Data-driven government agencies frequently rely on using analytics (descriptive, predictive, prescriptive) together with high-quality data. A common benefit of being a data-driven agency is the ability to make informed decisions. Previous research has provided findings from case studies and small sample surveys that mix different types of organizations, e.g., municipalities and government agencies. Hence, it is difficult to generalize for an entire segment, e.g., government agencies, on what they do in practice to become more data driven. This paper aims to investigate what government agencies in Sweden do in practice to become more data driven. Data was collected from 137 government agencies via a web-based questionnaire, and analyzed with respect to current practices, systematicity, and strategies. The findings are that most government agencies are not characterized as data-driven, lack systematicity in using analyzed data in decision-making, and mostly develop strategies for technical platforms and data.
This study examines whether companies’ knowledge of the Industry 4.0 concept, geographic location, and size influence the digital maturity of Portuguese industrial firms. Data were collected through a self-assessment questionnaire based on the IMPULS model and analyzed using ordinal logistic regression and chi-square tests to test three hypotheses. The results show that none of these factors significantly affects digital maturity, suggesting that isolated variables do not fully explain digital progress and that broader contextual elements, such as support programs and internal digital strategies, may play a more decisive role. The study meets its objectives and contributes to understanding digital readiness in the Portuguese industrial context. Future research should incorporate additional variables, employ longitudinal or sector-specific approaches, and utilize qualitative methods to enhance the analysis further.
The integration of digital technologies has significantly reshaped marketing campaigns in Kuwait, where digital tools increasingly supplement or replace traditional outreach. In this paper, the authors offer a theoretical exploration of how digital transformation impacts marketing across business, political, and governmental sectors. Using secondary data and existing literature, the authors examine how platforms like social media, artificial intelligence, and analytics systems enhance engagement, personalization, and campaign efficiency. These technologies contribute to business intelligence by supporting real-time interaction and data-driven decision-making. However, challenges such as technological integration, regulatory uncertainty, and cultural factors persist. The study concludes with insights and future research directions focused on intelligence-driven innovation.
For business data visualization, there are two fundamental types of visual tasks: (a) differentiation tasks (identifying a data point or comparing individual data points) and (b) integration tasks (comparing sums of multiple data points and recognizing patterns). In this study, the authors propose a model of visual grouping that uses color to customize graphs for integration tasks. They conducted two experiments to investigate the effects of visual grouping on user performance across different task types. The results indicate that color grouping can enhance the outcomes of decision-making tasks, a specific type of integration task. More specifically, they found that when using graphs with visual grouping, participants spent significantly less time and achieved higher comprehension accuracy compared to those using graphs without visual grouping.
Companies are moving from a cottage industry to a factory approach to analytics, especially in regard to machine learning (ML) models. This change is motivating companies to adopt ML operations (MLOps) as a methodology for the timely development, deployment, and maintenance of ML models in order to positively impact business outcomes. The adoption of MLOps requires changes in processes, technology, and people, and these changes are informed by previous work on decision support systems (DSS), development operations (DevOps), and data operations (DataOps). The processes, technologies, and people needed for MLOps are discussed and illustrated using a customer purchase recommendation example. Current and future directions for MLOps practice driven by artificial intelligence (AI) are explored. Suggestions for further academic research are provided.
Despite the widespread acquisition of business intelligence (BI) systems, their implementation has not always been successful. This study examines the critical success factors (CSFs) that impact the implementation of BI systems in organizations. The systematic literature review follows the guidelines of Kitchenham and Charter's research that was published in 2007. A total of 93 articles published between 2011 and 2021 were analyzed for CSF related to BI systems implementation and delivery. The study identified 56 CSFs linked to organization empowerment & operations, 52 CSFs related to system implementation, and 28 CSFs associated with user enablement. The study found a paucity of research on user enablement in the context of BI implementation and delivery, highlighting a gap in the literature. The findings of this study can help organizations better understand the factors that contribute to successful BI system implementation and delivery, and guide future research in this field.
The purpose of this study is to determine the intraday hourly trading trends of currencies using predictive modeling techniques. The study encompasses two distinct intraday time intervals of 30 minutes and 1 hour, analyzing currencies from 8 different countries. It incorporates the use of wavelets MODWT to identify trends and noise in intraday currency analysis. Three predictive models, namely Support Vector Regression, Recurrent Neural Network, and Long Short-Term Memory, are applied to relative time series data to predict intraday trading currency trends. The study reveals significant noise presence in three currencies based on MODWT analysis. Additionally, it demonstrates that deep learning techniques, such as LSTM, outperform traditional machine learning approaches in accurately predicting intraday currency trends. This study contributes substantially to the theoretical understanding of international finance and provides practical insights for real-time problem-solving in currency markets. Further, this research adds to the discourse on leveraging sophisticated analytical methods within the domain of business intelligence to enhance decision-making processes in organizations operating within dynamic and complex financial environments.
Research on assessing a group's maturity in data-driven culture is rare and fragmented. This article investigates how maturity in data-driven culture can be assessed from a historical perspective. A case study was done on how the Education Council evolved in analytics maturity and as a group during 2014-2023. The assessment showed that the Education Council experienced both successful progression of group development and usage of analytics, as well as regression in group development and analytics usage. The practical implications of the findings are that group leaders need to be aware of the interplay between analytics usage and group development when planning to improve their group's maturity in data-driven culture.
The objective is to propose the use of the business intelligence tool Microsoft Power BI to contribute to the best decision making in the digital services company; there are deficiencies in the ERP integrator system currently used in the company, impairing decision-making in management and corresponding headquarters. The research is of an applied type, considering as a sample those involved in the operation of the ERP integrator system. The analysis of the ERP integrator and business intelligence Power BI software was used, obtaining as results that the use of ERP integrator stores a large magnitude of data that is not easily understandable, complex reading of reports, and lack of statistical graphs. Business intelligence Power BI was applied as a solution tool, obtaining tables designed with complete and correct data, extraction of tables for their subsequent relationship, and understandable statistical graphics; Power BI allows collaborators greater understanding when reading results.
The study presents the results of the work undertaken to analyse constructs that make the companies adopt big data in the food industry towards the financial and market performance. Data was collected from 300 food industry employees who work in vital roles in the company. Primary data was collected through a survey method and a theoretical model was tested. Technological—Organizational—Enviornmental (TOE) framework was adopted, and the factors were analysed using Smart PLS software. It reveals that trialability, observability, complexity, and top management support are having a greater influence on big data analytics (BDA) adoption. Furthermore, external support, uncertainty and insecurity, and organizational readiness are also identified to affect BDA adoption. The findings ascertain the impact of BDA on the financial performance and marketing performance of the organisations. Understanding the variables that affect BDA acceptability enables managers to take the appropriate steps for a successful deployment. The research aids BDA service providers in luring and spreading BDA in the food sector.
Business Intelligence – BI systems are increasingly accessible to small and medium-sized enterprises (SMEs). Like all Information Systems (IS), their implementation is very risky by nature. Several scholars underscore that IS risk management is more effective when initiated earlier in the system life cycle, as early as at the adoption. The objective of this research is to describe and understand the process of BI adoption in SMEs focusing on the management of implementation risk of from the adoption stage using an interpretive holistic single-case study of a small manufacturing firm in Tunisia in Africa that successfully adopted a BI system. Consistent with previous research, the study shows that in order to manage the implementation risk during the adoption stage, SMEs can proceed in a way that is more efficient for them that is rather intuitive, informal and unstructured, which is, however, explicitly based on an architecture of principles, policies and practices. The main limitation of the study is related to the qualitative single case study design.
Business Intelligence – BI systems are increasingly accessible to small and medium-sized enterprises (SMEs). Like all Information Systems (IS), their implementation is very risky by nature. Several scholars underscore that IS risk management is more effective when initiated earlier in the system life cycle, as early as at the adoption. The objective of this research is to describe and understand the process of BI adoption in SMEs focusing on the management of implementation risk of from the adoption stage using an interpretive holistic single-case study of a small manufacturing firm in Tunisia in Africa that successfully adopted a BI system. Consistent with previous research, the study shows that in order to manage the implementation risk during the adoption stage, SMEs can proceed in a way that is more efficient for them that is rather intuitive, informal and unstructured, which is, however, explicitly based on an architecture of principles, policies and practices. The main limitation of the study is related to the qualitative single case study design.
Consumer journey analysis led to efficient marketing implementation. A journey represents a path of steps and interaction between consumer and service units at each touchpoint. Dissatisfaction in the touchpoint, causes a negative effect to retain a customer. Previous studies always constructed the journey maps relied on the narrative approach. According to use Google, consumers always face massive websites to access, which is a pain point in the journey. Improving consumer buying, led to the research aims: identifying consumer needs, and reducing SEO pain-point using content relevance indexing. The data (social media posts from the Thai beauty communities in the year 2020) is analyzed and has found that there are two need types: curative and preventive. The study can segment the 150 websites into four groups which reduce the search space. Moreover, the significant words from the wrapping technique can use to create keywords in the homepage introduction that are matching the products to consumer needs.
Understanding consumer behavior is beneficial to a business in various aspects such as prediction of manufacturing quantity, new product launch, and aids in lock-in customers and lock-out competitors. The task is highly complex and traditional models do not help in absence of generalized decision making logic. Further such domains handle large amount of data in unstructured format. This article presents an intelligent system for modeling consumer behavior via a hybrid genetic fuzzy system from large source of data. The paper justifies and presents a literature survey with common observations. A four phase generic architecture of genetic fuzzy system presented for the modeling of consumer behavior. Detailed discussion on the architecture is also provided with an experiment. Technical details, fuzzy membership functions used in experiment, encoding strategy, genetic operators, and evaluation of rules using fitness function are also discussed in detail along with results. At end, applications of the research work in other domains are enlisted with possible future enhancements.
There are multiple studies establishing the importance of Business Intelligence (BI), in the Big Data Analytics context. Voice is yet to be seen as a contributing channel. Voice enabled assistants are at the forefront of conversational AI advancement. As humans speak to devices, brands and business are investing in engagement through voice channel. This voice engagement is resulting in both intangible and tangible benefits and generating voice commerce. The resultant voice data should be integral to BI, leading to Voice BI. This paper proposes a conceptual framework from engagement to intelligence, with support of five propositions to realise voice business intelligence. Type of applications and their engagement characterisation is segregated to create better understanding using Cross-Cases Observation Technique. Along with future research agenda to strengthen the propositions, this investigation observes building voice business intelligence by tracking relevant metrics which enable informed decisions.
