Competing on Analytics: The New Science of Winning
... BA is widely used to improve decision-making processes, enhance operational efficiency, and gain competitive advantage. By employing advanced analytical tools and methodologies, organizations can convert raw data into meaningful insights to support strategic planning and operational decisions (Davenport & Harris, 2007). ...
... This not only increases customer satisfaction but also fosters loyalty, as customers feel valued when businesses cater to their individual needs. This aligns with previous research by Davenport and Harris (2007) and Jayawardhana et al. (2013), who emphasized the transformative power of BA in converting raw data into actionable insights, thus enhancing decision-making capabilities in business. ...
... The findings of this study align with prior research emphasizing the role of BA in transforming raw data into actionable insights (Davenport & Harris, 2007;Jayawardhana et al., 2013). However, while BA enhances operational efficiency and customer engagement, its benefits are often inaccessible to small and medium-sized enterprises (SMEs) due to limited financial resources (Saayman & Geldenhuys, 2003). ...
... Igualmente, el lavado de dinero digital, facilitado por criptomonedas y transacciones en línea, ha complicado aún más el panorama de la auditoría. Esta realidad destaca la necesidad de un enfoque proactivo y adaptativo por parte de los auditores, quienes deben equiparse con herramientas y conocimientos que les permitan abordar estas amenazas emergentes de manera efectiva (13). ...
... La integración efectiva de la IA en los procesos de auditoría para combatir el fraude y la corrupción puede conceptualizarse en un marco que aborde tres niveles fundamentales: el nivel tecnológico, el nivel organizacional y el nivel ético y regulatorio. En el nivel tecnológico, es esencial contar con una infraestructura de datos robusta, algoritmos de IA adecuados y herramientas de visualización que faciliten el análisis de datos (13). ...
... El nivel organizacional se centra en la cultura de innovación y la adopción tecnológica dentro de las organizaciones. Esto incluye el desarrollo de habilidades y competencias específicas en IA para los auditores, así como la reestructuración de procesos de auditoría que permitan la integración de estas tecnologías de manera fluida y efectiva (13). sobre cómo la IA puede ser utilizada de manera efectiva para combatir el fraude y la corrupción, transformando así la práctica de la auditoría y asegurando la integridad financiera de las organizaciones en un entorno digital que cambia constantemente. ...
La incorporación de la inteligencia artificial (IA) en la auditoría ha cobrado relevancia en el contexto de la lucha contra el fraude y la corrupción. Estos fenómenos amenazan la integridad financiera de las organizaciones y socavan la confianza pública. Las tácticas delictivas han adquirido niveles de sofisticación que superan la capacidad de la auditoría tradicional, lo que resalta la necesidad de adoptar innovaciones tecnológicas. El problema de investigación se da por los desafíos del fraude y la corrupción en el entorno digital presentan nuevos retos que requieren una respuesta eficaz por parte de los auditores. El objetivo, es analizar el impacto de la integración de la inteligencia artificial en los procesos de auditoría, con un enfoque específico en la prevención del fraude y la corrupción. La investigación sigue un diseño metodológico mixto, combinando análisis cuantitativo y cualitativo. Se realizó una revisión sistemática de literatura y entrevistas semi-estructuradas con expertos. Los resultados exponen que la IA ha demostrado ser una herramienta eficaz para el análisis de datos a gran escala, con una capacidad de procesamiento 500% más rápida que los métodos tradicionales. Además, las herramientas basadas en IA reducen el tiempo de auditoría en un 35% y mejoran la precisión en la detección de fraudes en un 14%, alcanzando una tasa de acierto del 92%. La cobertura de auditoría también se ve incrementada, llegando al 100% de los datos, en comparación con el 5-10% de los métodos convencionales. Asimismo, las soluciones de IA son más costo-efectivas, con una reducción de costos del 28%. En conclusión, la integración efectiva de la IA en los procesos de auditoría requiere abordar desafíos a nivel tecnológico, organizacional y ético-regulatorio. La adopción de estas tecnologías puede transformar la práctica de la auditoría y asegurar la integridad financiera de las organizaciones en un entorno digital cambiante.
... Igualmente, el lavado de dinero digital, facilitado por criptomonedas y transacciones en línea, ha complicado aún más el panorama de la auditoría. Esta realidad destaca la necesidad de un enfoque proactivo y adaptativo por parte de los auditores, quienes deben equiparse con herramientas y conocimientos que les permitan abordar estas amenazas emergentes de manera efectiva (13). ...
... La integración efectiva de la IA en los procesos de auditoría para combatir el fraude y la corrupción puede conceptualizarse en un marco que aborde tres niveles fundamentales: el nivel tecnológico, el nivel organizacional y el nivel ético y regulatorio. En el nivel tecnológico, es esencial contar con una infraestructura de datos robusta, algoritmos de IA adecuados y herramientas de visualización que faciliten el análisis de datos (13). ...
... El nivel organizacional se centra en la cultura de innovación y la adopción tecnológica dentro de las organizaciones. Esto incluye el desarrollo de habilidades y competencias específicas en IA para los auditores, así como la reestructuración de procesos de auditoría que permitan la integración de estas tecnologías de manera fluida y efectiva (13). sobre cómo la IA puede ser utilizada de manera efectiva para combatir el fraude y la corrupción, transformando así la práctica de la auditoría y asegurando la integridad financiera de las organizaciones en un entorno digital que cambia constantemente. ...
La presente investigación explora los efectos de la falta de comunicación en la rotación de personal en INDUIMEV CIA. LTDA., empresa de manufactura en Ecuador, identificando cómo esta deficiencia afecta las áreas de ventas y producción y, en última instancia, compromete la productividad y cumplimiento con los clientes. La rotación de personal ha sido reconocida como un problema que genera inestabilidad, retrasa procesos productivos y provoca una pérdida de confianza por parte de los clientes debido a la ineficiencia generada por los cambios constantes en el equipo de trabajo. El objetivo principal de este estudio es analizar el impacto de la comunicación deficiente en la rotación de personal, así como proponer estrategias que fortalezcan los procesos internos y faciliten la retención del talento humano. Para ello, se empleó un enfoque cualitativo mediante la observación y entrevistas con jefes de áreas clave, lo que permitió una comprensión detallada de la problemática y sus efectos en los empleados. Los resultados revelaron que la falta de comunicación en INDUIMEV afecta significativamente la productividad, generando sobrecarga laboral y desmotivación, especialmente cuando no existen manuales de funciones claros ni programas de motivación o incentivos. Muchos empleados desconocen sus roles específicos y perciben un ambiente laboral que no fomenta el crecimiento profesional, lo que incrementa las tasas de rotación. En conclusión, la implementación de programas de comunicación estructurados, un manual de funciones detallado y políticas de motivación son esenciales para mejorar la retención y cohesión del personal. Este estudio no solo ofrece soluciones aplicables a INDUIMEV, sino que también resulta relevante para otras organizaciones que enfrentan problemas similares, subrayando la importancia de una comunicación efectiva para mejorar el clima organizacional y el rendimiento operativo.
... Melalui perspektif kajian literatur, beberapa perbaikan dan penyempurnaan telah diidentifikasi untuk meningkatkan efektivitas dan akurasi analisis bisnis. Salah satu metode penyempurnaan adalah dengan mengintegrasikan pendekatan analisis yang lebih maju, seperti analisis prediktif dan analisis preskriptif, ke dalam kerangka kerja analisis bisnis yang ada (Bertsimas & Freund, 2004;Davenport & Harris, 2007). Dengan memanfaatkan teknik-teknik ini, organisasi dapat lebih baik dalam meramalkan tren masa depan dan mengidentifikasi strategi yang paling optimal untuk mencapai tujuan bisnis mereka. ...
... Kebutuhan untuk mengembangkan metode analisis yang lebih adaptif dan responsif terhadap perubahan lingkungan bisnis. Ini melibatkan penggunaan algoritma dan model analitis yang lebih dinamis dan dapat beradaptasi secara real-time terhadap perubahan dalam data atau kondisi bisnis (Bertsimas & Freund, 2004;Davenport & Harris, 2007). Dengan demikian, organisasi dapat memiliki wawasan yang lebih cepat dan akurat tentang perubahan dalam lingkungan bisnis mereka, memungkinkan mereka untuk merespons dengan lebih tepat waktu dan efektif. ...
... Pertama, penelitian lebih lanjut dapat difokuskan pada pengembangan kerangka kerja atau model konseptual yang mengintegrasikan berbagai strategi analisis bisnis yang telah diidentifikasi dalam literatur. Kerangka kerja yang komprehensif akan membantu praktisi untuk memahami hubungan antara berbagai pendekatan analisis dan mengidentifikasi strategi yang paling tepat dalam konteks bisnis mereka (Bertsimas & Freund, 2004;Davenport & Harris, 2007). ...
This research aims to investigate the optimization of business analysis strategies through the perspective of literature review. By conducting a comprehensive literature review, this study seeks to gain a deep understanding of various business analysis strategies that have been examined in the literature. The discussions resulting from this research include identifying Challenges in Business Analysis and Refinement of Business Analysis Methods and Techniques. Additionally, in-depth discussions cover the Best Strategy Implementation in Organizational Contexts to Implications for business practices in Indonesia. Other findings highlight the importance of advanced information technology, such as big data analytics and machine learning, in enhancing the effectiveness of business analysis. In conclusion, this research concludes that by understanding and implementing business analysis strategies found in literature reviews, organizations can improve the quality of their decision-making, adapt their strategies to environmental changes, and achieve competitive advantages in the ever-changing market.
... Selain itu, kompetensi digital menjadi semakin penting seiring perkembangan teknologi, termasuk kemampuan mengelola data dan berkolaborasi secara virtual. Budaya organisasi juga menjadi faktor kunci, karena efektivitas pemimpin dipengaruhi oleh kesesuaian nilai-nilai pribadi dengan norma dan nilai yang dianut organisasi (Davenport & Harris, 2007). Adaptasi kontekstual, yaitu kemampuan pemimpin menyesuaikan pendekatan berdasarkan perubahan lingkungan internal dan eksternal, juga sangat menentukan keberhasilan. ...
... Pemimpin yang berhasil adalah mereka yang mampu membawa organisasi untuk tumbuh dan berkembang, sekaligus memberdayakan individu di dalamnya untuk mencapai potensi terbaik mereka. Dengan pendekatan yang tepat, kepemimpinan dapat menjadi kekuatan pendorong yang luar biasa dalam menghadapi tantangan dan menciptakan masa depan yang lebih baik (Davenport & Harris, 2007). ...
Latar Belakang : Kepemimpinan merupakan suatu proses sosial di mana seorang individu mempengaruhi sekelompok individu lainnya untuk mencapai tujuan bersama. Tujuan : Penelitian ini bertujuan untuk menyajikan pembahasan mendalam tentang konsep, teori, gaya, faktor penentu efektivitas, dan tantangan kepemimpinan dalam konteks organisasi modern, khususnya di era digital. Metode : Studi ini menggunakan metode studi pustaka dengan teknik analisis tematik untuk memahami dinamika kepemimpinan kontemporer. Hasil dan Pembahasan : Hasil menunjukkan bahwa kepemimpinan efektif memerlukan kombinasi antara gaya transformasional, situasional, autentik, dan pelayanan, dengan adaptasi terhadap perkembangan teknologi dan budaya lokal. Kesimpulan : Temuan ini diharapkan memberikan kontribusi teoretis dan praktis untuk pengembangan kepemimpinan di berbagai sektor organisasi.
... Tout d'abord, elles doivent adapter leurs processus de contrôle de gestion pour intégrer la flexibilité et la réactivité, sans pour autant sacrifier la rigueur et l'intégrité des données (Chenhall & Moers, 2015). De plus, elles doivent exploiter de manière optimale les technologies émergentes, telles que l'intelligence artificielle (IA) et le big data, pour améliorer leur capacité à anticiper les tendances du marché, à gérer les risques, et à optimiser la performance (Davenport & Harris, 2017). ...
... L'amélioration de la Précision et de la Rapidité des Analyses L'un des principaux apports de l'IA et du big data dans le contrôle de gestion est leur capacité à améliorer la précision et la rapidité des analyses. Contrairement aux méthodes traditionnelles, ces technologies permettent de traiter et d'analyser de grandes quantités de données en temps réel, fournissant ainsi des informations plus précises et actualisées pour la prise de décision (Davenport & Harris, 2017). Les algorithmes d'apprentissage automatique, par exemple, peuvent identifier des tendances cachées et des corrélations complexes dans les données, qui seraient autrement inaccessibles avec des méthodes analytiques classiques (Jordan & Mitchell, 2015). ...
En contexte turbulent et incertain marqué par la montée croissante de la notion de la transformation digitale. Dans un monde en perpétuelle mutation, les entreprises doivent réviser leurs méthodes de gestion pour rester performantes. Cette communication explore comment l'intelligence artificielle (IA) et le big data peuvent révolutionner le contrôle de gestion, les systèmes d'information et le management de la performance. Objectif : Adapter les pratiques de gestion des entreprises pour les rendre agiles et connectées grâce à l'IA et au big data. Méthodologie : Cette recherche est menée à travers une approche qualitative combinant entre une revue de littérature et des entretiens semi directifs. Résultats : L'IA permet l'automatisation des tâches et l'analyse des données pour une prise de décision éclairée ; Le big data enrichit les systèmes d'information en offrant une vue globale et en temps réel de l’activité ; Le management de la performance bénéficie d'une évaluation plus précise et individualisée grâce à ces technologies. Conclusion : L'intégration de l'IA et du big data dans les pratiques de gestion permet aux entreprises d’anticiper les changements, d'optimiser leurs ressources et de saisir les opportunités de croissance. Ces technologies offrent un potentiel considérable pour piloter la performance de manière proactive et agile dans un environnement incertain.
... Machine Learning (ML), a subset of AI, focuses on developing algorithms that enable systems to learn from data and improve their performance over time without being explicitly programmed (Mitchell, 1997). ML algorithms are employed in various applications, such as predictive analytics, where they can forecast demand for public services based on historical data, and classification, where they categorize information to enhance decision-making (Davenport & Harris, 2007). For example, ML models can predict peak times for service requests, allowing public agencies to optimize resource allocation and reduce wait times (Chui, Manyika, & Miremadi, 2016). ...
... Data analytics and big data are integral to enhancing public service delivery through AI. Data analytics involves the systematic examination of large datasets to extract meaningful patterns, trends, and insights that inform strategic decisions (Davenport & Harris, 2007). By utilizing statistical and computational techniques, data analytics helps organizations understand complex datasets and make data-driven decisions. ...
... Traditional decision-making strategies, totally based on intuition or partial data, usually cannot confront these issues effectively. Lack of identification based on the decision will affect poor identification of risks, wrong mitigation strategies, and culminate in the failure of projects [2] [3]. ...
... Data-driven methodologies ensure better resource utilization, less uncertainty, and increased successful projects. The approach also inspires transparency and accountability in the project teams [1] [3]. ...
Data-driven decision-making will help reduce associated risks and guarantee the success of a project in the realm of project management. This paper digs deeper into the necessity of deploying data to make decisions in various phases of project execution by discussing the problem statement, proposing feasible solutions, describing uses and impact, and outlining the scope of data-driven decision-making for project management. Besides, it highlights the competency and skill a project manager should have to apply data effectively for managing risks within a project.
... In recent years, data analytics has become a fascinating area of productivity and opportunity that is posing a growing challenge to businesses. According to Barton and Court (2012) and Davenport and Harris (2009), data analytics capabilities are likely to revolutionise how businesses conduct their operations. Technology breakthroughs and shifting consumer habits are causing major changes in the banking sector in Lusaka, as well as in many other countries. ...
... This implies that data analytics greatly improves banks' capacity to efficiently segment their clientele, enabling more specialised services and higher levels of customer satisfaction. This conclusion is consistent with research by Davenport (2009) that discovered that two of the most effective methods for enhancing organisational decision-making are data analytics and decision automation. Additionally, the findings support a study by that revealed data analytics is utilised to optimise internal operations and enhance existing goods or services. ...
Data has emerged as the new currency, and businesses across industries are increasingly turning to data analytics to gain insights, make informed decisions, and gain a competitive edge. The banking industry, as a data-rich sector, stands to benefit significantly from leveraging data analytics tools and techniques. It is for this reason that this study looked at Analysing the Effectiveness and Usage data analytics in business management within commercial banks operating in Lusaka Zambia. A mixed-methods approach was used to gather comprehensive insights and a total of 100 employees from 10 different commercial banks in Lusaka made up the sample size. Regression analysis as well as descriptive statistics was used to establish relationships and give context to the observed data. The study observed that data analytics greatly improves banks' capacity to efficiently segment their clientele, enabling more specialised services and higher levels of customer satisfaction. The study also found that data analytics is essential for developing and visualising a number of indicators for the inefficiency of numerous internal and external processes and observed that data-driven insights should be increased as they enhance decision-making processes pertaining to risk assessment and mitigation techniques, which is crucial for preserving financial stability and compliance among institutions. It was observed that a well-educated workforce is essential to effectively leverage data analytics thus to optimise the use of data analytics banks should greatly improve their ability to make strategic decisions by emphasising ongoing professional development and incorporating analytical techniques into every aspect of company management.
... Predictive analytics, a branch of AI, leverages historical data, statistical algorithms, and machine-learning techniques to predict future events (Davenport & Harris, 2007). Predictive analytics enables businesses to predict customer behavio ur, market trends, and emerging risks by understanding the patterns and connections across data. ...
... Predictive analytics is one of the most popular applications of AI in BI, allowing businesses to predict trends, analyze customer behavio ur, and detect problems in advance. For example, Davenport and Harris (2007) popularized predictive analytics as a key source of competitive advantage, using extract patterns and correlations t o predict future events. On this basis, Delen and Demirkan (2013) 178 AI bot s and chatbots-powered Virtual assistants have made it no longer look like a remedy for intelligent automation in BI. · Sivarajah, Kamal, Irani, and Weerakkody (2017) surveyed the use of AI-based applications for querying and reporting, which provide real-time insights and help improve accessibility. ...
As one of the most disruptive forces in BI, AI is rapidly transforming how organizations use data and drive insights for decision points. For instance, companies can use AI technologies like predictive analytics and intelligent automation to get relevant insights, improve decision-making, and improve operations. Predictive analytics use machine learning in analyzing data when the points of trends are sought and identifying future growth and opportunities for businesses to meet and overcome challenges and seize market opportunities. Using intelligent automation, a company can monitor for trends in processes that they can optimize, automate tedious BI tasks to free up time for business-critical analysis, and improve BI workflows to enhance accuracy. This paper describes the integration of AI and BI [14, 15], highlighting the leading applications, benefits and challenges. It also covers real-life case studies illustrating how enterprises used AI-driven BI solutions to promote an enterprise-wide culture of data-driven decision-making, increase agility and foster innovation across industries. These findings are a reminder that AI can give companies the internet of intelligent tools that can be a source of strategic value and a competitive distinction in today's fast-evolving market.
... The increasing automation of routine tasks frees up time for deeper analyses and for the development of innovative solutions, making it essential that the professional knows how to use this time productively, focusing on activities that add value to the business. This perspective finds support in studies such as that of Davenport and Harris (2017), which highlight the strategic role of the finance professional in generating value through advanced data analysis and integration between areas, emphasizing that process automation should be seen as an opportunity for more consultative and less operational performance. ...
The present study aims to map and analyze the hard and soft skills considered essential for the future of finance professionals, in light of digital transformations and evolving labor market demands. To achieve this objective, a mixed methodological approach was adopted, combining literature review with empirical research through the application of a structured questionnaire to 203 finance professionals, from different regions, economic sectors, and levels of experience. The collection method included closed and open-ended questions, allowing both quantification and qualification of respondents' perceptions about the most valued competencies and challenges faced for professional development. The main results highlight the centrality of technical skills related to artificial intelligence, machine learning, data analysis, business intelligence, financial modeling, and process automation. Simultaneously, strategic thinking, adaptability, continuous learning, emotional intelligence, and effective communication stand out as the most demanded soft skills. There is a convergence between theory and practice regarding the valuation of these competencies, but also the identification of barriers to skill development, such as lack of time, resources, and strategic guidance. The research also reveals the need for a multidisciplinary and collaborative approach from finance professionals, capable of acting as strategic business partners and agents of organizational transformation. The main implications and contributions of the study lie in providing insights for the development of training policies aligned with technological and behavioral trends, both for professionals, companies, and human resources leaders. By integrating theoretical analysis and empirical data, the work contributes to the advancement of knowledge about the profile of competencies demanded in the financial sector, guiding training and development practices aimed at the sustainability and competitiveness of organizations in the digital era.
... Mechanical calculators were introduced in the early part of the 20th century, and specifically automated accounting systems in the 1960s, allowed technology to play an influential role in the development of accounting methodologies. AI is, therefore, with this very recent technological revolution that adds new impetus to the change in the field of accounting [3,4]. This paper tries to explore if AI is changing accounting; it tries to dwell on the benefits, issues, and future potential of this technology. ...
The force that is driving this revolution in the accounting domain is Artificial Intelligence (AI). It holds the power to enhance efficiencies, accuracies, and decision-making virtues. The present article ventures to illustrate how AI is bringing changes to the accounting sector. Moreover, it imparts a granular understanding of the vital armaments of AI that includes machine learning, natural language processing, and data analysis. Incorporating AI into accounting can reap several benefits, among them are productivity increases, better precision, cost-savings, and improved ability for analytics. At the same time, the implementation of AI also throws in challenges: examples include technical problems of interfacing, issues concerning ethics, and the demand for new capabilities and education for accountants. Other articles describe cases where AI has been successfully applied in accounting and detail the lessons learned and effects on business. In these, the future of AI in accounting is described through the crystal ball of emerging trends and technologies, and, moreover, whether accountants will tell their clients differently because of AI. It also considers the greater effects of AI on the financial landscape by surfacing the need for new regulatory standards to guarantee that AI is utilized in an ethical and responsible manner. This paper in total tries to paint a comprehensive picture of how AI will cause a transformation in the accounting profession and why professional adaptation is needed.
... In particular, the emerging implementation of the advanced analytics approaches improves audit case selection . By large, an analytics-driven approach could promote a more efficient process and improve efficiency (Davenport & Harris, 2007). 2 The initial implementation of CRM, as outlined in the DGT's Circular Letter, integrates the taxpayers' Ability to Pay concept specifically within the tax collection function, which defined as the map of taxpayer's compliance risk in fulfilling their tax liabilities. ...
Tax revenue remains one of the challenging fiscal issues in Indonesia. Improving tax collection performance through comprehensive reform has been an influential agenda, especially for the Directorate General of Taxes. One of the critical improvement areas is the utilization of information technology in tax assessment and audit functions. This study explores the taxpayers’ ability concept as a complementary measure to the existing taxpayer monitoring module, particularly in case selection and targeting functions under the Compliance Risk Management (CRM) framework. The 5Cs of credit analysis (Character, Capacity, Capital, Condition, and Collateral) are employed as proxies for the taxpayers’ ability to pay. This research aims to identify the most effective machine learning algorithm for classifying taxpayers' ability to pay to enhance the CRM's effectiveness for corporate taxpayers, limited to those administered in large and medium tax offices. Several machine learning algorithms were tested, including logistic regression as a baseline comparison, based on the quantitative and qualitative performance comparison. The findings reveal that the Light Gradient Boosting Machine algorithm provides the most effective results in terms of both accuracy and computational efficiency. However, several challenges need to be addressed to improve the model implementation.
... Data-driven business strategies have become the cornerstone of success in the modern retail industry. Companies that can effectively manage and analyze data tend to have a sustainable competitive advantage [17]. By leveraging data, retail businesses can gain deeper insights into consumer behavior, identify new market opportunities, and optimize operations and supply chains [18]. ...
This study aims to analyze the number of Indomaret outlets in Jakarta by utilizing informationsystems technology and data mining techniques. Using quantitative data from 500 Indomaretlocations, the analysis was conducted to identify distribution patterns and the factors influencingoutlet growth. Clustering and linear regression methods were employed to evaluate the relationshipbetween the number of outlets and demographic and economic variables, such as population density,per capita income, and distance from the city center. The analysis results indicate a significantrelationship between population density and the number of Indomaret outlets, with a regressioncoefficient of 0.75 (p < 0.01), meaning that every increase of 1,000 people in population density isassociated with the addition of 3 Indomaret outlets. Clustering analysis also identified three strategiclocation groups with high growth potential. The main contribution of this research lies in integratingdata mining methods with spatial analysis to understand modern retail expansion in urban areas—anapproach that is still rarely explored in previous studies. These findings not only enrich the literatureon data-driven retail location analysis but also provide practical insights for industry players informulating data-based expansion strategies. This research offers valuable insights for Indomaret’smanagement in making strategic decisions regarding expansion and store placement, demonstratingthat the use of information systems and data mining is effective in supporting quantitative analysisfor business development in the retail sector.
... Analytics is a framework of knowledge and practice related to organizational actions. It uses the data it manages to produce various insights to support decision-making and the actions of organizational actors (Davenport & Harris, 2007;Power et al., 2018;White & Imhoff, 2010). The term analytics has various variants because of differences in the emphasis on the purpose or process. ...
This study employs a “practice-as-research” approach to investigate the role of analytics as a pivotal component of Indonesia’s tax administration system, specifically focusing on addressing tax gaps. Tax analytics systems and applications operate centrally to generate data that support several tax administration functions, including taxpayer registration, compliance monitoring, dispute resolution, and law enforcement. However, the tax officers—who serve asend-users of this data—frequently encounter the “last-mile problem”,where the data provided is not immediately actionable. Consequently, such tax officers are often required to develop their own data pipelines to further process and analyze the data before it can be effectively used for decision-making. This study identifies two categories of last-mile issues: those that can be eliminated and those that can only be mitigated to a limited extent. Two key recommendations are proposed to address these challenges. First, existing analytics applications should enhance taxpayer profile data by integrating the most comprehensive analytics outcomes, including compliance risk profiling. This can be achieved by implementing a “reverse-ETL” approach to improve existing analytics applications, facilitating the seamless flow of processed data back into operational system data. Second,the study advocates for more flexible self-service analytics platformfor scenarios where last-mile challenges are unavoidable. This could be an analytics sandbox or a data-as-a-product approach that leverages containerization to enable tax officers to process and analyze data independently. These recommendations aim to improve the efficiency and effectiveness of Indonesia’s tax administration system by addressing the critical last-mile challenges faced by tax officers, thereby enhancing the overall utility of analytics in supporting tax-related decision-making processes
... Analytical Leadership: This model emphasizes the use of analytical tools for better understanding markets and customers. Analytical leaders assist organizations in identifying hidden patterns in data and making informed decisions through the application of statistical and mathematical models (Davenport & Harris, 2007). In marketing, this model pertains to leaders who leverage data analysis to enhance targeting in marketing campaigns, optimize pricing strategies, and improve customer experience (Lahrmann et al., 2011). ...
With the emergence of quantum computing and artificial intelligence as transformative drivers in technology industries, innovative approaches to marketing leadership have become essential. This paper explores the application of quantum mechanics principles, including uncertainty, entanglement, and superposition, in marketing leadership models within leading companies active in the fields of quantum computing and artificial intelligence. The main objective is to develop a theoretical framework that delineates how to leverage these principles to enhance adaptability and innovation in marketing strategies within complex and dynamic environments. This research employs qualitative content analysis on credible documents and articles through a library study approach, identifying and introducing marketing factors and quantum studies that contribute to business success. It also illuminates new pathways for steering marketing strategies in the digital transformation era based on quantum technologies and artificial intelligence. This approach can aid in better understanding the dynamics of advanced technology markets and making more effective strategic decisions.
... Data-Driven Decision Making: With advancements in Big Data analytics, businesses now use real-time data to drive strategic decisions. Predictive analytics, machine learning, and data visualization tools enable managers to optimize operations and anticipate market trends (Davenport & Harris, 2007). Theoretical foundations of business management have evolved from classical theories emphasizing efficiency and structure to modern approaches focusing on flexibility, human behavior, and digital transformation. ...
In an increasingly complex and competitive global marketplace, advanced business management and administration play a critical role in ensuring organizational success and sustainability. This study explores the key principles, strategies, and innovations that define modern business management. It examines the theoretical foundations of management, including classical and contemporary approaches, and their application in today's dynamic business environment. The research highlights the importance of strategic business management, which involves corporate strategy formulation, competitive advantage analysis, business expansion, and risk management. It also delves into leadership theories, organizational behavior, and employee motivation, demonstrating how effective leadership fosters a productive corporate culture. Furthermore, financial management and decision-making are examined, emphasizing budgeting, investment strategies, and risk assessment in achieving financial stability. With the rapid advancement of digital technologies, the paper also explores the role of digital transformation, artificial intelligence (AI), and e-commerce in business administration. Innovation and technological adoption have become essential for companies seeking to enhance efficiency, improve customer engagement, and maintain a competitive edge. Additionally, marketing and brand management strategies are discussed, focusing on consumer behavior, market analysis, and the growing impact of digital marketing on business success. The study also covers human resource management and workforce development, analyzing best practices in talent acquisition, employee retention, and training programs that contribute to a skilled and motivated workforce. Moreover, globalization has significantly influenced business operations, requiring organizations to navigate international trade policies, cross-cultural management, and diverse global markets. Finally, the paper addresses contemporary challenges such as economic fluctuations, political uncertainties, and technological disruptions while forecasting emerging trends that will shape the future of business administration. The findings emphasize the need for businesses to remain agile, innovative, and ethically responsible to thrive in an evolving business landscape. This study provides valuable insights for business leaders, managers, entrepreneurs, and scholars interested in advanced business management. It serves as a comprehensive guide to understanding the principles, challenges, and future directions of business administration in the 21st century.
... With real-time data analytics and AI-powered hyper-personalization, brands will be able to predict and meet customer needs more accurately than ever before. This trend shows how important it is to have marketing plans that are flexible and quick to change (Davenport & Harris, 2019). ...
This study explores how AI-driven personalization transforms consumer behavior by analyzing the psychological mechanisms influencing decision-making, preferences, and purchasing habits. Using quantitative surveys, qualitative interviews, and experimental analyses, it examines consumer reactions to recommendation systems and personalized experiences. Findings reveal a complex interplay between technology and psychology, highlighting benefits like enhanced decision-making and user experience, alongside concerns regarding autonomy, privacy, and algorithmic bias. The research addresses ethical issues and proposes a framework for balancing marketing efficacy with consumer wellbeing, offering insights for marketers, AI developers, and policymakers to foster a more equitable digital landscape.
... Real-time analytics capabilities enable banks to make immediate decisions on critical aspects such as loan approvals, fraud detection, and customer queries. As noted by Davenport and Harris (2007), the use of real-time analytics improves operational efficiency and enhances customer trust. For instance, banks can instantly assess credit applications by analyzing customer financial data against predictive models, ensuring faster turnaround times. ...
This study investigates the role of consumer analytics in the banking sector, concentrating on how customer insights might enhance the personalization of financial products. As competition grows in the banking business, understanding client preferences and habits has become vital for delivering tailored services that fit individual demands. Effective application of consumer analytics can substantially enhance customer satisfaction and engagement, ultimately driving business success. This research utilizes a quantitative approach through a structured questionnaire designed to gather responses from banking customers in Bengaluru. Customers' experiences with current financial products and their opinions about personalization made possible by analytics are evaluated in this study. By analyzing customer feedback, the study aims to identify key factors influencing satisfaction and loyalty, underscoring the significance of personalized services in retaining clients. This research contributes to the existing literature on consumer behavior in banking by providing empirical insights into the impact of analytics-driven personalization. The findings will offer practical implications for banks seeking to leverage consumer data strategically, enabling them to develop more customer-centric approaches. By pinpointing the elements that foster stronger customer relationships, this study aspires to guide financial institutions in refining their product offerings and improving overall competitiveness in an evolving market landscape.
... Mobile banking applications are evolving to offer personalized financial management tools, enabling customers to track expenses, set budgets, and receive tailored financial advice (e.g., Laukkanen et al., 2016). Furthermore, advanced data analytics are employed for fraud detection, risk assessment, and customer segmentation, allowing banks to make data-driven decisions and enhance operational security (e.g., Davenport & Harris, 2017). These concrete applications illustrate the diverse ways in which technology is reshaping the banking landscape, driving operational improvements, and enhancing customer experiences. ...
In today’s competitive business environment, digital transformation is crucial for organizational success. The Jordanian banking sector faces the challenges of adapting to rapid digital advancements, evolving customer expectations, and intense competition. This study investigated the impact of digital transformation capabilities—technological adaptation, strategic positioning, and competitive positioning—on perceived performance among 129 bank managers from 16 Jordanian commercial banks. Data were collected via a web-based survey that included a 29-item perceptual scale using a 5-point Likert scale. Multiple linear regression analysis revealed a significant positive relationship between these capabilities and perceived performance, explaining 68% of the variance. Specifically, technological adaptation (β = 0.310), strategic positioning (β = 0.260), and competitive positioning (β = 0.360) all significantly predicted perceived performance. Harman’s single-factor test indicated minimal common method bias, and strong positive correlations were found among all study variables. This research underscores the importance of a holistic digital transformation strategy for Jordanian banks, emphasizing the need for strategic investments in technology, competitive differentiation, and alignment with business objectives. Future research should explore additional factors such as organizational culture and regulatory frameworks and incorporate objective performance measures to provide a more comprehensive understanding of the impact of digital transformation. This study offers valuable insights for practitioners, policymakers, and researchers seeking to navigate digital disruption and foster business growth.
... As organizations collect vast amounts of data from various sources-such as customer interactions, social media, and usage patterns-product managers must be able to analyze and act on this data to inform decisions. According to Davenport and Harris (2007), data-driven decision-making enables businesses to optimize products, enhance customer experiences, and improve operational efficiency. ...
In the rapidly evolving digital era, product management has become a cornerstone for organizations striving to innovate and stay competitive. Disruptive product management strategies are pivotal in navigating the complexities of new technologies, changing consumer expectations, and intense market competition. This paper explores the key strategies that define successful disruptive product management, such as customer-centric innovation, agile methodologies, data-driven decision-making, and cross-functional collaboration. Furthermore, it highlights the role of emerging technologies like artificial intelligence, machine learning, and blockchain in reshaping product development processes. By focusing on rapid iteration, scalability, and flexibility, disruptive product management fosters environments that embrace change and continually evolve in response to market demands. This abstract serves as an introduction to understanding how product managers can leverage these strategies to drive transformative growth and sustained value creation in the digital age.
... The significant influence of technological readiness on innovation process efficiency validates the strategic importance of technological investments. Organizations that demonstrate high technological readiness can effectively deploy digital tools and systems, resulting in streamlined operations and enhanced efficiency (Venkatesh et al., 2016;Davenport, 2018). The findings also suggest that technological readiness amplifies the benefits of an innovation culture, reinforcing arguments for an integrative approach to strategy formulation. ...
This study constructed a core conceptual framework using organizational innovation culture, technological readiness, business success, and innovation process efficiency as variables by introducing the following demographic gender, age, education, work experience, and organization size variables. Attempting to analyze the differences in the mean values of respondents' perceptions of the demographic variable groupings for each of the variables of organizational innovation culture, technological readiness, business success, and innovation process efficiency and the influence of the independent variables on the dependent variables, A total of 382 valid questionnaires were distributed and returned in the overall study. This study found a close statistically significant positive correlation between organizational innovation culture, technological readiness and firm success; provides a nuanced understanding of the complex interactions between organizational innovation culture, technological readiness, firm success, and innovation process efficiency, thus contributing to the existing body of knowledge and providing insights for subsequent related studies.
... BI tools like Tableau and MS Power BI offer dashboard features through which users and their teams can grab information in the form of stories and make decisions. As Davenport and Harris (2017) rightly pointed out, it has been estimated that organizations that use BI for communication can solve problems 40% faster than organizations that don't use business intelligence for communication. Most importantly, it helps reveal inter-departmental dependencies and improve collaboration, thus delivering a flexible supply chain. ...
The main objectives of the study are to provide a comprehensive overview of emerging technological solutions, their applications, and their impacts on supply chain digital transformation. It is qualitative research, and secondary data were collected. The study identified five effective applications of the individual solutions. AI provides effective insights, demand forecasting, warehouse automation, transportation and route optimization, supplier selection and management, and predictive maintenance. Blockchain enables tracking and transparency, enhancing traceability, cutting down on counterfeiting, encouraging sustainable and ethical sourcing, and facilitating smart payments. Business intelligence ensures improved communication, monitoring expenses, inventory management, tracking key performance indicators, and optimized visualization. Data science facilitates demand prediction, route enhancement, inventory management, hazard assessment, and supplier administration. IoT enables shipment and delivery tracking, warehouse capacity monitoring, inventory management, storage condition monitoring, and routine optimization and automation. RFID is effective for warehouse management, inventory management, freight transportation, supply chain visibility, and retail management. These emerging technologies collectively promote a more integrated, adaptable, and resilient supply chain landscape, address significant challenges, and open doors to future innovations. The results suggest that by adopting all emerging technologies within the supply chain context, business executives would increase their efficiency and enhance firm value as well. Contribution/Originality: The study highlights how the emerging technologies complementing each other in digital transformation. It also shows where and how these technologies are used so that the users can get an overview of the applications.
Regenerative Leadership and the Shift to an Ecological Paradigm outlines the regenerative leadership framework by which it may serve as a driver for the evolution of people and the planet; however, it works through change within people and organizations or institutions and society, ultimately leading to change within the planet. This discussion intertwines theory and practice by drawing upon interdisciplinary frameworks (e.g., Peter Drucker's leadership arcs, ecological systems thinking, and ethical leadership models). The chapter reinforces leaders' need to integrate ethics and sustainability into their practices with examples from organizations like Unilever, Patagonia, and Salesforce. It also delves into the potential of digital and AI-powered ecosystems to enable innovation, collaboration, and resilience. The chapter showcases how distinct traditions lay the foundation for regenerative leadership practices through an in-depth analysis of varying cross-cultural approaches, including Ubuntu, Confucianism, and Indigenous philosophies. It treats systems thinking as a bedrock for addressing the world's complex challenges and helping leaders align their organizations and strategies with societal and environmental will. In the end, the chapter presents regenerative leadership as an engaging leadership model that integrates theoretical frameworks and practical possibilities for emergent ways of working while sustaining growth and value creation in a networked world.
This study explores the role of predictive analytics in planning the future human resource needs of Palestine Telecommunications Company (PalTel). The primary aim is to examine how PalTel uses predictive tools to optimize human resource management, improve decision-making processes, and forecast future staffing requirements. By employing predictive analytics, companies can better plan for future skill gaps, employee turnover, and recruitment needs. The research highlights the significance of data-driven decision-making in human resource planning and investigates how PalTel applies these techniques to align with its organizational goals. The study employs a structured questionnaire, gathering insights from 200 employees across various departments. The survey focuses on three key areas: the use of predictive analytics in human resource planning, its impact on organizational performance, and the challenges and opportunities that arise from adopting such technology. The findings show that a significant percentage of PalTel employees believe that predictive analytics has positively impacted decision-making, performance management, and talent development. However, challenges such as the availability of accurate data and the need for specialized training were also identified. The study concludes by offering recommendations for enhancing the implementation of predictive analytics, including improved training programs, better data management, and investment in technological infrastructure. The findings have significant implications for how companies can leverage predictive analytics to enhance human resource planning and improve organizational performance.
In the era of data-driven decision-making, the leadership of organizations has increasingly shifted towards the prioritization of metrics and quantitative analysis. This article explores how metrics, once viewed primarily as tools for operational efficiency, have become central to the formulation and execution of organizational strategy and vision. Through an interdisciplinary lens, the article examines how the widespread adoption of key performance indicators (KPIs), predictive analytics, and data visualization techniques influence the direction of leadership, drive strategic decision-making, and shape organizational culture. While the use of metrics in leadership is often seen as a pathway to objectivity and accountability, the article also critiques the potential drawbacks, including the risk of over-reliance on numbers, the reinforcement of existing biases, and the reduction of human factors in strategic planning. Drawing from case studies in both corporate and public-sector organizations, the article argues for a balanced approach that integrates quantitative analysis with qualitative insights to ensure holistic and ethical leadership.
Цифрова трансформація радикально змінює інформаційно-аналітичну діяльність (ІАД), інтегруючи нові технології, інструменти та методології. Цей процес веде до автоматизації аналізу даних, розвитку штучного інтелекту (ШІ) та великих даних, що суттєво підвищує ефективність і швидкість прийняття рішень. Завдання дослідження: визначити ключові технологічні тренди у сфері ІАД, оцінити ефективність новітніх інструментів аналітики, вивчити конкретні приклади використання сучасних аналітичних систем у бізнесі, державному управлінні та науці.ШІ та машинне навчання дозволяють аналізувати великі обсяги даних, передбачати тенденції та автоматизувати процеси збору інформації. У статті визначено, що найпоширенішими інструментами є нейромережі, обробка природної мови (NLP) та когнітивні обчислення. Технології Big Data забезпечують ефективне зберігання, обробку та аналіз величезних масивів інформації. Встановлено, що хмарні платформи, такі як AWS, Google Cloud, Azure, дозволяють отримувати доступ до ресурсів аналітики без значних капіталовкладень. Сучасні платформи, такі як Power BI, Tableau та Qlik Sense, надають можливість інтерактивної візуалізації та оперативного аналізу інформації, що підвищує ефективність аналітичної діяльності. Прогнозна аналітика використовує алгоритми ШІ для передбачення майбутніх подій на основі історичних даних. Наприклад, регресійний аналіз, кластеризація та глибоке навчання. Цифрова трансформація докорінно змінює інформаційно-аналітичну діяльність, інтегруючи передові технології та методи аналізу даних. Використання штучного інтелекту, великих даних та автоматизованих систем дозволяє оптимізувати процеси аналізу інформації та підвищити їхню ефективність.
The U.S. healthcare system faces unprecedented financial challenges, requiring innovative approaches to financial management and resource allocation. This article examines the strategic role of Financial Planning and Analysis (FP&A) and Business Intelligence (BI) in enhancing healthcare resilience, with a specific focus on cost efficiency and resource optimization. Through a comprehensive literature review and analysis of case studies from hospitals and health systems, this research demonstrates how advanced financial forecasting and predictive analytics contribute to sustainable healthcare operations. Healthcare organizations that adopt robust FP&A practices together with BI tools achieve substantial cost reductions between 25% to 50% in specific areas without compromising their quality of care. Case studies highlight successful applications in sepsis prediction, fraud detection, demand forecasting, and resource allocation optimization. The integration of clinical and financial data emerges as a critical factor in developing accurate predictive models that support strategic decision-making. This article provides finance professionals with actionable insights on implementing effective FP&A and BI solutions to enhance cost management and resource allocation in healthcare settings. The research concludes that predictive analytics represents a transformative approach to healthcare financial management, enabling organizations to anticipate challenges, optimize resources, and build financial resilience in an increasingly complex environment.
Resumo: O presente estudo tem como objetivo mapear e analisar as habilidades e competências técnicas (hard skills) e comportamentais (soft skills) consideradas essenciais para o futuro dos profissionais de finanças, à luz das transformações digitais e das novas demandas do mercado de trabalho. Para atingir tal objetivo, foi adotada uma abordagem metodológica de caráter misto, combinando revisão de literatura com pesquisa empírica por meio da aplicação de questionário estruturado a 203 profissionais atuantes no setor financeiro, provenientes de diferentes regiões, setores econômicos e níveis de experiência. O instrumento de coleta contemplou questões fechadas e abertas, permitindo tanto a quantificação quanto a qualificação das percepções dos respondentes acerca das competências mais valorizadas e dos desafios enfrentados para o desenvolvimento profissional. Os principais resultados evidenciam a centralidade das habilidades técnicas relacionadas à inteligência artificial, machine learning, análise de dados, business intelligence, modelagem financeira e automação de processos. Simultaneamente, destacam-se como soft skills mais demandadas o pensamento estratégico, a adaptabilidade, o aprendizado contínuo, a inteligência emocional e a comunicação eficaz. Observa-se uma convergência entre teoria e prática quanto à valorização dessas competências, mas também a identificação de barreiras para a capacitação, como falta de tempo, recursos e orientação estratégica. A pesquisa revela, ainda, a necessidade de uma postura multidisciplinar e colaborativa do profissional de finanças, capaz de atuar como parceiro estratégico do negócio e agente de transformação organizacional. As principais implicações e contribuições do estudo residem na oferta de subsídios para o desenvolvimento de políticas de capacitação alinhadas às tendências tecnológicas e comportamentais, tanto para profissionais quanto para empresas e líderes de recursos humanos. Ao integrar análise teórica e dados empíricos, o trabalho contribui para o avanço do conhecimento sobre o perfil de competências demandadas no setor financeiro, orientando práticas de formação e desenvolvimento voltadas à sustentabilidade e competitividade das organizações no cenário digital.
This study investigated the relationship between digital leadership and organizational performance of telecommunication firms. The study adopted a cross-sectional research design. The population of the study consists of top and middle-level employees of the four major telecommunication firms in Rivers State. The Spearman’s Rank Order Correlation Coefficient was adopted in testing the four proposed hypotheses. Based on these analyses, results revealed that the two dimensions of Digital Leadership (Digital Competence and Innovative Thinking) both had positive and significant relationship with the measures of Organizational Performance, which are Market Share and Increased Patronage. Based on the findings, the study concludes that there is a very high positive and significant relationship between Digital Leadership and Organizational Performance of telecommunication firms in Rivers State. Consequently, the study recommends that Management of telecommunication firms who want to improve their performance should ensure that decision-making is necessarily premised on digital competence. This means that leaders of these organisations should rely heavily on data before taking key decisions that bear on the survival of the organisation. In doing this, both market share and increased patronage would be enhanced, thereby leading to improved organisational performance. In addition, telecommunication firms are encouraged to evolve innovative thinking that fully embraces information technology in all their operations, including production and service delivery. Herein, as this research has confirmed, lies the recipe for their strategic prowess in achieving improved market share, increased patronage and consequential enhanced organisational performance.
In an era of rapid technological evolution, businesses operating in global markets face the dual challenge of integrating cutting-edge technology while maintaining alignment with strategic objectives. Advanced analytical techniques provide a robust framework to address this dynamic interplay, enabling data-driven decision-making and fostering competitive advantage. This paper explores key analytical approaches-such as predictive modeling, real-time analytics, and scenario planning-that are instrumental in harmonizing business strategy and technology transformation. A review of the literature reveals insights from various industries, highlighting case studies where these techniques have successfully bridged strategy and technology gaps. The paper further discusses challenges, including cultural adaptability, resource allocation, and ethical considerations, offering a roadmap for future applications.
Innovation in human resource development (HRD) is a key factor in enhancing organizational competitiveness in the digital era. This study discusses various trends in HRD innovation, key success factors, implementation challenges, and recommendations to improve the effectiveness of these innovations. The findings indicate that the utilization of digital technology, flexibility in workforce management, data-driven talent management, and gamification in training are the main trends in HRD innovation. The success of these innovations is influenced by management support, an innovative organizational culture, the use of technology, and employee engagement. However, several challenges remain, including resistance to change, skill gaps, and budget constraints. Therefore, organizations need to increase investment in HR technology, adopt digital-based training models, build an adaptive organizational culture, and develop flexible career development programs. By implementing these strategies, HRD innovation can be more effective in supporting organizational growth and preparing the workforce for the ever-evolving industry dynamics. Keywords: HRD Innovation, Digital Technology, Talent Management, Employee Training, Career Development
Buku ini lahir dari keresahan sederhana: mengapa banyak orang
memiliki ide, semangat, bahkan keberanian—namun tetap ragu untuk
memulai usaha? Jawabannya sering kali bukan karena kurangnya
potensi, melainkan karena kurangnya pemahaman dasar dan dorongan
awal. Di sinilah buku ini mencoba hadir: menjadi teman belajar bagi siapa
pun yang ingin memulai langkah pertama dalam dunia kewirausahaan.
Penulis merancang buku ini dengan bahasa yang ringan dan
aplikatif, tanpa menghilangkan pijakan ilmiah yang kuat. Harapannya,
pembaca tidak hanya memahami konsep kewirausahaan, tetapi juga
terdorong untuk mencoba, berani gagal, lalu bangkit kembali dengan
semangat yang lebih kuat.
Kewirausahaan bukan semata tentang bisnis atau keuntungan. Ia
adalah proses tumbuh, berpikir kreatif, membaca peluang, dan mengasah
keberanian. Dalam konteks yang lebih luas, wirausahawan adalah agen
perubahan yang mampu membawa dampak—bukan hanya bagi dirinya,
tetapi juga bagi lingkungan sekitarnya.
Penulis percaya bahwa setiap orang memiliki potensi untuk
menjadi wirausahawan, asalkan diberi ruang, kesempatan, dan
pemahaman yang tepat. Semoga buku ini bisa menjadi salah satu pintu
kecil menuju perjalanan besar itu.
This paper presents a Continuous Development Model for drilling engineers that enhances their technical expertise, leadership skills, and financial understanding. The study addresses the limitations of traditional execution-focused approaches by integrating structured methodologies into the design, execution, and evaluation phases. A comprehensive literature review highlights current industry trends and gaps, emphasizing the lack of structured learning and digital knowledge-sharing systems.
The proposed approach introduces Digital Twin Technology, AI-driven risk assessment, real-time execution analytics, structured mentoring programs, and automated post-well evaluations. By embedding these innovations, drilling teams shift from reactive performance monitoring to proactive decision-making and continuous learning.
Empirical results demonstrate increased engagement, better leadership preparedness, and stronger financial acumen among engineers. Engineers transition more seamlessly into Team Leader and Well Engineering Manager (WEM) roles, with improved strategic decision-making abilities. Moreover, structured exposure to P&L analysis has reinforced their understanding of technical and financial performance optimization.
This novel framework establishes a scalable, digital-first approach to workforce development, ensuring long-term organizational efficiency, cost reductions, and enhanced well performance. The findings advocate for the widespread adoption of Continuous Development principles across the oil and gas industry to cultivate more adaptable, knowledgeable, and business-oriented engineering teams.
This study investigated the development of entrepreneurial skills among senior high school students through their participation in various entrepreneurial activities. The research aimed to understand the extent of skill development, the types of activities students engage in, their insights on these activities, the challenges they face, and the in-school activities that can be proposed to enhance their entrepreneurial skills. The study employed a mixed-methods approach, combining quantitative surveys to measure the average mean scores of different entrepreneurial skills and qualitative interviews to gather insights from students about their experiences and challenges. Data were collected from senior high school students participating in entrepreneurial activities across various schools. The findings revealed that senior high school students exhibit strong entrepreneurial potential, particularly in risk-taking and technical skills, with room for improvement in communication and problem-solving abilities. Students engage in diverse entrepreneurial activities, including sustainable fashion, reuse initiatives, and food and beverage ventures, which effectively develop their skills. Participation in school academic activities and business-related events significantly enhanced their readiness to become entrepreneurs by providing theoretical knowledge, practical experience, skill development, and networking opportunities. However, students face significant challenges, such as a lack of startup capital, limited access to resources, and balancing school and business responsibilities. The study concludes that targeted training programs, mentorship, experiential learning opportunities, funding initiatives, and activities that encourage problem-solving, critical thinking, and teamwork can further enhance students' entrepreneurial skills. Future research should explore the impact of in-school entrepreneurial activities, sustainable fashion and reuse initiatives, and the role of practical experience and networking in enhancing entrepreneurial readiness among senior high school students.
Era digital telah membawa perubahan signifikan dalam berbagai aspek kehidupan, termasuk pola kepemimpinan di lembaga pendidikan. Transformasi kepemimpinan kepala sekolah di era digital tidak hanya menuntut kemampuan adaptasi terhadap teknologi, tetapi juga perubahan paradigma dalam pengelolaan sumber daya manusia, pengambilan keputusan, dan komunikasi di lingkungan sekolah. Artikel ini membahas bagaimana kepala sekolah dapat mengintegrasikan teknologi digital ke dalam strategi pendidikan, mengembangkan kompetensi kepemimpinan digital, dan membangun budaya sekolah yang tanggap terhadap perubahan. Dengan mengadopsi pendekatan transformasional, kepala sekolah dapat menciptakan lingkungan pendidikan yang kolaboratif dan inovatif untuk menghadapi tantangan era digital.
Alat dan teknologi yang mendukung HR Analytics dalam pengambilan keputusan manajemen sumber daya manusia (MSDM). Pada bagian ini, dijelaskan komponen utama sistem HR Analytics, termasuk data HR, sistem informasi SDM (HRIS), indikator kinerja utama (KPI), serta alat analitik yang diperlukan. Selain itu, teknologi seperti big data, cloud computing, dan alat business intelligence (BI) diuraikan untuk menunjukkan bagaimana teknologi ini berkontribusi dalam pengolahan dan analisis data SDM. Penggunaan metode analitik canggih, seperti machine learning dan analisis statistik, turut dijelaskan untuk mengoptimalkan pengambilan keputusan berdasarkan data. Dengan memanfaatkan berbagai alat dan teknologi ini, organisasi dapat meningkatkan efektivitas pengelolaan sumber daya manusia serta mendukung pertumbuhan dan daya saing.
The research analyzes Big Data Analytics effects on organizational results through studies of business analytics capability improvement alongside better decision quality and sustainable product development outcomes. Organizations that utilize big data efficiently improve both their operational speed and their ability to process and utilize data which enables better decisions and innovative sustainable practices. Evidence shows that big data integration as a strategic business process component creates substantial organizational improvements which supply essential knowledge for professionals and academics alike. The research demonstrates strong evidence of performance gains yet points to the necessity for additional investigations about these relationships throughout different industry sectors.
This study explores the strategic integration of Human Resource (HR) analytics and its profound impact on decision-making within HR functions. As organizations increasingly rely on data-driven insights, HR analytics has emerged as a critical tool for optimizing workforce management and driving smarter, more informed decisions. The investigation delves into the mechanisms through which HR analytics enhances decision accuracy, strategic alignment, and operational efficiency. By examining both the integration process and practical application of HR analytics, this paper highlights its role in fostering proactive, evidence-based decisions that align with organizational goals. The findings underscore the importance of embedding HR analytics in decision-making frameworks to unlock new opportunities for innovation and competitiveness in human capital management.
This edited volume, Innovate to Dominate – AI and Sustainability in Business, explores the transformative intersection of artificial intelligence (AI) and sustainability in shaping modern business practices. Through twelve data-driven, interdisciplinary chapters, the book examines how AI technologies—ranging from machine learning and big data analytics to emotional AI and blockchain—are revolutionizing consumer behavior, marketing strategies, e-commerce, and organizational decision-making. With a strong focus on ethical AI, sustainable innovation, and digital governance, the contributions provide real-world case studies and theoretical frameworks to assess AI’s role in enhancing transparency, personalization, and long-term value creation. From AI-enabled customer engagement and gig economy disruption to smart technologies for carbon reduction and responsible public policy, this volume offers actionable insights for academia, industry, and policymakers. It advocates for responsible innovation that balances technological advancement with environmental and social accountability, making it a vital reference for sustainable business transformation in the digital age.
This chapter explores the emergence and transformative impact of generative artificial intelligence (Gen AI) on organizational intellectual capital (IC) management and the shift from a data-driven to an information-driven management paradigm. It contributes to future IC research and practice by outlining several implications that Gen AI will have on an organization’s IC management. First, regarding human capital, it challenges organizations to develop and retain a specific breed of information engineering experts, sets demand for personnel reskilling including prompt engineering, and emphasizes the central role of collaboration between technical Gen AI experts and domain experts. Second, the chapter argues that organizations must pay attention to certain facets of their structural capital, including the controlled generation of new information through established business processes and the design and maintenance of adaptable information systems. Further, there is a need for an information-driven culture, which drives the utilization of Gen AI outputs in practice. Third, it emphasizes an increasing need to augment an organization’s proprietary information with extra-organizational information, as higher volumes of complementary information improve Gen AI performance. This development will lead to a market of commercialized information products, where organizations may operate as clients and providers or participate through different networks and ecosystems.
The global supply chain landscape is undergoing unprecedented transformation, driven by technological advancements and an increasing emphasis on sustainability. As the world moves towards digitalization, businesses are faced with the challenge of integrating modern technologies while maintaining ethical and sustainable practices. In this book, The Future of Supply Chains: Navigating Digital Transformation and Sustainable Procurement, we explore how these trends are reshaping the way companies manage procurement, logistics, and supply chain operations.
This book is intended for supply chain professionals, business leaders, students, and anyone interested in understanding the evolving dynamics of modern supply chains. It aims to provide readers with insights into cutting-edge technologies such as artificial intelligence, blockchain, the Internet of Things, and sustainable procurement practices. Through practical examples, case studies, and expert analysis, we hope to shed light on the future direction of supply chain management and equip readers with the tools to navigate the complexities of digital transformation.
We would like to express my deepest gratitude to all the professionals and academics who have contributed to the research and development of the concepts discussed in this book. Their expertise and experience have greatly enriched the content and provided valuable real-world perspectives.
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