Toronto University Interaction Between Big Data Characteristics Research Paper Please find the attached question and resources. 5-7 references and clear AP

Toronto University Interaction Between Big Data Characteristics Research Paper Please find the attached question and resources. 5-7 references and clear APA format with abstract and conclusion. Running head: PROJECT MANAGEMENT AT MM
Project Management at MM
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PROJECT MANAGEMENT AT MM
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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culture and the use of outdated systems.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
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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
undertaking’s 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. McCann’s
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
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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 McCann’s 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
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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
McCann’s 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
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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
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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
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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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