A Novel Approach in Business Intelligence for Big Data Analytics Using an Unsupervised Technique

There is a huge amount of data creating during the fourth industry revaluation and the data are generating explosively by various fields of the Internet of Things (IoT). The organizations are producing and storing the huge amount of data into the data servers every moment. This data comes from social media, sensors, tracking, website, and online news articles. The Google, Facebook, Walmart, and Taobao are the most remarkable organizations are generating most of the data in the web servers. Data comes into three forms as structured (text/numeric), semi structured (audio, video, and image) and unstructured (XML and RSS feeds). A business makes revenue from the analysis of 20% of such data, which is a structured form while 80% of data is unstructured. Therefore, unstructured data contains valuable information that can help the organization to improve the business productive, better decision-making, extract the insights, new products and services and understand the market conditions in various fields such as shopping, finance, education, manufacturing, and healthcare. The unstructured data are needed to be analyzed and distribute in a structured manner, that is required information’s are to be gathered through the data mining techniques are used to mining the data. In this paper, expose the importance of data analytics and data management for beneficial usage of business intelligence, big data, data mining and machine and data management. In addition, the different techniques that can be used to discover the knowledge and useful information from such data been analyzed. This can be beneficial for numerous users concern on text mining and convert complex data into meaningful information for researchers, analyst, data scientist, and business decision makers as well.

In today’s digital age, big data refers to the large amount of data companies can access for their daily activities. It encompasses data collected from various sources, which can later be interpreted, analyzed, and utilized for the company's benefit. The demand for large volumes arises from the market's need to scrutinize and leverage current data. With the increasing demand and availability of vast data also comes the need to find trends, patterns, and correlations in enormous amounts of unprocessed data to support data-driven decision-making, this process is known as big data analytics. With the advent of recent tools, these processes apply well-known statistical techniques—like regression and clustering—on larger datasets, so that they can be utilized to their utmost extent. Big data analytics is a subset of advanced analytics, involving complex applications that include predictive models, statistical algorithms, and what-if analysis powered by analytical systems. Proper and effective utilization and analysis of big data have emerged as a significant challenge for companies worldwide. It involves the intricate process of examining and perusing this large dataset, alongside insightful analysis of hidden patterns, correlations, market trends, and customer preferences. These insights help organizations make informed and intelligent business decisions. Data analytics technologies and techniques help organizations with the correct and necessary means to analyze datasets and extract new information. Simultaneously, all queries related to Business Intelligence (BI) are also addressed by the various business operations and actions performed. A prime example of big data analytics is the education industry, where a vast amount of student records, admission progress, assessments taken, and the overall growth of a student, along with other data, needs to be collected, aggregated, processed, and analyzed. In the field of education, big data analytics refers to the utilization of data for accounting, decision-making, predictive analytics, and numerous other purposes. Although this data varies to a colossal extent in type, quality, and accessibility, presenting substantial challenges, it also offers immense benefits.

The growing importance of big data has headed enterprises to advance their big data analytics capability to strengthen their firm performance. This study tests how big data capability impact on business intelligence infrastructure to achieve firm performance measures such as operational performance and marketing performance. This study is based on the recent literature on the knowledge-based view, big data capability, IT capability, and business intelligence. The primary survey of 272 responses from Chinese firms’ IT managers and big data analysts are used to uncover the relationship in the proposed model. The finding shows that the big data analytics capability significantly impacts on business intelligence infrastructure that in turn positively impact on operational performance and marketing performance. Further, the business intelligence infrastructure partially mediates between big data analytics capability and operational performance, and fully mediates between big data analytics capability and marketing performance. This research contributes to the information systems literature such as big data analytic capability, business intelligence, and firm performance measures, and thus offers grounds to extend more widespread studies in this field. This study adds to the literature on the theory and practical bases for big data capability and business intelligence infrastructure.

Big data is a field that treats ways to analyze, systematically extract information from, or otherwise deal with data sets that are too large or complex to be dealt with by traditional data-processing application software. Huge amount of data produced in business can be processed to get useful information using B usiness intelligence (BI). Business intelligence comprises the strategies and technologies used by enterprises for the data analysis of business information. BI technologies provide historical, current and predictive views of business operations. Mobile business intelligence (MBI) is the ability to access BI-related data such as Key Performance Indicators (KPIs), business metrics, and dashboards on mobile devices. The MBI provides users with necessary information and content, regardless of their location and time, in order to gain business insights through information analysis using applications optimized for mobile devices. Although in recent years, the business intelligence and analytics has gained an increasing amount of interest among researchers and practitioners, there is a little academic literature available on MBI. This survey is intended to study the adoption of Business Intelligence (BI) with special emphasis on mobile business intelligence in various fields during past 10 years and highlights the challenges faced by users in its implementation. The study also emphasized on relationship between MBI and big data analytics, application of MBI, and difficulties in using the MBI tools. Keywords : Mobile business intelligence, business intelligence, big data, analytic

Nowadays a lot of massive data is stored and typically, the data itself contains a lot of non-trivial but useful information. Data mining techniques can be used to discover this information which can help the companies for decision-making. However, in real life applications, data is massive and is stored over distributed sites. One of my major research topics is to protect privacy over this kind of data. Previously, the important characteristics, issues and challenges related to management of the large amount of data has been explored. Various open source data analytics frameworks that deal with large amount of Data analytics workloads have been discussed. Comparative study between the given frameworks and suitability of the same has been proposed. Digital universe is flooded with huge amount of data generated by number of users worldwide. These data are of diverse in nature, come from various sources and in many forms. To keep with the desire to store and analyze ever larger volumes of complex data, relational databases vendors have delivered specialized analytical platforms that come in many shapes and sizes from software only to analytical services that run in third party hosted environments. In addition new technologies have emerged to address exploding volumes of complex data, including web traffic, social media content and machine generated data including sensor data, global positioning system data.Big data is defined as large amount of data which requires new technologies and architectures so that it becomes possible to extract value from it by capturing and analysis process. Due to such large size of data it becomes very difficult to perform effective analysis using the existing traditional techniques. Big data has become a prominent research field, especially when it comes to decision making and data analysis. However, Big data due to its various properties like volume, velocity, variety, variability, value and complexity put forward many challenges. Since Big data is a recent upcoming technology in the market which can bring huge benefits to the business organizations, it becomes necessary that various challenges and issues associated in bringing and adapting to this technology are brought into light. Another challenge is that data collection may not have enough accuracy which will lead to a non-consistent analysis that can critically affect the decision based on this analysis. Moreover, it is clearly apparent that organizations need to employ data-driven decision making to gain competitive advantage. Processing, integrating and interacting with more data should make it better data, providing both more panoramic and more granular views to aid strategic decision making. This is made possible via Big Data exploiting affordable and usable Computational and Storage Resources. Many offerings are based on the Map-Reduce and Hadoop paradigms and most focus solely on the analytical side. Nonetheless, in many respects it remains unclear what Big Data actually is; current offerings appear as isolated silos that are difficult to integrate and/or make it difficult to better utilize existing data and systems. Since data is growing at a huge speed making it difficult to handle such large amount of data (Exabyte). The main difficulty in handling such large amount of data is because that the volume is increasing rapidly in comparison to the computing resources. The Big data term which is being used now a days is kind of misnomer as it points out only the size of the data not putting too much of attention to its other existing properties.If data is to be used to make accurate decisions in time it becomes necessary that it should be available in accurate, complete and timely manner. This makes the data management and governance process bit complex adding the necessity to make Data open and make it available to government agencies in standardized manner with standardized APIs, metadata and formats thus leading to better decision making, business intelligence and productivity improvements.This paper presents a discussion and evaluation for the most prominent techniques used in the processes of data collection and analysis in order to identify the privacy defects in them that affects the accuracy of big data. Depending on the results of this analysis, recommendations were provided for improving data collection and analysis techniques that will help to avoid if not all then most of the problems facing the use of big data in decision making. Keywords: Big Data, Big Data Challenges, Big Data Accuracy, Big Data Collection, Big Data Analytics.

The global Business Intelligence (BI) market grew by 7.3% in 2016 according to the Gartner report (2017). Today, organizations require better use of data and analytics to support their business decisions. Internet power and business trend changes have provided a broad term for data analytics - Big Data. To be able to handle it and leverage a value of having access to Big Data, organizations have no other choice than to get proper systems implemented and working. However, traditional methods are not efficient for changing business needs. Long time between project start and go-live causes a gap between initial solution blueprint and actual user requirements at the end of the project. This article presents the latest market trends in BI systems implementation by comparing agile with traditional methods. It presents a case study provided in a large telecommunications company (350 BI users) and the results of a pilot research provided in the three large companies: media, digital, and insurance. Both studies prove that agile methods might be more effective in BI projects from an end-user perspective and give first results and added value in a much shorter time compared to a traditional approach.

This paper examines big data and the opportunities it presents for improved business intelligence and decision making. Big data comes in multiple forms. It can be structured, semi-structured, or unstructured. The opportunity it presents is that there is so much of it and it is readily available to organizations. Organizations use big data for business intelligence (BI). They can apply analytics in BI activities to assess big data in order to gain new insights and opportunities for decision making. The problem is that oftentimes the data is of poor quality and it contains personal information. This paper explores these issues and examines the importance of effective data management in facilitating sound business intelligence. The Master Data Management methodology is reviewed and the importance of management support in its deployment is emphasized. With the advent of new sources of big data from IoT devices, the need for even more management involvement is stressed to ensure that organizational BI yield sound decisions and that use of data are in compliance with new regulations.

Organizational business intelligence and decision making using big data analytics

This paper proposes an ontology of big data analytics and examines how to enhance business intelligence through big data analytics as a service by presenting a big data analytics services-oriented architecture (BASOA), and applying BASOA to business intelligence, where our surveyed data analysis showed that the proposed BASOA is viable for developing business intelligence and enterprise information systems. This paper also discusses the interrelationship between business intelligence and big data analytics. The proposed approach in this paper might facilitate the research and development of business analytics, big data analytics, and business intelligence as well as intelligent agents.KeywordsBig data analyticse-commerceBusiness intelligenceIntelligent agents

This chapter introduces the implementation of Business Intelligence (BI), thus explaining the application overview of BI, the components of BI, the practical implementation of BI, the business value of BI, the trends in implementing BI, and the guidelines for implementing BI. BI is a broad category of business applications and technologies for gathering, providing access to, and analyzing data for the purpose of helping business enterprise users make better business decisions. BI enlarges business performance, thus leading to higher level of efficiency, better quality outputs, better marketing decisions, and lessened risk of business failure in order to gain a competitive advantage in the global business environments. It is important to create and develop a BI system to enable the useful transformation of information into the valuable knowledge for enhancing BI in organizations. Implementing BI will increase organizational performance and achieve business goals in modern business.

In the Present scenario, there is huge amount of data is generated every moment which projects data analytics plays an important role in Business Intelligence. The data is analyzed and processed in almost all the technologies and departments but, it has somehow less contribution in the education sectors. According to the society, the growth of IT sector is not a valid aspect. So in order to justify never the IT stream will not fall in. For achieving this, data analytics are processed with the dynamic data set collected from the social environments. These structured and unstructured data sets will be grouped in data layer then analyzed in analytics layer and foretelling by using Business Intelligence reporting tool in reporting layer to provide a graphical representation for this issue. With the help of this, the layered conceptual BI model which we proposed will enrich the growth of intakes in IT streams will always be static in end user layer.

Recent advances in computing have lead to diverse applications in wide domains such as cyber-forensics, data science, business analytics, business intelligence, computer security, Web technology, and Big data analytics. Data analytics refer to broad term where many of the areas such as cyber-physical systems (CPS), Internet of Things (IoT), Big data, machine learning and data mining overlap among each other. However, there are subtle differences among these areas. Data analytics form as one of the key components of these wide areas in computing. For example, in IoT the data that are collected from various devices need to analyze for inferring the outcomes of it. Hence, data analytics need to be carried on it. There are various platforms available for data analytics. In this chapter, a brief overview of the term data analytics, different types of data, different types of analytics, and the analytical architecture is first discussed. It is later followed by the different phases that are involved in the lifecycle of the data analytics project and the interconnection of Big data and Hadoop ecosystem.

Social media data analytics for business decision making system to competitive analysis

While many studies on big data analytics describe the data deluge and potential applications for such analytics, the required skill set for dealing with big data has not yet been studied empirically. The difference between big data (BD) and traditional business intelligence (BI) is also heavily discussed among practitioners and scholars. We conduct a latent semantic analysis (LSA) on job advertisements harvested from the online employment platform monster.com to extract information about the knowledge and skill requirements for BD and BI professionals. By analyzing and interpreting the statistical results of the LSA, we develop a competency taxonomy for big data and business intelligence. Our major findings are that (1) business knowledge is as important as technical skills for working successfully on BI and BD initiatives; (2) BI competency is characterized by skills related to commercial products of large software vendors, whereas BD jobs ask for strong software development and statistical skills; (3) the demand for BI competencies is still far bigger than the demand for BD competencies; and (4) BD initiatives are currently much more human-capital-intensive than BI projects are. Our findings can guide individual professionals, organizations, and academic institutions in assessing and advancing their BD and BI competencies.

The huge amount of data and complexity of decisions in the current information age requires decision makers to utilize information analysis tools for supporting business decisions. This is also the case for social network sites which control huge amounts of data just waiting to be transformed from information to valuable knowledge through Business Intelligence methods. These techniques are not yet widely in use within companies whose core business revolves around user generated content. This research conducts a qualitative research to provide more knowledge and a deeper understanding of a Business Intelligence approach which supports the business model of companies exploiting a Social Network Site. Available Business Intelligence process models do not take the organizational aspects into account as continuous process improvement elements. Therefore, this work proposes the new method: the Social Network Intelligence Process (SNIP) Method. The SNIP Method and its related management information items were validated through a series of expert interviews and an in-depth single case study at the leading Dutch social network site.