Enterprise Cloud Analytics
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Abstract
Cloud computing revolutionize IT and business by offering computing as a utility over the internet. The evolution from internet to a cloud computing platform, the emerging development paradigm and technology and how these will change the way enterprise applications should be architected for cloud deployment plays an important role but these enterprise technologies are critical to cloud computing. New cloud analytics and business intelligence (BI) services can help businesses (organizations) better manage big data and cloud applications.Analysing and gathering business intelligence (BI) has never been easy, but today BI is complicated further by overwhelming amounts of data loads and the number of data entry and access points. New cloud analytics advancements may offer BI relief and even profit-increasing predictability for enterprises. These new cloud analytics applications can deliver functional capabilities that can be easily, quickly and economically deployed, producing tangible and measurable benefits far more rapidly than in the past. Many organizations that recognized, effectively analysing their business needs and providing the data they require to make the right business decisions depends on a combination of internally generated data and externally available data.
Related papers
Cloud Business Intelligence
International Journal of Advanced Networking and Applications (IJANA), 2019
Using Business Intelligence in the cloud is considered a key factor for success in various fields in 2018, about 66 percent of successful organizations in BI already using cloud. 86% of Cloud BI adopters choose Amazon AWS as their first choice, 82% choose Microsoft Azure, 66% choose Google Cloud, and 36% identify IBM Bluemix as their preferred provider of cloud BI services. In recent years, both Business Intelligence and cloud computing have undergone dramatic changes and advancements. The newest capabilities that these recent developments bring forth are introduced. In this paper the latest technologies in the field of Cloud (SaaS) BI is introduced. The paper shows also that many of the current problems in Cloud (SaaS) BI can be solved by enhance the performance and increase the use and acceptance of this technology. Many of the key characteristics of Business Intelligence systems tend to complement those of cloud computing systems and vice versa. Therefore, when integrated properly, these two technologies can be made to strengthen each other's advantages and eliminate each other's weaknesses.
Analytics in/for cloud-an interdependence: A review
Journal of Network and Computer Applications, 2018
Cloud computing has brought a paradigmatic shift in providing data storage as well as computing resources. With the ever-increasing demand for cloud computing, the number of cloud providers is also increasing evidently, which poses challenges as well as opportunities for consumers and providers. From a consumer point of view, efficient selection of cloud resources at a minimum cost is a big challenge. On the other hand, a provider has to meet consumers' requirements with sufficient profit in the fiercely competitive market. The relationship between cloud computing is truly symbiotic in the sense that cloud computing makes the practice of analytics more pervasive while analytics makes cloud computing more efficient and optimal in a lot of ways. In addressing these issues, analytics plays an important role. In this paper, we reviewed some important research articles, which focus on cloud computing from the viewpoint of analytics. Analytics and cloud computing are found to be quite interdependent. From analytics perspective, cloud computing makes available high-end computing resources even to an individual customer at an affordable price. We call this thread "Analytics in Cloud". From the point of view of cloud computing, efficient management, allocation, and demand prediction can be performed using analytics. We call this thread "Analytics for Cloud". This review paper is mainly based on these two threads of thought process. In this regard, we reviewed eightyeight research articles published during 2003-2017 related to the formidable duo of cloud computing and analytics.
BUSINESS INTELLIGENCE OVER CLOUD
It was the time when in a particular sector there were only 2-3 companies and competition was not as stiff as today. Each of these was having its own niche product and niche market. Data with the companies was also of smaller size and easily manageable which could be easily analyzed to identify hidden trends in the business to make effective strategy. But now things are getting changed and with the liberalization and globalization more people started moving towards manufacturing and service sector. More and more Multi National Companies are coming up leading to tighter competition among domestic players in providing quality product and service in time. In this era of high competition where Adam Smith way of doing work has become obsolete, and companies are now looking for new ways of doing work, organizations have now started moving towards business intelligence tools to analyze their continuously growing data which is multiplying exponentially and here lies the significance of BI to explore the company's big data to tap right opportunity at right time at right place to provide right service to right people, then only can a business stay ahead in global competition. While it will be easier for companies to gather and analyze the data with the stress on BI efforts, the challenge and opportunity will not be limited around collection of big data but the ability to translate it into strategic business decisions and demonstrate the right return on investments. To ensure this Business intelligence needs to move at the speed of business.
The Challenges of Business Intelligence in Cloud Computing
Indian Journal of Science and Technology, 2015
Background/Objectives: Cloud technology is one of the trending acquisitions by IT industry and cloud organizations. Though it comes with numerous challenges and cost barriers, it is still considered to be an important source of commercial analytics. Methods/Statistical Analysis: Cloud combines itself with facets available in the organizations. Necessarily it doesn't seek for technical domains but matches with all available resources. Business Intelligences is a wonder source that enhances itself with cloud terminology and technical nuances of cloud resources. Response time and aspects of business intelligence solutions goes hand in hand. This paper gives a clear understanding and deep insights on mathematical indicators of Business Intelligence (BI) and its encounters with cloud lexicons. Findings: This study analyses the crucial challenges that accompanies cloud technology when utilized with Business Intelligence. Major contribution lies around the crucial uncertainties that overrule the opportunities in cloud computing. Mathematical analyses like return on investment and payback value methods which are used to determine the economic handouts and proportions towards the obtainable tenets are also deliberated in this work. Agility is measured in terms of the potential users that bypass cloud resources through business intelligence. The study also encompasses various Business Intelligence chauffeurs. Applications/ Improvements: The concept of BI can be well handled through cloud tools like CloudSim, CloudAnalyst and Aneka using different models of cloud. The results can be improvised with the capability of BI methods.
Cloud Business Intelligence Survey
International Journal of Computer Applications, 2014
EVALUATION OF TOP CLOUD SERVICE PROVIDERS' BI TOOLS: A COMPARISON OF AMAZON QUICKSIGHT, MICROSOFT POWER BI, AND GOOGLE LOOKER
IAEME PUBLICATION, 2024
Business intelligence (BI) and data visualization tools play a pivotal role in today's businesses by facilitating data-driven decision-making. Amazon's AWS, Microsoft's Azure, and Google's GCP are the three foremost cloud service providers, providing extensive solutions encompassing computing, storage, databases, and advanced cloud services. This paper delivers a thorough comparative analysis of three top BI platforms: Amazon Studio, Microsoft Power BI, and Google Looker. It evaluates their features, user-friendliness, integration options, pricing structures, and customer support. By comparing specific features and examining real-world applications, this paper offers bits of knowledge that will assist organizations with picking the best tool to meet their particular requirements. The objective is to aid businesses and professionals in comprehending the strengths and weaknesses of each platform, thereby enabling them to make informed choices tailored to their unique needs.
Impact and Implications of Big Data Analytics in Cloud Computing Platforms
International Journal for Research in Applied Science & Engineering Technology (IJRASET), 2022
Big Data and Cloud Computing as two mainstream technologies, are at the center of concern in the IT field. Cloud Computing refers to the processing of anything, including Big Data Analytics, on the "cloud". The "cloud" is just a set of highpowered servers from one of many providers. They can often view and query large data sets much more quickly than a standard computer could. Essentially, "Big Data" refers to the large sets of data collected, while "Cloud Computing" refers to the mechanism that remotely takes this data in and performs any operations specified on that data. Cloud Computing services largely exist because of Big Data. Likewise, the only reason that we collect Big Data is because we have services that are capable of taking it in and deciphering it, often in a matter of seconds. The two are a perfect match, since neither would exist without the other. The combination of both yields beneficial outcome for the organizations. Not to mention, both the technologies are in the stage of evolution but their combination leverages scalable and cost-effective solution in big data analytics. Big data and Cloud computing are perfect combination. Besides that, there are also some real-time challenges to deal with. In this paper, discribes both the aspects. This paper introduces the characteristics, trends and challenges of big data. In addition to that, it investigates the benefits and the risks that may rise out of the integration between big data and cloud computing.
On the Modeling and Management of Cloud Data Analytics
Run-time Models for Self-managing Systems and Applications, 2010
A new era is dawning where vast amount of data is subjected to intensive analysis in a cloud computing environment. Over the years, data about a myriad of things, ranging from user clicks to galaxies, have been accumulated, and continue to be collected, on storage media. The increasing availability of such data, along with the abundant supply of compute power and the urge to create useful knowledge, gave rise to a new data analytics paradigm in which data is subjected to intensive analysis, and additional data is created in the process. Meanwhile, a new cloud computing environment has emerged where seemingly limitless compute and storage resources are being provided to host computation and data for multiple users through virtualization technologies. Such a cloud environment is becoming the home for data analytics. Consequently, providing good performance at run-time to data analytics workload is an important issue for cloud management. In this paper, we provide an overview of the data analytics and cloud environment landscapes, and investigate the performance management issues related to running data analytics in the cloud. In particular, we focus on topics such as workload characterization, profiling analytics applications and their pattern of data usage, cloud resource allocation, placement of computation and data and their dynamic migration in the cloud, and performance prediction. In solving such management problems one relies on various run-time analytic models. We discuss approaches for modeling and optimizing the dynamic data analytics workload in the cloud environment. All along, we use the Map-Reduce paradigm as an illustration of data analytics.
Cloud Business Intelligence for Smes Consortium
Foundations of Management, 2012
Business Intelligence technology for over 20 years is the market leader in analytical processing of data. As numerous market researches demonstrate Business Intelligence has substantial affect on global competitiveness of enterprises and on the stability of their position in the market, which is particularly important in times of economic downturn. Although main users of this technology are large companies and corporations, software vendors are still looking for solutions that are also available for the SME (Small and Middle Enterprises) sector and non-profit enterprises. One option available recently is possibility to use Cloud Computing environment. The article considers the opportunities and risks posed by the organization of Cloud Business Intelligence system on the example of using it in SME sector.
Cloud-Based Big Data Analytics—A Survey of Current Research and Future Directions
Advances in Intelligent Systems and Computing, 2017
The advent of the digital age has led to a rise in different types of data with every passing day. In fact, it is expected that half of the total data will be on the cloud by 2016. This data is complex and needs to be stored, processed and analyzed for information that can be used by organizations. Cloud computing provides an apt platform for big data analytics in view of the storage and computing requirements of the latter. This makes cloud-based analytics a viable research field. However, several issues need to be addressed and risks need to be mitigated before practical applications of this synergistic model can be popularly used. This paper explores the existing research, challenges, open issues and future research direction for this field of study.
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Analytics As A Service: Cloud Computing and the Transformation of Business Analytics Business Models and Ecosystems
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Due to the growth of data volumes, volatility and variety, business analytics (BA) become an essential driver of today’s business strategies. However, BA is mainly adopted by large enterprises because it may require a complex and costly infrastructure. As many companies strive to make better use of their data and to adopt data-driven management paradigms, cloud computing has been discussed as a costeffective approach to BA implementation challenges. To date, there has been little attention on the emerging class of analytical cloud services, “Analytics as a service” (AaaS). This article aims at demarcating AaaS as a cloud offering through an explorative research approach based on multiple case studies. Based on the analysis of 28 AaaS offerings, we derive a classification scheme for AaaS business model configurations and derive five business model archetypes. We discuss cloud computing’s implications on the business analytics ecosystem where partner networks play an important role at...
Beyond the Hype: Cloud Computing in Analytics
SSRN Electronic Journal, 2012
Machine learning (ML) techniques are becoming commonplace in business and research alike. With the automatization of data collection efforts, evermore data is being captured, rendering the task of extracting insightful patterns increasingly challenging. In addition to this "data avalanche" becoming evermore overwhelming, the usage of more computationally intensive algorithms in predictive analysis tasks also gives rise to new issues and challenges, so that a ML approach typically entails a trade off between computational efficiency and predictive performance. In recent years, however, new paradigms in analytics have been proposed geared towards solving these data and computational challenges, including cloud computing, distributed computing, and parallel computing approaches. We set out to discern one of these new hypes in analytics, cloud computing, and present a case study hereof which was performed at KU Leuven. In this study, we set up a benchmarking experiment using the Microsoft Windows Azure cloud platform with Techila Technologies middleware, and compare the results with those obtained in a non-parallelized setup. The results show that significant analysis speed-ups can be gained when performing computational tasks in the cloud.
Efficient Big Data Analytics and Management through the Usage of Cloud Architecture
Journal of Advances in Information Technology, 2016
Due to continuous growth of information systems to store business data and advent of new areas like mobile computing, users now need to use the enterprise applications through tablets, Smartphone, iPhone, laptops, and desktop computers. Effective decision making can be achieved with efficient information system. With the introduction of Business Intelligence tools organizations can now analyze the raw data and perform various activities like data mining, online analytical processing (OLAP), querying and reporting. Business Intelligence technology will help the managers to make better informed decisions. In order to perform data driven decision making business analytics practices are adopted. Cloud computing in recent days has gained a lot of prominence to store and process the business data. Organizations use cloud storage to manage data as they face challenges in local storage. But cloud also possesses certain challenges due to which organizations at large still find difficult to move to the cloud. Cloud computing can also be used to store and process big data. As Big data need to be analyzed for attaining maximum business value a new data model need to be proposed which have the properties those differs from traditional data model. In this paper we discuss that various challenges faced by organizations to move to cloud. We also propose that how the challenges can be overcome by the organizations so that cloud will be a promising architecture for better information management. Data model for Big data is also proposed in this paper.
What drives Big Data Analytics to Cloud
The key transformational platform for data between sourcing and storage is data analytics. Analysis of data can be achieved using either traditional stand-alone computers or by moving data analytics to the cloud. Given the volume, velocity and variety of data, knowledge extraction from new-age data has a complexity, which one would not have associated with data processing a decade back. This paper posits that ‘Big Data Analytics’ would slowly migrate towards Cloud Computing platforms and proceeds to find out the drivers that would accelerate this migration. Keywords: Big Data Analytics, Cloud Computing, Migration Drivers, Business Analytics, Factor Analysis
Cloud Computing and Big Data Analytics
2013
A large volume of data is generated by many applications which cannot be managed by traditional relational database management system. As organizations use larger and larger data warehouses for ever increasing data processing need, the performance requirements continue to outpace the capabilities of the traditional approaches. The cloud based approach offers a means for meeting the performance and scalability points of the enterprise data management providing agility to the database management infrastructure. As with other cloud environments, data management in the cloud benefits end users by offering a pay-as-you-go (or utility based) model and adaptable resource requirements that free up enterprises from the need to purchase traditional hardware and to go through extensive procurement process frequently. The data management, integration and analytics can be offloaded to public and/or private clouds. By using public cloud, enterprises can get processing power and infrastructure as needed, whereas with public cloud enterprises can improve the utilization of the existing infrastructure. By using cloud computing, enterprises can effectively handle the wide ranging database requirements with minimum effort, thus allowing them to focus on the core work rather than getting bogged down with infrastructure. Despite all these benefits, decision to move from dedicated infrastructure to the cloud based Data processing depends on several logistics and operational factors such as security, privacy, availability etc
Collaboration of Business Intelligence and Cloud Computing and Selecting the Best Cloud Business Intelligence Solution
AJIT-e Online Academic Journal of Information Technology, 2021
In this study, business intelligence concept and architecture were explained from data sources to reporting with many advantages provided to institutions in the first part. Then, both cloud computing technology with its service and deployment models and the characteristics of cloud computing experienced clarified in the second part of the study. The relationship between cloud computing and business intelligence and the concept arisen from this collaboration, cloud business intelligence, were represented with its benefits and obstacles experienced by companies using this technology in the third part. Four service providers as alternatives serving cloud business intelligence solutions were selected and the criteria were determined according to the needs of the company, that would like to use a cloud business intelligence software. After all the criteria are prioritized and the alternatives are determined, the best software was chosen by using the Analytic Hierarchical Process software...
Business Intelligence in the Cloud
Business Intelligence (BI) deals with integrated approaches to management support. In many cases, the integrated infrastructures that are subject to BI have become complex, costly, and inflexible. A possible remedy for these issues might arise on the horizon with "Cloud Computing" concepts that promise new options for a net based sourcing of hard-and software. Currently, there is still a dearth of concepts for defining, designing, and structuring a possible adaption of Cloud Computing to the domain of BI. This contribution combines results from the outsourcing and the BI literature and derives a framework for delineating "Cloud BI" approaches. This is the bases for the discussion of six possible scenarios -some of which within immediate reach today.
Biztool: A Flexible Cloud-Centric Framework for Business Analytics
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Towards a Business Intelligence Cloud
The requirements for Business Intelligence (BI) and reporting instruments are increasing since many years. Reporting instruments must be proactive, integrated, flexible and always available. They have to offer self-service functions and deal with the growing amount of data. In view of these requirements, the characteristics of cloud computing show a revolutionary character. First 'BI Cloud' offers are already available on the market. However, the lack of a standardized architecture as well as the lack of understanding of the features and advantages of analytical applications in the cloud causes that currently only a few companies use BI in the Cloud. To support the standardization and development of analytical applications delivered as a cloud service the University of Anonymous designed a reference model for BI in the Cloud in an ongoing research project. From this research project this position paper reports: The topics cloud computing and BI are merged, the resulting benefit of BI in the cloud is shown and our ap-proach for standardization is presented.
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