Knowledge management (KM) plays an increasingly vital role in helping businesses make better decisions. As a result, businesses actively seek improved methods to collect and handle the vast amounts of data, information, and knowledge now readily available. In the late 80s, information and communication technology (ICT) paved the way for the early development of KM, aiming to control the flow and use of the ever-growing data and information within companies. Two KM pioneers [1] advanced the field by viewing knowledge as an intellectual asset that companies must develop and monitor, while identifying the social, cognitive, and business components of KM. The volume of useful data and information today far exceeds what was imaginable even a decade ago. To enhance organizational decision-making and gain a competitive advantage, businesses must urgently explore this vast data set and its relationship to knowledge management (KM). [2] identifies the combination of conventional data management, open-sourced technologies, commodity hardware, and the availability of data in high volume, velocity, and variety as the key factors that led to the emergence of big data. The proliferation of smartphones, tablets, and social media further expanded big data's influence across several sectors. Organizations have increasingly adopted the extraction of useful insights from big data as a critical strategy to gain a competitive edge. The great volume, diversity, and velocity of big data make it impossible to store and analyze using conventional data management methods. [3,4] highlight the need for organizations to adopt new structures and technologies to store and analyze this data, enabling real-time decision-making. Researchers are now focusing on big data analytics, a field expected to profoundly impact organizational success. Enterprise big data originates from many sources, including sales, supply chain, research, and customer interactions. Organizations must gather, store, and analyze this data quickly enough to establish a knowledge foundation for successful decision-making. [5]  and other recent studies show that big data has become increasingly popular in decision-making and that organizations in both the public and private sectors benefit from this new technology. According to [6] . To manage the ever-increasing data volumes, organizations require a unified infrastructure capable of handling all types of big data and analyzing it consistently. Cloud computing has emerged as a popular and appealing method for storing, processing, and distributing data. A. Escalante and [7] highlight that it offers elastic data storage and processing capacity on demand, without the hefty upfront costs associated with traditional data center deployments. Big data analytics and storage demand massive computing and storage infrastructures, which cloud computing now provides. In this context, we propose a smart cloud-based computing framework designed to efficiently analyze big data from various enterprise sources and share it in real time, helping organizations gain insights and make better decisions. The innovative aspect of this strategy lies in offering a unified infrastructure that organizations can leverage for cloud computing. This infrastructure allows them to utilize big data analytics as an on-demand service and rapidly adapt IT resources to meet changing demands. This article explores the connection between knowledge management and big data analytics in the context of informed decision-making, considering the advantages and disadvantages of cloud computing.

Conceptual model of the study

A significant gap exists in understanding how cloud-based computing can effectively enhance the integration of big data analytics and knowledge management (KM) to drive real-time, data-driven decisions, despite the increasing importance of these tools in organizational decision-making. Big data presents unique challenges for typical KM systems due to its velocity, diversity, and volume, as highlighted in previous research. However, no comprehensive investigation has yet explored how a cloud-based architecture can optimize big data for KM, particularly in enhancing efficiency, reducing latency, and addressing privacy issues. This study aims to explore how a cloud-based computing architecture can fill these gaps and improve knowledge management to enhance organizational decision-making and competitive advantage.

1. Investigate how a cloud-based computing infrastructure integrates big data analytics with knowledge management (KM) to improve organizational decision-making.

2. Identify the challenges traditional KM systems face with big data in terms of volume, velocity, and variety, and determine how cloud-based solutions address these challenges.

3. Create an intelligent cloud-based computing system that promptly and accurately analyzes large amounts of data from various enterprise sources to support well-informed decisions.

4. Assess how cloud-based big data analytics can enhance knowledge management techniques in terms of efficiency, latency, and privacy.

5. Evaluate how organizations can benefit from integrating cloud and fog computing for better knowledge management and real-time data analytics.

RESEARCH GAP

Research extensively covers how cloud computing and big data analytics support organizational decision-making, but much remains unknown about combining these technologies with KM practices to enhance efficiency, reduce latency, and address privacy concerns. Previous studies primarily focus on decision-making using big data and cloud computing for storage and processing. Although cloud-based computing systems provide a unified platform for integrating KM with big data analytics, research in this area remains limited. Insufficient studies explore how cloud-based frameworks can make KM more data-driven, flexible, and responsive in real time, which would enhance competitive advantage and enable more informed decision-making. Furthermore, issues such as data protection, regulatory compliance, and integrating fog computing with cloud systems to enhance KM in real-time scenarios receive insufficient attention. To address these gaps, this study explores the potential of a cloud-based KM system incorporating big data analytics for real-time, informed decision-making.

Researchers used a mixed-methods strategy to design and test an intelligent cloud-based KM framework that incorporates big data analytics, aiming to help businesses make better decisions. Cloud computing, big data, and knowledge management are just a few of the subjects that will be covered in this process of conceptual model creation via a thorough literature review. Big data analytics can be effectively integrated into KM with the help of this conceptual framework. The proposed framework is tested and simulated using a quantitative technique. The cloud-hosted KM system was built and tested by researchers using Hadoop and Apache Spark, two big data analytics platforms. Data processing speed, scalability, efficiency in creating actionable knowledge, and latency were some of the metrics used to evaluate the system's performance. The system's capacity to enhance decision-making is assessed by statistical analysis using the results of the simulation. Researchers used a case study approach to qualitatively investigate the KM system's viability. Companies who have already embraced cloud computing and big data analytics were the main focus. These companies were from a variety of industries, including healthcare, retail, and manufacturing. Findings about practical challenges, efficacy, and acceptability of integrating cloud-based KM were derived from semi-structured interviews with knowledge managers, IT specialists, and decision-makers. Key trends and patterns pertinent to KM adoption were identified using thematic analysis after transcribing and coding the qualitative material. 
Data was collected by researchers through interviews, document analysis (including studies of pertinent regulations and reports), and cloud-based KM system simulations. Quantitative insights into system performance were offered by simulation data, while qualitative insights into implementation efficacy and problems were provided by interviews and documentation. In order to make sure the KM system was evaluated thoroughly, the study team performed statistical and thematic analysis.  Cloud computing, big data analytics, and knowledge management (KM) experts validated the proposed framework by reviewing it and providing input. Their feedback helped make the framework better, making it more scalable and suitable for application in real-world situations. Obtaining informed consent for interviews, keeping participant anonymity, and assuring data anonymization were all ethical considerations. 
In order to thoroughly assess the suggested smart cloud-based KM framework, this research integrates quantitative simulations with qualitative case studies. The results shed light on how to enhance organizational decision-making through the integration of KM with big data analytics. 

Background and literature review

Cloud based big data analytics

[8] note that to address cloud computing's latency issues in real-time applications, many experts are focusing on improving data analytics by integrating cloud and fog computing. The Fog Engine, widely discussed in the literature, enhances efficiency by processing data closer to its source for large-scale analytics. While cloud computing handles massive amounts of data, it is less suitable for quick processing, a gap that fog computing fills. Fog computing, however, poses challenges in Internet of Things (IoT) environments, including privacy, security, and resource management. Overall, combining fog and cloud computing creates a more responsive system, enhancing big data analytics in IoT.

[9] specifically discuss how standard large data clustering algorithms struggle with accuracy due to the variety of data types. To improve accuracy in cloud environments, they present an AI-powered clustering approach that utilizes parallel processing. They also emphasize the importance of Hadoop and the Bayesian Information Criterion (BIC) in enhancing clustering algorithms. Despite the use of big data clustering in fields like graph processing and market analysis, the complexity and volume of data continue to present challenges. [10]  highlights that the inadequacies of conventional DBMSs in handling uncertain data have led to the development of approaches that often oversimplify ambiguity. The study explores the characteristics of structured, semi-structured, and unstructured big data and its associated challenges through the "Vs Model" (volume, velocity, variety).  [11] highlight the growing importance of cloud computing in enabling organizations to efficiently manage large-scale datasets by overcoming traditional data processing challenges. Scalability, real-time analytics, and the integration of distributed computing frameworks like Apache Hadoop and Apache Spark provide key advantages for faster data processing. However, concerns over data privacy, security, and regulatory compliance remain significant. The research underscores the transformative impact of cloud-based big data processing and predicts that these effects will continue as data-driven companies adapt to the evolving digital landscape.

Figure: Typical Sources of Big Data

Knowledge Management System

Defines Knowledge Management (KM) as an organization's efforts to enhance knowledge practices, behaviors, and performance through activities like knowledge generation, transfer, and utilization. KM improves innovation, learning, and decision-making, leading to better organizational outcomes. Information and communication technology (ICT) enables virtual collaboration, while social capital drives knowledge networking, especially in developing economies. We discuss KM evaluation, its diverse applications across industries, and highlight challenges like the digital divide. 

Figure: Comprehensive Knowledge Generation in Traditional and Big Data-Driven KMS

[12] define the journal "Knowledge and Information Systems" as a venue for disseminating research on the theoretical underpinnings, infrastructure, and enabling technologies of knowledge and advanced information systems. The journal reviews submissions within three months and publishes cutting-edge research reports, critical reviews, and application papers. It emphasizes the multidisciplinary nature of information systems and highlights user interaction by integrating expanded conference papers and showcasing practical applications in education, business, and cognitive processes.

Figure: Knowledge Management Cycle - From Data Collection to Application

[13] review how Business Intelligence (BI) systems enhance organizational decision-making by integrating varied data sources. BI systems rely on technologies such as ETL, data warehouses, OLAP, and data mining to improve decision quality and customer loyalty. BI systems enable business activities like logistics optimization, fraud detection, and client segmentation. They also help organizations manage the complexities of global decision-making by facilitating coordination, information exchange, and aiding in tactical, strategic, and operational planning.

[14] highlight the importance of KMS in turning organizational knowledge into intellectual capital for a competitive edge. Secure access and user-friendly interfaces are two of the most important features of any system that aims to maximize productivity. In order to improve efficiency and output, a system should provide safe access and intuitive interfaces.

Pascale,[15] , KBDSS are becoming more popular as service systems in many different industries, which helps to create value by making better decisions. The goal of future research is to make KBDSS even better, especially for applications in the service industry.

Data volume, velocity and variety

[16] in this study, by facilitating enhanced operational control, cost savings, and decision-making, it provides a competitive advantage. Industries such as finance and agriculture find big data analytics useful, and cloud computing is essential for storing and organizing massive volumes of data. Machine learning and Apache Hadoop are two examples of cutting-edge technologies that can be used into big data management to improve the handling and examination of intricate data sets.

[17] the study highlights the need of automating metadata identification. The need of automating efforts to handle variation in order to improve data analysis efficiency was highlighted by experiments that showed algorithmic solutions efficiently decreased false positives and discovered data structures.

Cloud infrastructure efficiency

Approximate Computing (AC) provides scalable, energy-efficient cloud computing infrastructure by adding intentional mistakes without significantly affecting result quality, as pointed out [18] AC boosts speed and efficiency in areas like 3D gaming, robotics, image processing, and financial analysis. Recent developments, such as MIT's probabilistic transformation compiler and MSR's AC-based runtime, demonstrate growing interest in the field. The paper also discusses challenges in deploying AC, particularly in managing heterogeneous applications while optimizing energy efficiency.

[19] stresses that HEIs must align with novel technology developments, particularly cloud-based applications, to remain competitive and revolutionize database management procedures. The literature emphasizes the growing importance of on-demand information for effective decision-making in HEIs. Based on prototype testing in a Saudi Arabian university, the report recommends a cloud-based architecture to enhance knowledge exchange and decision-making. This review highlights the increasing significance of cloud solutions in education, especially for managing knowledge and facilitating decisions.

[20] explores key topics like the challenges posed by the volume, variety, and velocity of big data in the context of big data analytics and knowledge management. Scalable and affordable big data analytics heavily rely on cloud computing. The review focuses on how knowledge management systems and big data can collaborate to enhance decision-making. While Hadoop and NoSQL databases are highlighted as tools for effective data processing, the article also addresses challenges such as data integration complexity and the need for skilled personnel.

[21] discuss overcoming the limitations of traditional data storage and processing by integrating Big Data with cloud computing. They highlight challenges in data analysis, such as optimization, security concerns, and the need for effective data visualization to support decision-making. Significant security concerns remain, requiring encryption and secure communication techniques. The article emphasizes tools like MapReduce and SPARK Notebook for efficient data management and underscores the need for ongoing innovation and security protocols, particularly in healthcare and energy applications.

[22] studied how big data analytics affects decision-making, and the difficulties of working with large amounts of data. This review backs up the study and points out how it helps with making decisions and analysing big data.

Organisational data integration

Integrating edge computing with cloud resources demands careful attention to privacy, security, and efficient resource management [23] . The review highlights key aspects such as cloud resource optimization, dynamic orchestration, and load balancing for IoT and industrial applications. Processing data locally can help reduce costs, energy consumption, and delays. Federated learning and edge AI offer alternatives for collaborative machine learning without sharing raw data, preserving privacy and reducing network strain. The analysis addresses the challenges and potential solutions for seamless edge-cloud integration.

[24] emphasizes that Big Data Analytics (BDA) significantly enhances organizational performance and decision-making, particularly in Pakistan's textile industry. The research demonstrates how BDA improves decision-making by analyzing large data volumes through an information processing perspective. Strategic decision-making depends on four key big data features: validity, variety, volatility, and variability. The report urges organizations to analyze global trends to boost competitiveness and provides strategic advice for BDA implementation, while stressing the importance of key decision makers in the process.

[25] introduces Big Data Integration (BDI), highlighting the significance of connecting various data sources to increase value. Compared to conventional data integration methods, BDI handles large, dynamic, diverse, and quality-variable datasets more effectively. Although the discipline has made strides, many unanswered questions still need solutions before fully utilizing Big Data.

[26] argue that Big Data Integration (BDI) plays a crucial role in maximizing the value of the massive data volumes produced in the Big Data age. Compared to conventional integration approaches, BDI stands out for its unprecedented volume, speed, variety, and reliability. Methods like data fusion, record linking, and schema mapping are essential. Ongoing research remains necessary to address obstacles like integrating crowd sourced data and balancing cost and quality.

Nowshade, [27] argue that knowledge, especially tacit knowledge, resides in various organizational contexts and can exist apart from human consciousness. The literature emphasizes the crucial role of big data in helping organizations thrive, enhancing knowledge, encouraging innovation, and building dynamic capacities. Big data-related innovations include two main types: data-driven and data-enabled. The assessment calls for an all-encompassing knowledge management framework and stresses the importance of human capital and organizational infrastructure for successful big data projects.

[28] in this study to develop a conceptual model for assessing the implementation of Big Data Analytics (BDA). To better understand what is needed for BDA to be successful, theoretical frameworks such as RBV and ISSM were utilized. When considering the effects of BDA on decision-making, it became clear that organizational, technological, and human capacities were crucial.

[29] Data privacy, security, and skill shortages are some of the concerns brought up in the literature as it analyzes frameworks for implementing big data. Beyond the immediate benefits of big data adoption, empirical evidence points to the necessity of strategic leadership and training for firms to overcome these obstacles.

[30] , while advances in AI and ML do improve data analysis, it is still vital to balance these insights with human judgment. Based on the review's assumptions, decision-making processes will be further improved as analytics undergo continuous development.

[31] , in their study cognitive modeling and velocity into account are two examples of the structured approaches emphasized in the literature as crucial to improving decision-making efficiency. 

[32] Reinforcement Learning, and the difficulties autonomous agents encounter in dynamic are the factors to improve real-time adaptation, they present the OntoDeM model and suggest using ontologies to integrate varied information sources for better decision-making. When compared to more conventional decision-making approaches, the OntoDeM model performs better in four real-world scenarios. 

[34] Big Data Analytics (BDA) has transformed decision-making in Pakistan's textile industry. This study lays the groundwork for an information processing method that will help businesses make strategic use of the four (Diversity, validity, unpredictability, and volatility) defining features of big data. 

[34] analysed how Big Data (BD) and Big Data Analytics (BDA) factor into decision-making, by adopting SLR study. The results highlight the necessity to investigate organizational variables and barriers, as well as the significance of valid and valuable data and BD/BDA in decision-making. Research on the effects of BD adoption in strategic management can benefit from the findings of this study.

Knowledge Management Needs Assessment in Big Data Analytics

[35] emphasizes the importance of flexible platforms that may be integrated with current IT systems in light of the exponential expansion of big data analytics in many industries. There is a dearth of studies addressing topics like requirements validation and cost analysis inside the DASE framework, among others, and the literature advocates utilizing semantic web technologies to fill these gaps and improve capabilities. 

Aya, [36] analysed the challenges that come up with requirement engineering in big data projects. They mainly focused issues that like unclear requirements and users who don't want to be involved. To deal with these problems, they suggest a structure that combines traditional methods, mind mapping, and agile methods to improve the quality of requirements. They test this method using real-world studies that focus on speed, security, and dependability. The paper stresses the need for better methodological unity in requirement engineering and lists issues from the point of view of both the customer and the system.

Big Data-Enabled Knowledge Management Framework

[37]  how big data analytics (BDA) tools are becoming more popular and how hard it is for businesses to choose and use them because they are so complicated. Actor-network theory (ANT) is used to figure out how these stakeholders engage with each other. There needs to be more study on what makes decision support in BDA selection work so that organizations can get the most out of their use of tools.[38] conducted a systematic literature review (SLR) to set the stage for requirements engineering (RE) in Big Data applications. The study found specific problems in RE for Big Data. This led to the creation of a goal-oriented method for modeling quality standards, which provides useful tools for improving RE in Big Data projects.[39] in this research, educational big data is being optimized by technologies such as learning analytics and network platforms, which in turn improve teaching methods. On one hand, it discusses worldwide programs like ESRA in the US and TAD in the UK; on the other, it addresses problems like the requirement for trustworthy data and the digital literacy of teachers when using these approaches. Data-driven strategies are replacing more conventional, experience-based approaches to instructional decision-making with more objective, quantitative.

The difficulties of unequal data distribution in Big Data, where traditional classification algorithms frequently fail owing to the irresistible amount of data, are tackled [40]. The paper stresses the importance of using better evaluation metrics like AUC and discusses the limitations of tools like MapReduce and Hadoop in handling such data. It proposes data preprocessing techniques, ensemble methods, and the concept of Smart Data to improve model performance in imbalanced scenarios.[41] uses bibliometric analysis tools like Bibliometrix and VOSviewer to review 650 publications on information management and big data analytics, focusing on their impact on operational efficiency and decision-making. The study identifies influential journals, authors, and collaboration networks, and uncovers research gaps through Multiple Correspondence Analysis (MCA). It concludes with recommendations for future research to enhance big data analytics in information management.[42] reviews the determinants of Big Data Analytics (BDA) adoption in decision-making across New Zealand, China, and Vietnam. The study integrates dual process theory and the technology–organization–environment framework to explore key factors such as technology readiness, data quality, organizational knowledge, and individual preferences. It develops ten hypotheses on these factors and gathers empirical evidence through a survey of 363 respondents, highlighting both common and country-specific influences on BDA adoption. The review provides insights into improving BDA adoption in organizational contexts.[43] studied that the key aspects involve synthesizing information, critically evaluating methods, and summarizing major findings. In the context of big data analytics in a Malaysian bank, it covers issues, solutions, and concerns like data quality and security, establishing a foundation for structuring the research within existing knowledge.

Data scientists improve organizations' decision-making and knowledge management by turning raw data into meaningful insights, as highlighted [44] . Organizations that incorporate data science capabilities achieve more efficient operations, better strategic planning, and a major competitive advantage, fostering a data-driven culture, enhancing predictive analytics, and enabling cross-departmental knowledge exchange.

To improve knowledge management, some sectors adopted cloud-based analytics systems, as shown in case studies published[45] . This research demonstrates how businesses enhance their knowledge-sharing capacities by using cloud computing to gain better data-driven insights and make more informed decisions.

To illustrate how cloud computing and big data enhance the efficiency of knowledge management systems, [46] use instances from the retail and manufacturing sectors. The significance of advanced technology in facilitating strategic decision-making, improving operational efficiency, and optimizing knowledge-sharing is highlighted in their work.  [47] illustrate how cloud-based analytics facilitate information exchange and decision-making using a plethora of case studies. Highlighting 45 successful businesses that made impressive use of big data, they demonstrate the real-world impacts of advanced data analytics on business performance. 

While providing a conceptual framework for knowledge management (KM) systems, [47] handle the research issues and guiding principles associated with them. Additionally, they investigate into the ways in which cloud-based systems enhance data accessibility and knowledge-sharing processes, resulting in enhanced corporate decision-making. Through the use of case studies, [49] demonstrate how big data is being utilized by enterprises to enhance information exchange and decision-making. The groundbreaking possibilities of big data in fostering a data-driven culture and propelling strategic business advancements are illustrated by their work. 

Cloud computing and big data analytics complement each other to improve BI and KM initiatives, as described in an article [50] . The importance of advanced analytics in enhancing decision-making through the provision of practical understandings is highlighted by their research.  Cloud service integration with business portals enhances knowledge management and decision-making, as demonstrated [51]. Their research shows that these portals are crucial for better organizational decisions, more accessibility, and knowledge management.

Figure: Yahoo consumer targeting predictive modeling cycle

A multi-stage iterative process, this diagram depicts Yahoo's consumer targeting predictive modeling cycle. Yahoo starts by collecting data, which includes user actions, and then analyzes that data to find trends. Segmentation into target groups is made possible by using predictive modeling on this data, which forecasts customer behavior. During the campaign implementation phase, Yahoo develops and releases group-specific targeted campaigns. The end of the cycle is the feedback loop, which is dynamic and iterative since it gathers reaction data to improve models and future targeting efforts.

The efficiency, scalability, latency, and data processing speed of the cloud-hosted KM system were the primary areas of quantitative evaluation utilizing Hadoop and Apache Spark. Companies are facing increasingly large datasets, and traditional knowledge management systems are struggling to keep up. Therefore, scalability is of the utmost importance. The intelligent KM framework was able to improve corporate decision-making by efficiently analyzing massive amounts of data and turning it into actionable perceptions, according to statistical analysis of the system's simulations. Knowledge management was unquestionably helped by cloud computing's use of big data analytics, which allowed enterprises to make better, more timely decisions. 

Qualitative analysis, including case studies from companies in healthcare, retail, and industrial sectors, further demonstrated the system's value. Participants reported that cloud-hosted KM systems are more efficient and handle data protection issues better. The use of fog computing, combined with a distributed architecture, allowed localized data processing, which improved privacy protection and processing speeds. Processing data closer to its origin improved the system's compliance with regulations, reducing the need to transmit sensitive information across multiple layers. Case studies and interviews with decision-makers, IT specialists, and knowledge managers provided qualitative insights into the practicality and effectiveness of the cloud-based KM system.

Case studies from healthcare, retail, and industrial organizations, along with qualitative analysis, further proved the system's worth.  KM systems housed on the cloud are superior at protecting data and running efficiently. Reducing the need to transfer sensitive information across several tiers increased the system's compliance with rules by processing data closer to its origin. To gain a better understanding of the cloud-based KM system's efficacy and applicability, we conducted interviews with knowledge managers, IT experts, and decision-makers, as well as conducted case studies.

Respondents emphasized the system's ability to dismantle information silos and ensure that relevant data and information were accessible across the organization. By using big data analytics, they identified important trends and insights that were previously invisible to standard KM systems. However, challenges such as the complexity of the initial setup, the need for trained personnel to oversee integration, and concerns about data security, especially with fog computing in an IoT environment, were also noted. The findings show that cloud-based KM systems, combined with big data analytics, can significantly enhance organizational capabilities. The volume, velocity, and variety of big data present inherent challenges for traditional KM systems. This study found that cloud infrastructure provides businesses with enhanced data management capabilities that address these challenges effectively. For example, using Hadoop and Apache Spark improved the management of large datasets and facilitated access to sophisticated analytics tools, leading to faster, better decisions. The integration of cloud computing with fog computing also played a critical role. Fog computing addressed latency issues typical of cloud-based systems in real-time applications. The capacity of the system to break down information silos and make sure that pertinent data and information could be accessed throughout the company was a major point raised by respondents. Big data analytics helped those spot patterns and insights that were hiding in plain sight in the KM systems that were previously in use. The results demonstrate that when big data analytics are integrated with cloud-based KM systems, organizational capabilities can be greatly improved. Conventional KM systems have their work cut out for them by the sheer amount, speed, and diversity of big data. Cloud computing, according to this research, gives companies better data management skills, which help them deal with these problems. For instance, Hadoop and Apache Spark allowed for better administration of massive datasets and easier access to advanced analytics tools, which in turn led to quicker and better judgments. Another crucial factor was the combination of cloud and fog computing. With fog computing, real-time applications no longer had to deal with the latency problems that plague cloud-based systems.

Better decision-making and less privacy concerns due to data transit across faraway cloud servers were both made possible by fog computing, which moved processing closer to the source of the data.  When applied to a real-world business setting, the KM system proved to be quite useful. Big data analytics and cloud-based KM can remove obstacles to information sharing and encourage creativity by making data and knowledge available across departments. This solution demonstrated this principle in action by improving collaboration among staff. Data privacy, security, and the requirement for specialist knowledge are all issues that might use some work, though. An important conclusion drawn from the study is the need to combine data-driven insights with human discretion. In the context of company strategy, human knowledge is still necessary for making educated decisions, even though big data analytics can produce useful insights. Organizations need to put money into staff training and technology improvements if they want to combine data science with strategic decision-making. Organizational efficiency, decision-making, and knowledge sharing can be greatly enhanced by integrating big data analytics with a cloud-based KM framework. A complete and effective solution to modern information management problems can be achieved by combining cloud computing's scalability with fog computing's latency and privacy benefits. Achieving a successful implementation requires a steadfast commitment to reducing data security threats and guaranteeing access to competent human resources. 

Organizational efficiency, decision-making, and knowledge sharing are greatly improved when big data analytics are integrated with a cloud-based KM framework. Cloud infrastructure, in contrast to traditional KM systems, successfully handles the problems caused by the velocity, variety, and quantity of big data, according to the study. The proposed approach enabled real-time decision-making by improving the overall scalability, efficiency, and responsiveness of KM processes through the processing of big datasets with cloud computing platforms like Hadoop and Apache Spark.  The research highlighted the significance of merging big data and cloud computing to overcome latency and privacy challenges in cloud-based systems. Reduced privacy concerns, increased processing speed, and assurance of regulatory compliance are all benefits of cloud computing's ability to provide localized data processing. Research like these proves that distributed cloud-based KM framework solutions are essential for real-time data-driven decision-making. By removing data silos and making data easily accessible, the suggested KM framework encourages cross-departmental collaboration and innovation, as proven by qualitative case study findings. But the study did find some issues with the system that needed to be fixed for it to work right. These issues include things like being hard to set up at first, needing expert knowledge, and worries about data security. This study shows how important it is to find the right balance between data-driven insights and human knowledge. Insights from big data analytics must be interpreted by humans within the larger context of company strategy. Organizations should put money into staff training and technical developments if they want to incorporate data science into decision-making processes efficiently. Businesses can improve decision-making, expedite operations, and gain a competitive edge by combining cloud-based KM with big data analytics. This presents a viable answer to modern information management difficulties. Organizations should keep data security, privacy, and trained personnel as top priorities for the framework's effective rollout and industry-wide scalability.

The authors declare that they have no conflict of interest

No funding sources

The study was approved by the King Abdul Aziz University – Jeddah

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