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A write-up on The impact of home working on employeE motivation. Focus on the UK construction industry. 21 Impact of Business Intelligence on Project Management MSc. PROJECT MANAGEMENT UNIVERSITY OF NORTHAMPTON Abstract This study is associated with the assessment of the impacts of business intelligence in the management of projects. Business intelligence is an expansive domain- related to the management of the information related to the various aspects of business management and functionality. This study has utilised primary and secondary research methods for the development of the research findings related to the achievement of the overall aim of this study. Through the findings of the study, it has been assessed that business intelligence tends to play a significant role in the development of optimised business functions and overall profitable projects. Moreover, the findings of the study are in conformance with the associated past literature which also authenticates the notion of business intelligence asserting influence on the development of overall enhanced approaches of management of projects. The conclusive findings developed through the surveys highlight the domains of business project management augmentation through the effective utilisation of the business intelligence to render enhanced strategic management, efficient functionality and overall profitable business project ventures. Table of Contents CHAPTER ONE: INTRODUCTION 4 1.1 Introduction 4 1.2 Background and Rationale 4 1.3 Study Aim and Objectives 5 1.4 Research Questions 6 1.5 Scope and Limitations of Study 6 1.6 Study Layout 7 CHAPTER TWO: LITERATURE REVIEW 8 2.1 Overview and Significance of Business Intelligence 8 2.2 Typical Application of Business Intelligence 10 2.2.1 Product Sales Management 10 2.2.2 Exception Handling (Management by Exception) 10 2.2.3 Management of the Facts (Management by Fact) 11 2.2.4 Customer Relationship Management (CRM) 11 2.2.5 Product Innovation and Promotion 11 2.3 Business Intelligence for Project Risk Management 12 2.4 Development of the Business Intelligence Network Architecture System 15 2.5 Data Warehousing 17 2.6 Attributes of Data for BI 19 2.6.1 Integrated Data 19 2.6.2 Non-volatile 20 2.6.3 Update Over Time 21 2.6.4 Multi-granularity 21 2.7 Modes of Data Warehousing 22 2.7.1 Virtual Storage Mode 22 2.7.2 Storage Based on Relational Table 23 2.7.3 Classification Mode 23 2.7.4 Regression Mode 24 2.7.5 Time Series Mode 24 2.7.6 Clustering Mode 24 2.8 Discrepancies in Data Warehousing 25 2.9 Current Paradigms of Business Intelligence 28 2.10 Conclusion and Critical Analysis of Literature Review 30 CHAPTER THREE: RESEARCH METHODOLOGY 35 3.1 Introduction 35 3.2 Research Methodologies 35 3.3 Research Design 38 3.3.1 Data Collection 38 3.3.2 Data Analysis 39 3.4 Ethical Considerations 39 CHAPTER FOUR: FINDINGS AND DISCUSSION 41 4.1 Introduction 41 4.2 Findings and Discussions 41 CHAPTER FIVE: CONCLUSION AND RECOMMENDATIONS 56 5.1 Conclusion and Recommendation 56 References 61 Appendix……………………………………………………………………………………..65 CHAPTER ONE: INTRODUCTION 1.1 Introduction Among the various contemporary concepts related to the innovative approaches of business management, the utilisation of business intelligence is considered a necessary norm in the existing business environments (Cohen, 2017). Business intelligence refers to the notion of the collective information and data related to the existing paradigms of the respective business markets, the consumer trends, the related competition data and all relative business information required for optimum functioning of the businesses in the globalised environment of today (Rouhani & Lecic, 2018). This study will be focused towards the assessment of the requirement and significance of business information for the development of optimum project management approaches and functions. 1.2 Background and Rationale With the advancements of modern technology and the increase in the corresponding utilisation in various domains of business management, the paradigms of the codes of conduct in this respect have been altered drastically (Rouhani & Lecic, 2018). This includes the introduction of numerous contemporary approaches for the management of the various business functions. Moreover, the globalised integration of the informatics and data related to the development of the associated paradigms through the means of the internet has further changed the relative approaches of information management required for the optimum functioning of the various operations reflecting towards viable and profitable business functions (Elbashir et al., 2008; Trieu, 2017). Business intelligence in the existing paradigms of today’s modern world includes not only the information associated with the organisational statistics and information- rather, the business intelligence in the modern world of today entails the various unconventional data collection and processing approaches (Cohen, 2017). This includes features such as big data analysis and the use of the internet of things for developing patterns and trends for optimised business functionalities. Moreover, the introduction of various contemporary fields of operations- such as game theory analysis and decentralised economies- have resulted in further complicated systems of managing the various business functions (Williams & Williams, 2010). However, the development of optimum approaches resulting in the acquirement and processing of the information intrinsically related to the business operations tends to reflect towards the development of optimum approaches for the rendering of effective and profitable business functions (Williams & Williams, 2010; Turban et al., 2013). Business intelligence is currently being applied in numerous contexts of business management. This includes the domains of sales management, inventory management, warehouse management, systems management, operations management, supply chain management, customer relationships management and production management, including numerous other related business management domains (Rouhani & Lecic, 2018). The major challenge in the paradigms of the current bombarded information systems is to assess the necessary information to develop patterns and protocols for using the respective information for the augmentation of the corresponding business functions and operations. However, there are numerous existing businesses and projects which tend to operate on the conventional approaches of business management- uninformed of the various advantages and benefits of the effective utilisation of business intelligence for the optimisation of the respective operative paradigms (Williams & Williams, 2010; Trieu, 2017; Rouhani & Lecic, 2018). Hence, this study will be pertained towards the notion of the impact of business intelligence on the project management paradigms. 1.3 Study Aim and Objectives This study will aim to “assess the impacts of business intelligence on project management”. In order to effectively attain the relative aim of this study, the following objectives have been defined: · To evaluate the various factors associated with the business intelligence and its optimum utilisation · To appraise the approaches of business intelligence in the augmentation of the management of business projects · To assess the influence of business intelligence on the development of optimum functioning of project management 1.4 Research Questions · What are the impacts of business intelligence on management of projects? · What are the various approaches used for utilisation of business intelligence in management of projects? · What are the factors influencing effective utilisation of business intelligence for management of projects? 1.5 Scope and Limitations of Study As this study is associated with the analysis of the impacts of business intelligence on the management of projects, the primary focus of this study will be pertained towards the evaluation of the respective impacts. Although the study will include the various aspects related to the assessment of the impacts, the study is limited through the various constraints related to the study limitations of student academic projects. Moreover, the inclusion of the limited variables for the study analysis also present the relative limitations of this research study. Furthermore, this study is limited by the availability of the various literature being included in this study. Being a student academic researcher, the study is also constrained by the availability of related literature on the freely available websites and data bases- in addition to the limited databases provided by the university. In addition, the selection of the research methodology also reflects towards the various limitations related to this particular research study. Overall, the study limitations are copious, which will also be highlighted in the final section of this study. 1.6 Study Layout This study has been divided into five chapter- the foremost of which is the introduction to the overall topic under research. This chapter has introduced the relative background as well as the aim and objectives to be achieved through the completion of this study. Moreover, the second chapter is related to the critical review of past literature associated with this study. This will encompass the related conceptual frameworks as well as the included terminologies and concepts required for the completion of this study. The third chapter of this dissertation will include the relative research methodology being selected for this study. This will discuss the related research methodologies in addition to the enumeration of the exclusive study design being used for this particular study. Against the mentioned methodology for this study, the fourth chapter will enumerate the various results acquired through the effective implementation of the stated research approach. In addition, the findings of the study will be discussed against the associated secondary literature in order to augment and emphasise the various findings acquired through the implementation of the research methodology for this study. Finally, the fifth chapter of this study will conclude the various findings of this study- in addition to the highlighting of the relative recommendations against the conclusive findings of the completed study. CHAPTER TWO: LITERATURE REVIEW 2.1 Overview and Significance of Business Intelligence In recent years, the traditional domestic business intelligence field has made the project deep target and lean management modularisation as an important means to create high-quality enterprises and implement project management, and combined with the implementation of various large-scale business intelligence projects, more prominent lean management (Turban et al., 2013). Leading role in cost control and optimisation of deep target management in the field of business intelligence. The implementation of lean management in business intelligence enterprises can rationally use enterprise resources, promote profits and disadvantages, and maximise economic benefits (Chen et al., 2012; Turban et al., 2013). In todays society, the network economy characterised by e-commerce is gradually maturing, and the emergence and widespread use of the network revolutionises our way of life (Collier, 2012). E-commerce has made the players on the arena back to the same starting line – both traditional and emerging e-commerce companies have the same opportunities in the era of network economy. Emerging .com companies are launching B2B or B2C transactions over the Internet, establishing platforms for direct sales and service between suppliers or between suppliers and customers, competing for market share from traditional companies; traditional companies are not willing to show weakness and have changed strategies while maintaining the traditional channels, it opened up the second battlefield of e-commerce (Minelli et al., 2012; Ramakrishnan et al., 2012; Kimball et al., 2015).   The data comes from the feedback of each application system within the respective project or organisation where the BI systems are being integrated, and data mining can discover new knowledge from the existing data warehouse and automatically correct the content in the knowledge base at any time, so the knowledge base in BI is a dynamic structure (Collier, 2012). But business intelligence also has shortcomings. The goal of business intelligence is the same as decision Support System (DSS), in order to improve the efficiency and accuracy of corporate decision-making (Minelli et al., 2012). However, BI provides valuable and decision-making information and knowledge through data analysis and knowledge discovery tools. Users must use this knowledge and knowledge to make judgments based on this knowledge and experience, make decisions, and have little ability to make intelligent decisions (Collier, 2012; Minelli et al., 2012). It does not provide functions such as solution generation, program coordination, and program evaluation, as in a specialised decision support system, and it does not have the ability to make group decisions (Minelli et al., 2012).  An obvious feature of the enterprise management model is the need to undertake the project by means of bidding. First, the enterprise must undertake the project, and then organise the construction by project, which has typical project management characteristics, that is to say the enterprise is based on project management (IşıK et al., 2013). Combined with business intelligence, it can extend the information application to the enterprises refined management, development strategy, resource planning and decision support; apply business intelligence, based on the information subsystem of the construction enterprise, can be the transactional data of the construction enterprise Transforming into analytical data, revealing and excavating the information and knowledge contained in the data, and playing a role in project management (Khan & Quadri, 2012; IşıK et al., 2013). The main functions of business intelligence technology in project management of construction enterprises include two aspects (IşıK et al., 2013): 1 integrating data, solving information island problems, and effectively promoting information sharing in project management; 2 extracting, processing and organically combining massive structured data and non-structure Data, access and create information and knowledge that is conducive to decision making in project management. 2.2 Typical Application of Business Intelligence 2.2.1 Product Sales Management It includes product sales strategy, sales volume analysis, factor analysis that affects product sales, and product improvement forecasting. The sales model is established through system sales information, and the overall sales model and regional and departmental sales models are divided (Khan & Quadri, 2012). Analyse sales volume and sales strategy for sales models that produce different results, analyse and evaluate the factors affecting sales, and implement product launch and removal plans for corresponding product sales plans according to different sales environments to increase the amount of marketing. Through historical data analysis, it is also possible to establish a predictive model that increases sales (Debortoli et al., 2014).  2.2.2 Exception Handling (Management by Exception)  It is a typical example of a business intelligence data mining application that can monitor its performance goals in real time and continuously, and the business intelligence system can monitor its deviation from the planned goals (Debortoli et al., 2014). When the deviation is too large, the system will notify the corporate responsibility supervisor of the deviation status by various communication methods, such as e-mail, at the first time, thereby reducing the enterprise risk and improving the companys revenue. Specific applications include credit card analysis, fraud monitoring in industries such as banking and insurance (Debortoli et al., 2014).  2.2.3 Management of the Facts (Management by Fact) Regardless of goal management or exception management, the power behind the support comes from the facts. E, the system that maintains business operations, accumulates countless facts and knowledge in daily transactions (Chiang et al., 2012; Wixom et al., 2014). The business intelligence system combines corporate goals, exceptions, and facts to enable managers to further analyse causes or trends, query and detect relevant information. In the age of lack of information, management relies more on personal experience and intuition to manage and make decisions. In the era of knowledge economy, enterprises must implement fact management, relying on fantasy and feelings, and using business intelligence to make scientific decisions based on understanding the daily business situation of enterprises (Wixom et al., 2014).  2.2.4 Customer Relationship Management (CRM) Customer is a key factor in the survival of an enterprise. Customer relationship management (CRM) is an important task for enterprises. Through the customer relationship management subsystem of business intelligence, companies can analyse customer spending habits and consumption trends, improve customer satisfaction, and then take corresponding countermeasures to enhance customer retention, cultivate loyal customers, and maintain good customer relationships (Debortoli et al., 2014; Larson & Chang, 2016).  2.2.5 Product Innovation and Promotion New products promote the development of enterprises, but the development and promotion of new products must be based on a certain market (Larson & Chang, 2016). Good corporate history information can be used to predict market demand trends; data warehouse technology in business intelligence systems to solve which products need to be updated, whether it needs to carry out some kind of advertising campaign; what kind of users are targeted for advertising campaigns; Most likely, the potential customers of the company, the direct advertising strategy for such customers can outweigh the untargeted gain; predict the future earnings of the project and so on (Debortoli et al., 2014).  2.3 Business Intelligence for Project Risk Management The risk is dangerous or damage to the project, it is a possible event, it may not happen, it may happen, and the project risk is a potential problem in the process of the event that may hinder the success of the project or may exist during the implementation of the project (Wixom et al., 2014). Teams risk itself is a possible event, and only when the conditions that trigger the risk or the conditions for the risk of conversion exist, will the risk actually arise. What is called project risk management is to control the conditions of conversion and risk triggering through various methods such as transfer, avoidance and mitigation. Risk management has certain practical significance for the scope of the project and the cost of the project scope and the actual progress of the project as well as the plan estimation (Chiang et al., 2012; Wixom et al., 2014). The prevention of risk management is very beneficial to understanding the nature of the project, so that the employees of the company can better understand the disadvantages of the project. And the advantages, do a good job of project risk prevention, and other knowledge of the integration project management is also beneficial (Wixom et al., 2014). In addition, risk management is also a form of insurance for the project, which can effectively reduce the adverse events of the project, and risk management is also the project implementation process. In an investment, there will be a lot of costs involved, but in any case, the cost management of the project will not exceed the project revenue (Debortoli et al., 2014; Wixom et al., 2014). IT project risk management is similar to general project risk management. The main goal is to reduce the probability of potential risks, so that the project can achieve the highest possible profit while completing quality on time and quality. While business intelligence (BI) can have a profound impact on leadership decisions, in an increasingly complex environment, enterprise development, mergers and acquisitions, and business model changes mean enterprise IT infrastructure It may also become more complex, and this complexity makes enterprises more attractive to an architecture that is scalable and flexible, because users need a new level of information integrity, integration, execution, and availability (Chiang et al., 2012; Alhyasat & Al-Dalahmeh, 2013). If the pre-implementation is not adequate, and the expectations of BI for all industries are too high, it is likely to lead to huge problems, pressures and more potential risks in the implementation of a BI project. How to minimise the risk of the BI project itself is undoubtedly a problem that every CIO must consider in the implementation of BI projects (Alhyasat & Al-Dalahmeh, 2013). To ensure maximum impact from BI, the management of BI projects needs to take into account all aspects of the business, from strategy to implementation, project management and delivery. A well-designed and executed BI strategy must be aligned with the companys business goals, and technology, data, processes, organisational structure, customer focus, management requirements, internal support, etc. should be considered together (Alhyasat & Al-Dalahmeh, 2013; Moro et al., 2015).  The risk of inaccurate definition of BI user groups and requirements is undoubted, and the user is the centre of any BI project. But because BI systems often have different user groups, it is not an easy task to know who the end user is (Moro et al., 2015). From the ordinary employees who use daily reports to the high-end users who are engaged in data analysis on a full-time basis, each group of people has different requirements for the use of BI products. Some BI system implementers have spent a lot of time completing a certain requirement, but found that users have no interest in the content displayed by the BI system, which is not what this user needs to see most (Moro et al., 2015; Debortoli et al., 2014). There are also many companies that do not have clear goals. Even BI is only used to make unnecessary comparisons with users in the same industry, rather than based on the development needs of enterprises (Alhyasat & Al-Dalahmeh, 2013). This makes the BI project more difficult and increases the risk. The most common mistake made by technicians is to treat users as software design experts (Alhyasat & Al-Dalahmeh, 2013; Wixom et al., 2014).  BI projects are often a wide-ranging work, from data collection, clean-up, storage, to data calculation, analysis, presentation, to information release. And monitoring and so on will be involved. Although from the perspective of the big environment, various technical means are relatively mature, for a large amount of data, with modern technology (Demirkan & Delen, 2013). In some enterprises that are preparing to build a new BI system, business leaders often generate some overly optimistic ideas. They often set goals very high, and the scope of implementation is too broad. The system above the line will immediately solve all management decision problems. This will not only affect the budget and duration of the project, but often expand the scope too much and often fail to effectively plan and utilise people and resources (Demirkan & Delen, 2013; George et al., 2015).  The risk that the senior leadership does not pay enough attention to BI is the analysis and summary of the basic data. Logically speaking, employees are responsible for providing data, which is then analysed by the BI system. The final result is seen by the leadership (Wixom et al., 2014). However, in many practical work, this is not the case. Some senior leaders basically do not look at the reports of the BI system, or they have the following employees manually sort out the reports and show them to the leaders. In this process of reorganisation, employees who are not responsible will correct some errors, but the manual changes may be only the final result, not the basic data. This means that the data in the BI system is always wrong (Wixom et al., 2014; Moro et al., 2015). This is obviously not the result that managers want. In addition, BI projects require the companys spending from top to bottom. This requires leaders to value BI projects and give BI administrators greater power. Only in this way can we send staff from other departments to support the operation of the BI system (George et al., 2015). It is a pity that many failed projects or projects with unsatisfactory results are caused by insufficient power of the B project manager and the inability to uniformly allocate the resources of the enterprise (Wixom et al., 2014).  2.4 Development of the Business Intelligence Network Architecture System In the implementation strategy and actual implementation of BI, there is a big disconnect between the current systems. The existing BI system answers many corporate questions, but the answers to these questions are usually limited to a specific environment and department (Wixom et al., 2014; George et al., 2015). On the contrary, many questions that are specific to the entire enterprise or market level are not well answered. Another limitation is that the data warehouse system focuses on the analysis of historical data, rather than directly operating the entire enterprise information value chain. Gartner defines BI networks as: a flattened network that can be interconnected for knowledge users, through which users of knowledge can share and exchange data, metadata, data/process models, analysis results, and decision information (Wixom et al., 2014). Through this network, knowledge users can make more accurate decisions faster and better. Gartner had predicted that by 2008, BI network products will become mainstream BI system products, and its network products will include collaborative work components, workflow components and enterprise application integration (EAI) software (Wixom et al., 2014; Alpar & Schulz, 2016). These software components are of great value in nature, and at the same time increase the coverage and intrinsic value of BI network products. However, current developments asserts that the technological advancements have rendered even more enhanced changes (Alpar & Schulz, 2016).  The solution is more open, scalable, and customisable, providing a customised interface while ensuring core technology. In response to the unique needs of different enterprises, BI system makes the system personalised when it provides the same technology as the data warehouse technology in the business intelligence system, that is, adding its own code and solution based on the original solution, enhancing Customised interfaces and extended features; Provides enterprises with customised tools based on business intelligence platforms, giving the system greater flexibility and scope of use (Demirkan & Delen, 2013; Wixom et al., 2014; George et al., 2015; Sharda et al., 2016).  From individual business intelligence to embedded business intelligence is a major trend in business intelligence applications, namely embedding business intelligence components in the enterprises existing application systems, such as finance, manpower, sales, etc., so that the transaction processing system in the general sense has the characteristics of business intelligence (Wixom et al., 2014). Considering a component of the BI system rather than the entire BI system is not a simple matter, such as applying OLAP technology to an application system, a relatively complete business intelligence development process, such as enterprise problem analysis, solution design, and prototype system development (Wixom et al., 2014; George et al., 2015). Processes such as system applications are indispensable. Transition from traditional functions to enhanced functions. Enhanced business intelligence capabilities are business intelligence capabilities that were implemented in comparison to earlier SQL tools (Alpar & Schulz, 2016). In addition to the traditional BI system functions, most of the current BI systems have implemented the functions of the data analysis layer. Data mining and enterprise modelling are applications that BI systems should strengthen to better improve system performance (Thamir & Poulis, 2015).  2.5 Data Warehousing The concept of data warehouse is proposed by the book “Building Data Warehouse” published by WH Gongn in 1992 (Thamir & Poulis, 2015). The introduction of data warehouse is based on the rapid development of relational database, parallel processing and distributed technology. In order to solve the problem that information technology has a large amount of data in the development process, on the other hand, useful information is very poor solution (Wixom et al., 2014; George et al., 2015). According to the definition … Kindly see below the meeting details and project breakdown structure. Assignment Topic: The Impact of home working on employee motivation, Focus on the UK construction industry Project Structure First Page: Title Page Second Page: Abstract Third Page: Acknowledgment  Table of Contents List of Figures List of Tables Abbreviation Chapter one: Introduction This chapter should include the following: Background of the project: why am I doing it? Research aims and objectives Include research questions and scope Methodology on a couple of paragraphs Data Collection Ethical consideration Chapter Two: 1- 2,000 words This chapter should include the following: Literature review Talk about the history, concerns definitions Chapter Three: 3,000 - 4,000 words This chapter should include the following: Methodology Take Saunders 2007 onion layer as one: Talk about what they are, Qualitative/Quantitative, black or white, etc. Questionnaires Data (to both employees and employers) Sample size/how many people: Should be sent to 50/60 people Chapter Four: Results and evaluation This chapter should include the following: 10 to 12 questions and each question should have four to five variants: Strongly agree, agree, neutral, disagree, strongly disagree. Results should be shown via graphs, charts, etc Chapter Five: Discussion and conclusion This chapter should include the following: Results from interviews and questions Results from the literature review in chapter two What I obtained and also the limitations (another section in the chapter) Future recommendation (according to my research) References Appendices