the management of Agodi Gardens should engage thorough big data analytics, so that data elicited by customers can be appropriately analysed and topical inference could be drawn from the analysis.
28
Iglesias, Felix, Robert Annessi, and Tanja Zseby. "DAT detectors: uncovering TCP/IP covert channels by descriptive analytics." Security and Communication Networks 9, no. 15 (2016): 3011–29. http://dx.doi.org/10.1002/sec.1531.
Full text
29
Bahrudin, Muhammad Joko Umbaran Haris, R. Rizal Isnanto, and Rahmat Gernowo. "Blockchain-Based Web Certification uses Descriptive Analytics to standardize Data." E3S Web of Conferences 448 (2023): 02001. http://dx.doi.org/10.1051/e3sconf/202344802001.
Full textAbstract:
Certification is an important component for a cryptocurrency project in terms of data security, networks and tokens or coins in the cryptocurrency network to attract investors to invest in a blockchain project. This is what encourages this research to conduct data analysis trials based on descriptive analysis for cryptocurrency project certification. In the data analysis used, the data analysis process includes using a pilot test to test the questionnaire so that the data obtained for testing the Abrar LMS system is maintained and its validity is continued. Pilot test is a test of the reliability and validity of research equipment. Before the survey was distributed to actual respondents, the survey was first tested on students and heads of university study programs. The validity of a test or a series of procedures indicates how well it measures what it is designed to measure. This interest can be proven by the results of respondents who have been analyzed using SPSS to conduct pilot tests, validity tests, and reliability tests. Testing the system on respondents using alpha to test the functionality of the system so that valid results are obtained from the combination of respondent data analysis.
30
Abualigah, Laith. "Enhancing Real-Time Data Analysis through Advanced Machine Learning and Data Analytics Algorithms." International Journal of Online and Biomedical Engineering (iJOE) 21, no. 01 (2025): 4–25. https://doi.org/10.3991/ijoe.v21i01.53203.
Full textAbstract:
This paper investigates the amalgamation of sophisticated machine learning and data analytics algorithms to enhance real-time data analysis across diverse domains. Specifically, it concentrates on the utilization of machine learning methods for real-time data analysis, encompassing supervised, unsupervised, and reinforcement learning algorithms. The research underscores the significance of instantaneous processing, analysis, and decision-making in contemporary data-centric environments spanning industries like defense, exploration, public policy, and mathematical science. The paper explores data analytics strategies for real-time data analysis, including descriptive analytics, diagnostic analytics, predictive analytics, and prescriptive analytics. Descriptive analytics techniques are explored for summarizing and visualizing extensive sensor data, while diagnostic analytics methodologies focus on detecting anomalies and irregular patterns in real-time data streams. Predictive analytics endeavors to predict forthcoming events based on historical data trends, thereby enabling proactive decision-making. Lastly, prescriptive analytics provides decision recommendations and optimization tactics grounded in predictive models and constraint logic. By offering a comprehensive examination of machine learning techniques and data analytics methodologies, the paper furnishes insights into augmenting real-time data analysis capabilities across various sectors. Additionally, it presents a case study on processing real-time data from an environmental monitoring system, illustrating the practical application of advanced machine learning and data analytics algorithms for proactive decision-making and environmental management.
31
Anggraini, Leriza Desitama, Imelda Saluza, and Faradillah Faradillah. "Utilizing Data Analytics To Identify Fraud Potential: An Internal Auditor's Perspective." Accounting and Finance Studies 5, no. 1 (2025): 037–52. https://doi.org/10.47153/afs51.12922025.
Full textAbstract:
Research Aims: This research aims to analyze the influence of Data Analytics on the detection of potential fraud. Design/methodology/approach: The methodology used is Structural Equation Modeling-Partial Least Squares (SEM-PLS) with data collected through questionnaires distributed to respondents, namely internal auditors who work in South Sumatra Province and Bangka Belitung Province. Research Findings: The research results show that Descriptive Analytics and Diagnostic Analytics have a significant influence on fraud detection and prevention, while Predictive Analytics and Prescriptive Analytics do not show a significant influence. These findings indicate that internal auditors are more effective in using descriptive and diagnostic analytics in fraud detection efforts, while the application of predictive and prescriptive analytics is still limited. Theoretical Contribution/Originality: The theoretical contribution of this research is to enrich the literature regarding the role of data analytics in fraud prevention, especially in the context of internal auditors in Indonesia. This research also provides insight into the importance of developing the capacity of internal auditors in using data analytics to increase the effectiveness of fraud monitoring and detection systems in organizations. Keywords: Data Analytics, Internal Auditor, Fraud
32
Voronova, E. Yu, and A. A. Vekshina. "Improving the organization of cost analysis in a digital environment." Economics and Management 29, no. 2 (2023): 141–49. http://dx.doi.org/10.35854/1998-1627-2023-2-141-149.
Full textAbstract:
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.
33
Revelle Akshara and Dr. Ajay Jain. "Data to Decisions: Optimizing E-commerce Sales Potential with Analytics." International Research Journal on Advanced Engineering Hub (IRJAEH) 2, no. 04 (2024): 1087–93. http://dx.doi.org/10.47392/irjaeh.2024.0150.
Full textAbstract:
Descriptive, diagnostic, predictive, and prescriptive analytics play crucial roles in optimizing the performance and user experience of an e-commerce site. Descriptive analytics involves examining historical data to gain insights into past performance, enabling businesses to identify trends, patterns, and anomalies. This analysis helps in understanding what has happened, such as identifying popular products or peak sales periods, and provides a foundation for further analysis. Diagnostic analytics goes beyond descriptive analytics by examining why certain events occurred, identifying factors that influenced outcomes, and uncovering strengths, weaknesses, and areas for improvement within an e-commerce platform. Predictive analytics utilizes statistical algorithms and machine learning techniques to forecast future trends and outcomes, enabling businesses to anticipate customer preferences, demand for specific products, and potential sales opportunities. By leveraging predictive insights, e-commerce sites can adjust strategies, inventory levels, and marketing campaigns proactively to stay ahead of the competition and meet evolving customer needs. Prescriptive analytics takes predictive insights to the next level by recommending specific actions or strategies to optimize business processes and achieve desired outcomes. This could involve personalized product recommendations, targeted marketing strategies, or dynamic pricing adjustments based on real-time data analysis. By harnessing these four types of analytics, e-commerce businesses can make informed, data-driven decisions, enhance customer experiences, drive sales growth, and maximize profitability.
34
Muhammad, Rizky Satya Bhaskara, and Wasesa Meditya. "Enhancing Car Showroom Profitability through K-Means Clustering for Customer Segmentation." International Journal of Current Science Research and Review 06, no. 07 (2023): 4904–16. https://doi.org/10.5281/zenodo.8176412.
Full textAbstract:
Abstract : Indonesia is known to be one of Southeast Asia’s largest markets for used cars. Used car showrooms in Indonesia are numerous and varied, some focus on one car brand, some focus on the lower middle class, some focus on the upper middle class, and some provide all types of cars. One of them is PQR Showroom, Even though PQR Showroom was able to generate such a great amount of sales, the profits generated by the PQR showroom are not proportional to the amount of capital invested. To increase the profit, we proposed the solution using descriptive analytics and prescriptive analytics using K-Means. We also carry out simulations by comparing sales in existing years with the results of the descriptive and prescriptive analytics that have been made for the expected profit. The results show that the simulation comparison with the data we have obtained from descriptive and prescriptive analysis gives the best-expected profit compared to the initial sales results at the PQR Showroom. It shows that the data using descriptive and K-Means is great than before. The fastest cars that have been sold is LCGC and the most wanted car is MPY Toyota Avanza and the best profit that can generate is SUV Toyota Fortuner.
35
Kothapalli, Srinikhita. "Data Analytics for Enhanced Business Intelligence in Energy-Saving Distributed Systems." Asia Pacific Journal of Energy and Environment 9, no. 2 (2022): 99–108. https://doi.org/10.18034/apjee.v9i2.781.
Full textAbstract:
This research examines how data analytics might improve Business Intelligence (BI) in energy-saving distributed systems to improve energy management and sustainability. Secondary data-based reviews synthesize literature on data analytics frameworks, data processing methods, and BI tactics in distributed energy scenarios. According to critical results, descriptive, diagnostic, predictive, and prescriptive analytics turn raw data into energy-efficient insights. Descriptive and diagnostic analytics highlight historical trends and inefficiencies, whereas predictive and prescriptive methods maximize resource allocation and real-time decision-making. Adaptive energy management requires robust BI frameworks with centralized data warehousing, visualization, and real-time analytics. However, enormous data volume, real-time processing limits, data security, and lack of standards limit these analytics' usefulness. Policy guidelines should include cybersecurity safeguards, AI and edge computing integration incentives, and standardized protocols to improve data processing and system interoperability. These findings demonstrate the importance of data-driven BI in improving energy efficiency and sustainability in distributed energy systems and meeting global energy targets.
36
Syahreza, Dina Sarah. "Studi Literatur Analisis Sumber Daya Manusia: Sistematisasi Topik dan Arah Penelitian dari Literatur Terpilih." Reslaj : Religion Education Social Laa Roiba Journal 1, no. 2 (2019): 253–64. http://dx.doi.org/10.47467/reslaj.v1i2.399.
Full textAbstract:
Literature studies related to human resources, but they have not been systemized in a focused topic. The way human resources are managed today is heavily influenced by the emergence of a global workforce and the increasing relevance of business analytics as an organizational strategic capability. While human resource analytics has been mostly discussed in the literature in the past decades, however, the systematic identification and classification of major topics has not been introduced. Where there is room for conceptual contributions that aim to provide a comprehensive definition of concepts and research areas related to HR analytics in the future. This study uses a systematic literature through a review process. Next, the researcher constructs the concept of human resource analytics as widely presented. There were 91 main research topics identified related to the three majors, namely HR analytics (technology and organization), application (descriptive) and diagnostic/prescriptive), and values (employee values and organizational values). We also speculate on an “exponential” view of HR analytics made possible by assertion of artificial intelligence and cognitive technology. This research provides a major systematization effort and future research directions to develop further studies in the field of HR analytics. Where this research offers insights to support the design of innovative analytic projects within organizations.
 Keywords: Human Resources, Systematization, Literature Study
37
Pamisetty, Vamsee. "Big Data and Predictive Analytics in Government Finance: Transforming Fraud Detection and Fiscal Oversight." International Journal of Engineering and Computer Science 10, no. 12 (2021): 25731–55. https://doi.org/10.18535/ijecs.v10i12.4680.
Full textAbstract:
Governments around the world are experimenting with Big Data and predictive analytics. They deploy various software applications like predictive policing, fraud detection, and capacity demand prediction while at the same time developing and investing in broader data analytical infrastructure and analytical skill sets. Implementing Big Data and predictive analytics can be a challenging endeavor, however, as these analytics often rely on open-source algorithms that are unsupervised and black-boxed. Equally challenging is how government institutions endeavor to use a waterfall approach from exploratory to model predictive analytics, yet how often predictions are only perfunctory and unempirical [1]. To shed light on how government finance organizations conceptualize and prepare for such analytical disruption pertaining to predictive analytics specifically, data processes, stakes and concerns were articulated based on in-depth interviews with 23 analysts who work with predictive analytics in a regional government agency. Descriptive coding of the interviews revealed that changes to data processes are being prepared or enacted, but many foreseen stakes and concerns about changes to both data processes and knowledge processes remain unresolved. New agendas to address such issues and better understand the approaches adopted were proposed. Governments across the world are aiming to exploit Big Data and associated predictive analytics to govern more effectively and efficiently. These analytics come in many sorts and varieties. In government finance, the topical applications of predictive analytics have to date mainly been found in fraud detection, capacity demand prediction, budget revenue prediction, and the prediction of homelessness and recidivism. A plethora of software applications built on open-source predictive analytics algorithms exist, encompassing packages for forecasting demand, and estimating regression models including linear and logistic types. However, there is some hesitancy in adopting most of these analytics, as open-source predictive analytics algorithms are rarely supervised and almost always deployed as black-boxed. Black-boxified analysis is countercultural to the emancipation and democratization of knowledge advocated in government, as well as other more mundane concerns about accountability and validity. With black-boxification work piling-up on agency knowledge processes, concerns arise about how this analytical work is handled, displayed, devised and/or aggregated to produce knowledge that meets government quality expectations of reproducibility, replicability, auditability, and trainability.
38
Cortez, Joey Ernehst M., Jorge Kenneth G. Ishii, Angelica Mae R. Ongkiko, Clinton R. Ortega, Bernandino P. Malang, and Florinda G. Vigonte. "Health Information System Users in Public Health Facilities: A Descriptive Analytics." International Journal of Multidisciplinary: Applied Business and Education Research 4, no. 1 (2023): 156–73. http://dx.doi.org/10.11594/ijmaber.04.01.15.
Full textAbstract:
Health Information Systems (HIS) are vital in making or developing the policies of health programs in the Philippines. The HIS is broadly defined as a system that integrates data collection, processing, reporting, and use of the information necessary for improving health service effectiveness and efficiency through better management at all levels of health services. This study is conducted to visualize the current situation of the health information system users in public health facilities in the Province of Bataan. It endeavors to answer on how the health facilities in the Province are described based on their report platform, the number of personnel, trained and untrained per facility, and the needs of the facilities when it comes to the training of the encoders. The researchers used the descriptive method specifically the Dashboarding, Analysis, and Reporting or DAR method for this study. This study focused on the data gathered from the Health Information Systems Assessment Tool. The assessment was conducted with 24 different health facilities in the Province of Bataan. The study revealed that 14 out of 24 (58%) of the Rural Health Units (RHUs) in the Province of Bataan used paper-based reporting while 10 out of 24 (42%) of the RHUs used a health information system. Twenty one out of 48 encoders (44%) are untrained while 27 out of 48 (56%) are trained. Capability training in each health information system used is proposed. In conclusion, the Province of Bataan is supporting the implementation of the use of health information systems (HIS) by designating encoders for every public health facility.
39
Alhawtmeh, Omar M., Eman Mohammed Al-Nimr, and Abdullah Majed Almaani. "The impact of big data analytics on sustainable auditing in Jordanian commercial banks." Humanities and Social Sciences Letters 13, no. 2 (2025): 574–88. https://doi.org/10.18488/73.v13i2.4181.
Full textAbstract:
This study examines the impact of big data analytics, specifically its dimensions of descriptive, predictive, and prescriptive analysis on sustainable auditing practices in Jordanian commercial banks. A descriptive analytical approach was employed to achieve the research objectives. The study population consisted of 12 Jordanian commercial banks with participants including data analysts, auditors, IT staff, and sustainable auditing consultants. 300 questionnaires were distributed, of which 277 were successfully retrieved and analyzed using SPSS. The results indicate that big data analytics significantly influences sustainable auditing practices in Jordanian commercial banks. The findings suggest that as technological advancements accelerate, increased investments in big data analytics are essential for enhancing auditing processes and achieving sustainability objectives in the banking sector. This underscores the growing importance of big data as a critical tool in modern financial management and auditing. Adopting BDA in sustainable auditing faces challenges, including data security concerns, high implementation costs, and the need for specialized expertise. Overcoming these obstacles requires strategic investments in technology, workforce training, and regulatory support. The findings of this study provide valuable insights for auditors, financial institutions, and policymakers seeking to enhance sustainable auditing practices through big data analytics (BDA). The integration of BDA in Jordanian commercial banks can lead to several practical benefits such as enhanced audit efficiency and accuracy, improved regulatory compliance, cost reduction and resource optimization.
40
Maslina Zolkepli and Isa Ramlan. "Heatmap Visualization Approach for Descriptive Analytics of Covid-19 Cases in Malaysia." Journal of Advanced Research in Applied Sciences and Engineering Technology 34, no. 2 (2023): 1–17. http://dx.doi.org/10.37934/araset.34.2.117.
Full textAbstract:
The Covid-19 pandemic has presented unparalleled obstacles to the world, including Malaysia and Indonesia. A visual analytics approach known as the Covid-19 heatmap visualization is proposed to provide relevant and timely information about Covid-19 daily cases and deaths in Malaysia. The aim of this approach is to offer a platform for various stakeholders to make informed decision based on descriptive analytics. The Covid-19 information visualization approach allows users to compare different types of visualizations such as heatmaps, line charts, and bar charts to detect Covid-19 trends or patterns. The approach is implemented as a web-based system using D3 JavaScript library and tested using the data collected from Coronavirus Pandemic Data Explorer. Results show that the Covid-19 heatmap visualization approach is an invaluable approach for understanding pandemic trends as it provides users with easy access to visualizations and fast understanding of Covid-19 situations such as daily cases, deaths and recoveries. The target users for the proposed approach are the National Security Council and Ministry of Health staff, company crews, and the general public. Future upgrades to the approach includes adding additional attributes such as the number of tests and recoveries to assist users in making predictive analytics using machine learning models.
41
Khaiyr, Mohammed Al-Laraib, and Kristina Candance Rogermann. "Retail Analytics: Driving Success in Retail Industry with Big Data Analytics." International Journal of Data Science and Advanced Analytics 4, no. 4 (2022): 158–63. http://dx.doi.org/10.69511/ijdsaa.v4i4.113.
Full textAbstract:
As retail market gets extensively competitive, the capability to optimize on serving company processes while gratifying buyer expectations has never ever been more vital. Thus, channelizing and managing information to work towards client delight and produce wholesome income is essential to survive prosperously. In the situation of huge list players worldwide and in India, information or maybe quite big data analytics has become being used at each stage of the list progression tracking emerging products that are popular, forecasting future demand and sales through predictive simulation, optimizing product placements and provides through client heat mapping and many others. Alongside this, determining the buyers very likely to be keen on specific item sorts according to the previous purchase behaviors of theirs, exercising the simplest way to address them via specific advertising initiatives and finally exercising what to market them following is the thing that forms the center of data analytics. This report is the result of a descriptive exploration on the past, future and present of the application and retail industry of company analytics in shaping proper advertising strategies.
42
Kumarasinghe, Aritha, and Marite Kirikova. "Requirements Template for Analytics Projects." Complex Systems Informatics and Modeling Quarterly, no. 39 (July 31, 2024): 65–85. http://dx.doi.org/10.7250/csimq.2024-39.04.
Full textAbstract:
Data analytics projects have become a common accomplishment in many enterprises. However, establishing a data analytics project requires consideration of many factors that are not always recognized at the very beginning of the project. This study seeks to identify what generic requirements must be defined for data analytics projects and what analytics project attributes need to be addressed by these requirements. It proposes a requirements template for the generic requirements of the analytics projects. The template is intended to be used to reduce the complexity of starting the analytics projects by providing a checklist of requirements to be considered at the beginning of the project. The template is derived from analyzing 16 data analytics project reports for descriptive, diagnostic, predictive, and prescriptive analytics tasks. The template is then validated by analyzing its compliance with 20 analytics projects within the real estate domain using the corresponding research articles.
43
Ayaz, Muhammad, Muhammad Fermi Pasha, Tahani Jaser Alahmadi, Nik Nailah Binti Abdullah, and Hend Khalid Alkahtani. "Transforming Healthcare Analytics with FHIR: A Framework for Standardizing and Analyzing Clinical Data." Healthcare 11, no. 12 (2023): 1729. http://dx.doi.org/10.3390/healthcare11121729.
Full textAbstract:
In this study, we discussed our contribution to building a data analytic framework that supports clinical statistics and analysis by leveraging a scalable standards-based data model named Fast Healthcare Interoperability Resource (FHIR). We developed an intelligent algorithm that is used to facilitate the clinical data analytics process on FHIR-based data. We designed several workflows for patient clinical data used in two hospital information systems, namely patient registration and laboratory information systems. These workflows exploit various FHIR Application programming interface (APIs) to facilitate patient-centered and cohort-based interactive analyses. We developed an FHIR database implementation that utilizes FHIR APIs and a range of operations to facilitate descriptive data analytics (DDA) and patient cohort selection. A prototype user interface for DDA was developed with support for visualizing healthcare data analysis results in various forms. Healthcare professionals and researchers would use the developed framework to perform analytics on clinical data used in healthcare settings. Our experimental results demonstrate the proposed framework’s ability to generate various analytics from clinical data represented in the FHIR resources.
44
Dvulit, Zoriana. "The impact of business analytics on corporate management: opportunities and challenges." Management and Entrepreneurship in Ukraine: the stages of formation and problems of development 2024, no. 2 (2024): 39–47. https://doi.org/10.23939/smeu2024.02.039.
Full textAbstract:
This article explores the advantages and challenges of implementing business analytics in corporate management. The study examines the current state and future prospects of the business analytics market, analyzing various types of analytics: descriptive, diagnostic, predictive, and prescriptive. The research highlights the key benefits of using analytical tools in business, such as improved forecasting accuracy, effective risk management, and business process optimization. The paper discusses the growing importance of business analytics in the context of digital transformation and the increasing role of data in decision-making. It emphasizes that companies implementing advanced analytical tools can not only optimize their operational processes but also develop data-driven growth strategies. The study reveals that 68% of companies reported improved business processes after implementing business analytics tools, demonstrating its significant impact on operational efficiency. The article also addresses the main challenges and drawbacks of implementing business analytics in companies. These include issues with data infrastructure, high implementation costs, and the need for skilled professionals. The research highlights the importance of data quality and the potential risks associated with incomplete or inaccurate data analysis, which can lead to misguided decisions. The study examines the business analytics market, projecting its growth from $28.2 billion in 2023 to $56.2 billion by 2033, with a compound annual growth rate (CAGR) of 7.1%. This growth is attributed to the increasing digitalization and the need for enterprises to manage large volumes of data effectively. The paper concludes that business analytics is becoming an essential component of modern management, capable of influencing the strategic development of enterprises. It recommends that companies invest in innovative analytical processes and actively implement new technologies to remain competitive in a rapidly changing market environment. The research also suggests areas for further investigation, including the impact of new technologies such as artificial intelligence and machine learning on business analytics, the adaptation of analytics to specific industries, and ethical considerations in data usage. Overall, this study provides a comprehensive overview of the current state and future prospects of business analytics in corporate management, offering valuable insights for both practitioners and researchers in the field.
45
Karibo, Benaiah Bagshaw, and Amina Okwakpam Joy. "Analytical Maximization of Employees' Value: A Winning Strategy for Organizational Goal Attainment." International Journal of Management Sciences and Business Research 8, no. 5 (2019): 39–48. https://doi.org/10.5281/zenodo.3496436.
Full textAbstract:
In order to meet set goals, organizations need the efforts of employees, who in turn have values that the organization need to maximize. This empirical study attempted to examine if the use of analytical methods in maximizing employees’ value has any effect on the organizational goal attainment of manufacturing firms. It examined two dimensions of employees’ values (exchange and social values) and three levels of analytics (descriptive, predictive and prescriptive analytics). The study is a cross sectional one that adopted the use of a questionnaire to 5 top managers of 48 manufacturing firms. The data were analyzed with the use of percentages, mean and standard deviation. The result showed that the use of analytics in maximizing employees’ values has a positive impact on organizational goal attainment. It recommended that business leaders need to understand the different values employees expect from the organization in order to effectively maximize their values. Finally, it recommended that organizations should learn to adopt the higher levels of analytics such as the predictive and prescriptive analytics in maximizing employees’ values in order to effectively achieve their goal attainment objectives
46
1Bhanotra, A.K. 2Ahlawat A.R. and Maru P.M. "Data Analytics: Its Importance and Relevance." Science World a monthly e magazine 3, no. 5 (2023): 737–41. https://doi.org/10.5281/zenodo.7954095.
Full textAbstract:
Data analytics is the science of analysing raw data to make conclusions about that information. Various approaches to data analytics include looking at what happened (descriptive analytics), why something happened (diagnostic analytics), what is going to happen (predictive analytics), or what should be done next (prescriptive analytics).Data analytics relies on a variety of software tools ranging from spreadsheets, data visualization, and reporting tools, data mining programs, or open-source languages for the greatest data manipulation.
47
Chai, Junyi, Zhiquan Weng, and Wenbin Liu. "Behavioral Decision Making in Normative and Descriptive Views: A Critical Review of Literature." Journal of Risk and Financial Management 14, no. 10 (2021): 490. http://dx.doi.org/10.3390/jrfm14100490.
Full textAbstract:
Recent studies on decision analytics frequently refer to the topic of behavioral decision making (BDM), which focuses on behavioral components of decision analytics. This paper provides a critical review of literature for re-examining the relations between BDM and classical decision theories in both normative and descriptive reviews. We attempt to capture several milestones in theoretical models, elaborate on how the normative and descriptive theories blend into each other, thus motivating the mostly prescriptive models in decision analytics and eventually promoting the theoretical progress of BDM—an emerging and interdisciplinary field. We pay particular attention to the decision under uncertainty, including ambiguity aversion and models. Finally, we discuss the research directions for future studies by underpinning the theoretical linkages of BDM with fast-evolving research areas, including loss aversion, reference dependence, inequality aversion, and models of quasi-maximization mistakes. This paper helps to understand various behavioral biases and psychological factors when making decisions, for example, investment decisions. We expect that the results of this research can inspire studies on BDM and provide proposals for mechanisms for the development of D-TEA (decision—theory, experiments, and applications).
48
Malyshev, Viktor, Angelina Gab, Dmytro Shakhnin, and Viktorii Kovalenko. "Market research on the global bioengineering analytics market." ScienceRise, no. 1 (June 27, 2024): 58–67. https://doi.org/10.21303/2313-8416.2024.003611.
Full textAbstract:
The object of research: the state of the art, segmental analysis, dynamics and prospects of the global bioengineering analytics market. Investigated problem: The analysis of publications and literature data on the global bioengineering analytics market revealed a lack of material on the state and prospects of this global market segment. The main scientific results: The current state, growth factors, and main trends in the global life science analytics market are presented. A segmental analysis of the life science analytics market by market components, services, applications, uses, end users, and geographic regions is carried out. It is proved that the concepts of biotechnology are constantly expanding and cover many different application areas. The area of practical use of the research results: In practice, modern doctors and researchers in the field of bioengineering deal with large amounts of biomedical information obtained from various sources, including their own experience, observations, and research. To efficiently process and reliably analyze this amount of information, specialized analytical methods must be used today. Innovative technological product: the following segments in the life sciences analytics market were identified: by market components – services, by analytics types – descriptive analytics, by applications – sales and marketing, by uses – on-demand delivery, by end users – pharma, by geographic regions – North America. Scope of the innovative technological product: determination of the state, prospects, and leading segments of the bioengineering analytics industry is relevant for the practical activities of research institutions and manufacturing companies
49
Journal, IJSREM. "DATA SCIENCE: Data Visualization and Data Analytics in the Process of Data Mining." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 01 (2024): 1–10. http://dx.doi.org/10.55041/ijsrem28332.
Full textAbstract:
In the rapidly evolving landscape of data mining, the effective extraction of valuable insights from large datasets is paramount. This survey paper investigates the pivotal roles of data visualization and analytics in the intricate process of data mining abstraction. We delve into the symbiotic relationship between these two components, examining how they synergistically contribute to the extraction, representation, and interpretation of meaningful patterns and trends within complex datasets. The survey begins by elucidating the fundamental concepts of data mining abstraction and the significance of distilling actionable knowledge from raw data. It subsequently explores the multifaceted benefits of data visualization, elucidating its role in pattern identification, insight generation, and seamless communication of findings to diverse stakeholders.In parallel, the paper navigates through the landscape of data analytics, unraveling its diverse methods such as descriptive analytics, predictive analytics, and prescriptive analytics. Emphasis is placed on how these analytical techniques enhance the abstraction process, providing statistical rigor and predictive power to unveil hidden insights.The integration of data visualization and analytics is a focal point, showcasing their collective impact on various stages of data mining. From exploratory data analysis (EDA) for initial dataset understanding to the evaluation of mining models, the survey illuminates the collaborative nature of these components. Interactive dashboards emerge as a powerful tool, allowing users to dynamically explore datasets, visualize trends, and perform real-time analytics. Key Words:Data visualization , Data analytics ,Data mining , Tools and Software , Metrics and Key Performance Indicators (KPIs).
50
Ali Hasan Kamil. "A Comparative Study of Data Analytics Techniques for e-Marketing Optimization." Journal of Information Systems Engineering and Management 10, no. 31s (2025): 415–25. https://doi.org/10.52783/jisem.v10i31s.5060.
Full textAbstract:
Data-driven decision-making in the e-marketing landscape is becoming increasingly important. Businesses are able to collect a variety of data from their users across various platforms, including their website and even on social media. They use this data to make more informed decisions when designing e-marketing strategies and practices, and also to optimize the current strategy in terms of performance (i.e., ensuring that the messages are reaching the right people at the right time). The data sets collected over time grow rapidly in both size and cardinality, and one possible path to a non-technical person's understanding of the data is through descriptive analytics and predictive analytics techniques. Our focus is the comparative study of such data analytics techniques for e-marketing strategies that engage in e-CRM thinking and focus on systems already in use or existing consumer user bases, i.e., the application of models within a context of "operations marketing management." In this work, we provide an overview of the e-marketing optimization initiatives that will aid the reader in contextualizing the processed data. Following that, we present a comparative study of the three main data analytics techniques (descriptive/prescriptive data analytics, machine learning, and time series methods) to predict new customer visits to a computer manufacturer's website based on collected consumer data. Our results are promising, demonstrating that, when carried out correctly, the predictions from both descriptive and predictive analytics can contribute up to a 30% incremental trend over marketing campaign efforts for one week on a particular website.