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Please find the attached question and resources. 5-7 references and clear APA format with abstract and conclusion. question.png sample_doc.docx uncertainty_in_big_data_analytics.pdf Unformatted Attachment Preview Running head: PROJECT MANAGEMENT AT MM Project Management at MM xxxxxxxxxxxxxxxxxxxxx xxxxxxxxxxxxxxx Xxxxxx xxxxxxx 1 PROJECT MANAGEMENT AT MM 2 Abstract Project management is the process of aligning activities to make a final useful product. A project requires skilled personnel and total dedication for it to be successful. McCann has an experience of many years in this field. Therefore, he was mandated with overseeing the implementation of the green meters project. He utilized several organizational factors to enhance the chances of successful completion. He also faced many challenges due to inflexibility in the company’s culture and the use of outdated systems. Corporate politics is also hindering the smooth implementation of the green project. Some factors that enhanced the chances of success of the undertaking include support from the board, employees’ coordination, and McCann’s experience. It is crucial for all stakeholders in an organization to cooperate for project implementation to continue without hitches. This paper outlines organizational factors that increase the likelihood of the green meters project’s success, the aspects that hinder the undertaking’s implementation, and actions that McCann should take to salvage the situation. Keywords: Project management, corporate politics, schedule PROJECT MANAGEMENT AT MM 3 Project Management at MM Project management is a crucial activity that requires total dedication and skilled personnel for it to be successful. At MM, Werner McCann, a top project manager, was tasked with the designing and manufacturing of green meters, a new product for the company. The objective of the project was to enable customers to manage their electrical consumption better. Everyone at the company was excited about the project (Mckeen & Smith, 2015). It was seen as something that will propel MM to the top of the industry. Therefore, McCann was under pressure to deliver the project. Identifying organizational factors that increase the likelihood of the undertakings success, those that hinder its implementation, and the things that McCann needs to do to salvage the situation give an invaluable insight into the process of project management. Organizational Factors that Enhanced the Success of the Project Several organizational factors At MM enhanced the implementation of the green meters project. One of them was support from the top leadership. The board of management provided executive sponsorship to ensure that the process would continue smoothly (Mckeen & Smith, 2015). McCann received all the funding he needed to accomplish the huge task that was before him. Another organization factor was staff coordination. Employees from the various departments at the company were excited about the successful implementation of the project. Therefore, they worked hard in the duties they were assigned. Stakeholders output is required for the smooth implementation of a project (Kerzner, 2017). Everyone longed for the day that green meters would be ready for manufacture and distribution to customers. McCanns experience and skills were other factors that increased the chances of the project’s likelihood of success. He was hired for the position because he demonstrated the ability to oversee the successful completion of many undertakings. Skilled personnel is an essential aspect of a PROJECT MANAGEMENT AT MM 4 successful project management process (Schwalbe, 2015). McCann was a highly motivated individual and would do anything in his power to achieve his goals. The factors mentioned above are clear indications that MM is well managed. Organizational Factors that Hindered the Success of the Project Several organizational factors decreased the likelihood of success of the project. The first was corporate politics. The departmental heads were placing barriers to McCanns progress path. For instance, Tompkins had refused to allow for the switching of the data dictionary. He claimed that he was not being updated on the steps that have been taken in the implementation process. However, that was a lame excuse. He had a representative at the steering committee. Moreover, he continuously received progress reports. Corporate politics is a significant barrier to the proper running of a company (Meredith, Mantel Jr, & Shafer, 2017). It seems some people at the company wanted McCann to fail. Another organizational factor that affected the smooth implementation of the project was the lack of adequate skilled personnel (Meredith et al., 2017). McCann admitted openly that the project would put his team under huge pressure. There were few employees with technical knowhow at the company. Therefore, McCann was only left with the option of outsourcing some services. The situation created a logistic problem that threatened to disrupt the implementation schedule. The top leadership was reluctant to agree to this proposition. It was seen as an additional cost, which would make the project more expensive. Overall, highly skilled people in the technical field are rare to find. MM has low flexibility in its operations. The company is based on traditional culture and processes that are outdated. The lack of flexibility hindered the progress of the green meters project. McCann wanted to use an outside-in approach when designing the product (Mckeen & PROJECT MANAGEMENT AT MM 5 Smith, 2015). He wanted to integrate marketing, making orders, manufacturing, delivery, and maintenance into one process. However, MM was used to having separate systems for each operation. Moreover, they were using outdated tools that would not permit multitasking. The board was not ready for changes. They insisted that McCann works with the existing resources. Such a conservative mentality in this era is dangerous. The contemporary world is dynamic, and only flexible companies will survive. Things to Do To salvage his reputation and save the company from the imminent loss, McCann needs to take swift and decisive actions. He should write a letter to the CEO complaining about the conduct of Tompkins. No progress can be made before the data dictionary is switched. Thus, Tompkins is blocking the implementation process without concrete reasons. He is just envious of McCanns success. Tompkins considers him a threat to his position. Therefore, he is doing everything to make sure McCann fails to deliver. McCann should convene a crisis meeting with the management in the shortest time possible. The current situation requires the intervention of the executives. Moreover, McCann will only waste time if he continues trying to resolve issues on his own. Courage is an important aspect when one is facing a difficult situation (Mir & Pinnington, 2014). It enables a person to look for solutions instead of wasting time doing nothing. During the meeting, he should present all the barriers he has faced in his quest to implement the project. Besides, he should present the possible solutions to the current quagmire. The board will agree to most of his recommendations because they also want the project to succeed. McCann should also inform the project implementation team of the resolutions reached during the crisis meeting. He should prepare them for the new challenge that is glaring at them. PROJECT MANAGEMENT AT MM 6 Everyone should be updated on the changed mandate that they are required to perform. As a result, the project will be implemented in record time. Conclusion Werner McCann was tasked with the designing and manufacturing of green meters, an important product for the company. The objective of the project was to enable customers to manage their electrical consumption better. All stakeholders at the company were excited about the project. It was seen as something that will propel MM to the top of the industry. Therefore, McCann was under pressure to deliver the project. Several organizational factors boosted the chances of the project’s success. One of them was the unwavering support from the executive. The top leadership at the company wanted the project to succeed as it would have earned the company a huge amount of revenue. Another factor was the coordination that the staff exhibited during that period. McCann was assured of professional assistance from the various departments in the company. Moreover, his experience in the field of project management gave him the confidence to take up the challenge of overseeing this undertaking. However, some organizational factors hindered the smooth implementation of the project. They included inadequate skilled personnel in the technical field, corporate politics, and lack of flexibility. McCann needs to take some steps to steer this project back to the right path. He should first convene a crisis meeting with the executive, and then inform the project implementation team about the resolutions reached. He should also report all employees that are blocking the smooth progress of the undertaking. Such actions will lead to the successful completion of the green meter project. PROJECT MANAGEMENT AT MM 7 References Kerzner, H. (2017). Project management: A systems approach to planning, scheduling, and controlling. Hoboken, NJ: John Wiley & Sons, Inc. Mckeen, J. D., & Smith, H. (2015). IT strategy: Issues and practices. Upper Saddle River, NJ: Pearson Education, Inc. Meredith, J. R., Mantel Jr, S. J., & Shafer, S. M. (2017). Project management: A managerial approach. Hoboken, NJ: John Wiley & Sons, Inc. Mir, F. A., & Pinnington, A. H. (2014). Exploring the value of project management: Linking project management performance and project success. International Journal of Project Management, 32(2), 202-217. doi: https://doi.org/10.1016/j.ijproman.2013.05.012 Schwalbe, K. (2015). Information technology project management. Boston, MA: Cengage Learning. (2019) 6:44 Hariri et al. J Big Data https://doi.org/10.1186/s40537-019-0206-3 Open Access SURVEY PAPER Uncertainty in big data analytics: survey, opportunities, and challenges Reihaneh H. Hariri* , Erik M. Fredericks and Kate M. Bowers *Correspondence: rhosseinzadehha@oakland. edu Oakland University, Rochester, MI, USA Abstract Big data analytics has gained wide attention from both academia and industry as the demand for understanding trends in massive datasets increases. Recent developments in sensor networks, cyber-physical systems, and the ubiquity of the Internet of Things (IoT) have increased the collection of data (including health care, social media, smart cities, agriculture, finance, education, and more) to an enormous scale. However, the data collected from sensors, social media, financial records, etc. is inherently uncertain due to noise, incompleteness, and inconsistency. The analysis of such massive amounts of data requires advanced analytical techniques for efficiently reviewing and/ or predicting future courses of action with high precision and advanced decisionmaking strategies. As the amount, variety, and speed of data increases, so too does the uncertainty inherent within, leading to a lack of confidence in the resulting analytics process and decisions made thereof. In comparison to traditional data techniques and platforms, artificial intelligence techniques (including machine learning, natural language processing, and computational intelligence) provide more accurate, faster, and scalable results in big data analytics. Previous research and surveys conducted on big data analytics tend to focus on one or two techniques or specific application domains. However, little work has been done in the field of uncertainty when applied to big data analytics as well as in the artificial intelligence techniques applied to the datasets. This article reviews previous work in big data analytics and presents a discussion of open challenges and future directions for recognizing and mitigating uncertainty in this domain. Keywords: Big data, Uncertainty, Big data analytics, Artificial intelligence Introduction According to the National Security Agency, the Internet processes 1826 petabytes (PB) of data per day [1]. In 2018, the amount of data produced every day was 2.5 quintillion bytes [2]. Previously, the International Data Corporation (IDC) estimated that the amount of generated data will double every 2 years [3], however 90\% of all data in the world was generated over the last 2 years, and moreover Google now processes more than 40,000 searches every second or 3.5 billion searches per day [2]. Facebook users upload 300 million photos, 510,000 comments, and 293,000 status updates per day [2, 4]. Needless to say, the amount of data generated on a daily basis is staggering. As a result, techniques are required to analyze and understand this massive amount of data, as it is a great source from which to derive useful information. © The Author(s) 2019. This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creat​iveco​mmons​.org/licen​ses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. Hariri et al. J Big Data (2019) 6:44 Advanced data analysis techniques can be used to transform big data into smart data for the purposes of obtaining critical information regarding large datasets [5, 6]. As such, smart data provides actionable information and improves decision-making capabilities for organizations and companies. For example, in the field of health care, analytics performed upon big datasets (provided by applications such as Electronic Health Records and Clinical Decision Systems) may enable health care practitioners to deliver effective and affordable solutions for patients by examining trends in the overall history of the patient, in comparison to relying on evidence provided with strictly localized or current data. Big data analysis is difficult to perform using traditional data analytics [7] as they can lose effectiveness due to the five V’s characteristics of big data: high volume, low veracity, high velocity, high variety, and high value [7–9]. Moreover, many other characteristics exist for big data, such as variability, viscosity, validity, and viability [10]. Several artificial intelligence (AI) techniques, such as machine learning (ML), natural language processing (NLP), computational intelligence (CI), and data mining were designed to provide big data analytic solutions as they can be faster, more accurate, and more precise for massive volumes of data [8]. The aim of these advanced analytic techniques is to discover information, hidden patterns, and unknown correlations in massive datasets [7]. For instance, a detailed analysis of historical patient data could lead to the detection of destructive disease at an early stage, thereby enabling either a cure or more optimal treatment plan [11, 12]. Additionally, risky business decisions (e.g., entering a new market or launching a new product) can profit from simulations that have better decisionmaking skills [13]. While big data analytics using AI holds a lot of promise, a wide range of challenges are introduced when such techniques are subjected to uncertainty. For instance, each of the V characteristics introduce numerous sources of uncertainty, such as unstructured, incomplete, or noisy data. Furthermore, uncertainty can be embedded in the entire analytics process (e.g., collecting, organizing, and analyzing big data). For example, dealing with incomplete and imprecise information is a critical challenge for most data mining and ML techniques. In addition, an ML algorithm may not obtain the optimal result if the training data is biased in any way [14, 15]. Wang et al. [16] introduced six main challenges in big data analytics, including uncertainty. They focus mainly on how uncertainty impacts the performance of learning from big data, whereas a separate concern lies in mitigating uncertainty inherent within a massive dataset. These challenges normally present in data mining and ML techniques. Scaling these concerns up to the big data level will effectively compound any errors or shortcomings of the entire analytics process. Therefore, mitigating uncertainty in big data analytics must be at the forefront of any automated technique, as uncertainty can have a significant influence on the accuracy of its results. Based on our examination of existing research, little work has been done in terms of how uncertainty significantly impacts the confluence of big data and the analytics techniques in use. To address this shortcoming, this article presents an overview of the existing AI techniques for big data analytics, including ML, NLP, and CI from the perspective of uncertainty challenges, as well as suitable directions for future research in these domains. The contributions of this work are as follows. First, we consider uncertainty challenges in each of the 5 V’s big data characteristics. Second, we review several Page 2 of 16 Hariri et al. J Big Data (2019) 6:44 techniques on big data analytics with impact of uncertainty for each technique, and also review the impact of uncertainty on several big data analytic techniques. Third, we discuss available strategies to handle each challenge presented by uncertainty. To the best of our knowledge, this is the first article surveying uncertainty in big data analytics. The remainder of the paper is organized as follows. “Background” section presents background information on big data, uncertainty, and big data analytics. “Uncertainty perspective of big data analytics” section considers challenges and opportunities regarding uncertainty in different AI techniques for big data analytics. “Summary of mitigation strategies” section correlates the surveyed works with their respective uncertainties. Lastly, “Discussion” section summarizes this paper and presents future directions of research. Background This section reviews background information on the main characteristics of big data, uncertainty, and the analytics processes that address the uncertainty inherent in big data. Big data In May 2011, big data was announced as the next frontier for productivity, innovation, and competition [11]. In 2018, the number of Internet users grew 7.5\% from 2016 to over 3.7 billion people [2]. In 2010, over 1 zettabyte (ZB) of data was generated worldwide and rose to 7 ZB by 2014 [17]. In 2001, the emerging characteristics of big data were defined with three V’s (Volume, Velocity, and Variety) [18]. Similarly, IDC defined big data using four V’s (Volume, Variety, Velocity, and Value) in 2011 [19]. In 2012, Veracity was introduced as a fifth characteristic of big data [20–22]. While many other V’s exist [10], we focus on the five most common characteristics of big data, as next illustrated in Fig. 1. Volume refers to the massive amount of data generated every second and applies to the size and scale of a dataset. It is impractical to define a universal threshold for big data volume (i.e., what constitutes a ‘big dataset’) because the time and type of data can influence its definition [23]. Currently, datasets that reside in the exabyte (EB) or ZB ranges are generally considered as big data [8, 24], however challenges still exist for datasets in smaller size ranges. For example, Walmart collects 2.5 PB from over a million customers every hour [25]. Such huge volumes of data can introduce scalability and uncertainty problems (e.g., a database tool may not be able to accommodate infinitely large datasets). Many existing data analysis techniques are not designed for large-scale databases and can fall short when trying to scan and understand the data at scale [8, 15]. Variety refers to the different forms of data in a dataset including structured data, semi-structured ... 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