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Data Analytics: Business Proposal
Kai li
ALY6080
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Data Analytics: Business Proposal
Statement of the Purpose
The analytics marketplace and Business intelligence (BI) have been crowded with
excellent tools that deal with software installment. The overcrowding of these software tools has
been used for various purposes such as data storage, conversion, management, and budgeting in
an organization through software’s automation of the AI data processing solutions. Docdigitizer
has been one company that aims to provide such services to modern companies. This business
proposal aims to outline the major questions found in the customers’ review bar on the
Docdigitizer website. This question is what is the expectation of the customers, and how the
Docdigitizer would meet their customers’ expectations (Docdigitizer, 2022). Moreover, this
question would enable Docdigitizer to customize hyper-automation over raw information and
unstructured data for individuals and companies seeking their services.
Troyanos (2020) states that software industries and pundits of marketing campaigns
inflate the true values of BI tools. However, most online service provider start-ups that use BI are
very strong, only to fail in the first few months. The case of Docdigitizer has been different since
its beginning in 2017. For Docdigitizer to continue serving and growing its service, it needs to
address this question. The main goals of Docdigitizer are to help organizations automate their
labor-intensive, manual, document-based workflow and to optimize customers’ experience
(Docdigitizer, 2022). This business proposal will enable Docdigitizer to optimize its service by
improving customers’ experience and expectations.
Scope of the Project
Project Objective
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This project proposal aims to develop a constructive proposal for Docdigitizer that would
answer what the customers would expect from its services and what strategies the Docdigitizer
needs to take to increase customers’ expectations.
Deliverables
Software Products
The main software product that will be used in developing the project will be the
Docdigitizer system tabs. These tabs will be accessed from the IOS, Chrome, Android, and Mac
OS software. These are some of the popular software that people use to access services from the
Docdigitizer.
Equipment Replacement
The replacement of equipment before taking over the project will enhance clear project
findings that would be reliable. Such replacement will include the installation of better and fast
internet accessing desktops, laptops and printers, and other network-related assets.
Milestone
The project proposal will take about three months to cover the questions fully and
develop solutions. The first month would focus on analyzing customers’ expectations;
developing strategies to improve customers’ expectations would take two months, then analyzing
the new comments of the customers.
Customer Review
Joana V, key account manager, small business (50 or few employees)
Alexandre L, CEO, small business (50 or few employees)
Background
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Dues to the dramatic change in the scope of information technology IT in recent years,
there has been much shifting in how business operations are managed. There has also been the
rehabilitation of how well online gargets provide services such as communication, shopping, and
other online services. Consequently, the increase in online services has also provided an agent
with the need to meet customers’ expectations, repurchasing intentions, and satisfaction with the
services provided. According to Maslowska et al. (2020), perceived customers’ enjoyment of the
services provided positively impacts the repurchasing intention. Moreover, satisfaction increases
the chances of the customers repurchasing. The repurchasing, satisfaction and customers’
enjoyment lead to positive customers review. Therefore, it is pertinent for any online service
provider to understand what customers need when they visit certain websites. If companies
become aware of the customers’ needs, they would improve customers’ experience and thus
facilitate positive reviews, which in turn persuade other customers to purchase their services.
Literature Review
Most online customers and partners have been engaging in online searches to reduce the
risks of purchasing new products and services (Maslowska et al., 2020). Moreover, most
customers tend to believe what other people have commented about the services or products
provided by such online drives. Moreover, almost every customer looks at the product picture,
information relating to those products and services, and the reviews commented by other
customers. (Maslowska et al., 2020) suggest that the customers expect to see the review stars,
verified purchase, and helpfulness. This article might help the project identify how Docdigitizer
would increase their customers’ service expectations and examine their responses.
Ashfaq et al. (2019) proposed that lowering customers’ defection rates might benefit
online service providers. Moreover, managing both customers’ and employees’ turnover rates and
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loyalty may be more profitable for them. According to Ashfaq et al. (2019), service quality
facilitates companies’ success. However, there has been a gap between companies’ perceived
actual performance and customers’ expectations. The article also proposes that companies need
to distinguish between the minimum expectations of the customers and their desired services.
Therefore, this article suits the project proposal because it shows that the measure of the quality
of services originates from the quality of goods and services that organizations provide.
Design and Data Collection
The proposal for meeting customers’ expectations and improving the existing strategies
for Docdigitizer design would include developing a team to examine customers’ reviews. Later,
the team would analyze the most reviewed comment or complaints by generalizing related
reviews. The project manager will also develop another team to develop alternative solutions to
the problem based on improving customers’ experience. The project would take about four
months with a budget of about 125 dollars to ensure the project is successful.
The methods of collecting data would include sampling information for about fifty recent
from the Docdigitizers review section. For these responses, the project concentrates on the
comments that show that customers were improperly served. Another method of collecting data
would include selection and instruments. Such instruments would include questionnaires with
questions and ranges of how the customers are satisfied with the Docdigitizer services. Other
instruments and selection would include repurchase intention (RPI), perceived ease of use
(PEOU), satisfaction (SAT), and Expectation (EPT) (Ashfaq et al., 2019). Each of these
selections would have ranges of one to four, where one indicates strongly agree, two agree, three
disagree, and four strongly disagree.
Implementation Methodology and Strategies
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Using the Smart PLS 3 software, we can analyze the data results and estimate the
correlation and constructive customers satisfaction statistics. Furthermore, we would establish
the relationship between the results between the selections and instruments. After coming up
with the customers’ needs, such as display of other reviews, fast service accessibility, diversified
services, and improved and eases customers’ interaction with the website, we would develop
strategies to install these needs in the website. Some of the methodologies and strategies to use
would include training the customers online, providing remedies, revising the website, and
improving it according to the findings. Moreover, direct cutover would be applied where we
would disregard some of the old system data.
Conclusion
Indeed, customers’ review satisfaction and repurchasing possibility are interrelated and
are determined by the kind of service they receive from the service vendors. In a nutshell, the
project proposal above has analyzed the state of Docdigitizer and how it might improve its
customers’ expectations. The project proposal has addressed the purpose statement for the project
and the key objective for developing the proposal. Moreover, the proposal has outlined the
proposal’s deliverables and the background information about the project. A precise literature
review of two articles that would be important references for the development of the proposal
has been discussed. Lastly, the project proposal has concentrated on the design and data
collection processes as well as the implementation methodologies and strategies for the project
proposal.
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References
Ashfaq, M., Yun, J., Waheed, A., Khan, M. S., & Farrukh, M. (2019). Customers’ expectation,
satisfaction, and repurchase intention of used products online: Empirical evidence from
China. Sage Open, 9(2), 2158244019846212.
https://journals.sagepub.com/doi/full/10.1177/2158244019846212
Docdigitizer. (2021, March 11). How DocDigitizer grew 6x in 2020! Docdigitizer. Error!
Hyperlink reference not valid.
Docdigitizer. (2022). DocDigitizer Reviews. Error! Hyperlink reference not valid.
Maslowska, E., Segijn, C. M., Vakeel, K. A., & Viswanathan, V. (2020). How consumers attend
to online reviews: an eye-tracking and network analysis approach. International Journal
of Advertising, 39(2), 282-306.
https://www.tandfonline.com/doi/full/10.1080/02650487.2019.1617651
Troyanos, K. (2020). Use Data to Answer Your Key Business Questions. Harvard Business
Review, 21.
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Integrated Experiential Learning
Detecting and Preventing Fraud with Data Analytics: Business Proposal
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Detecting and Preventing Fraud with Data Analytics: Business Proposal
Statement of the Purpose
Fraud is not a new issue, but the financial crisis has recently implicated that it mainly
occurs during recession periods. Thus, managers need to learn and initiate anti-fraud measures to
reduce such cases and help the economy’s slow recovery. Fraud leads to significant financial
risks to companies, which threaten their image and profitability (Kayalvizhi et al., 2018).
Consequently, there has been the development of IT systems to detect and counter the cases of
fraud and create competitive advantage among organizations. There is a need for internal staff in
various companies to develop mechanisms that would analyze and integrate data analytics in the
processes like transactions to prevent Fraud (Kayalvizhi et al., 2018). The use of data analytics in
such mechanisms is universally essential because such processes cannot be done manually.
Therefore, as Docdigitizer increases its revenue and marketing size, there is a need to
develop mechanisms that would detect and prevent fraud cases, especially in recession periods.
In doing so, Docdigitizer has to use data analysis tools that effectively translate the problem. The
data analytics tools would be appropriate because they can be applied at all stages of the
interactive and complex process of developing an anti-fraud process (Kayalvizhi et al., 2018). In
addition, the analytic tools would help Docdigitizer forecast a future recession, thus optimizing
production and the management of the business inventories. Docdigitizer would be able to focus
on improving revenue streams, improving the value of services, and understanding customers’
behaviours.
Scope of Work
Project Overview
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The project involves developing and designing processes to detect and counter fraud
issues in the Docdigitizer Company using data analytic tools. The financial staff will use the data
analytic tools to access the website and measure the chances of fraud occurrences. Moreover, the
task and scope of this project will follow the timeline outlined in the deliverable.
Project Objectives
The business proposal aims to develop a constructive proposal for Docdigitizer that
would help the company initiate programs to detect and counter the development of fraud cases.
The project also aims to outline how technology can be implemented to detect fraud and enhance
its prevention inside the Docdigitizer organization.
Project Tasks
Website design, development, and installation of the site content
Installation of initial data content in the already designed and developed website to
authorize the use of fraud detection software in the initial setup.
Information Architecture
The initial page set up on the site will include both lists of pages and features of the
intended fraud prevention mechanism.
Deliverables
Website design
We will upgrade the Docdigitizer website design using the Forensic data analytics (FDA)
tools within two weeks to install fraud detecting and prevention tools on the website
Draft Version of the Site
After completing the design of the website and installing, the data analytic tools such as
Structured Query Language (SQL), Tableau, and Python tools, we will develop a draft version of
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the website for piloting and evaluation before coming up with the final version of the website the
fraud prevention features.
Milestone
The project proposal will take about two months to cover the purpose and fully develop a
program to detect fraud cases and prevent them with the systems of the project. The first month
will entail the navigation of potential chances of Fraud cases and choosing appropriate data
analytics tools. The second month will concentrate on initiating the tools in the Docdigitizer
website and system, developing a draft version, and finally coming up with the final draft of the
Background
Economic Fraud and crime have been the main intractable problems among many global
companies. As a result, organizations and international companies have been losing a lot of
revenue to fraud and other related crimes. Most organizations affected by such occurrences are
developing companies and organizations that lack the proper means to detect fraud within their
systems. Therefore, fraud prevention and detection mechanisms are vital, especially today,
because they are the tools that maintain and sustain economic growth and productivity
conditions.
Moreover, companies operate with large volumes of data, especially due to the
development of technology, making it necessary for them to implement continuous monitoring
processes. These continuous processes would help identify the anomalies in the organization’s
behavioural patterns and data streams that are potentially fraudulent. The use of such a
monitoring process would help companies have proper directions and recommend the use of
control activities. The monitoring process may be enhanced through technology such as data
analytics that prevents the occurrence of fraudulent practices.
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Literature Review
The detection of financial fraud has become a critical task among accounting
practitioners since it has remained one of the critical issues discussed in accounting literature and
topics. Three factors determine the potentiality of fraud to occur. These factors include
opportunity, pressure, and rationalization (Tang & Karim, 2018). With the uncertainty and
unpredictability of fraudsters’ techniques and uncertainty, the process of detecting fraud needs to
be diligent judgmental, and skilful. Furthermore, the integration of brainstorming sessions with
Big Data may broaden the size, strength, and information results through analytical processes
(Tang & Karim, 2018). The audit team may promote this integration through Big Data tools in
every brainstorming process, such as data integration, collection, fraud detection, identification
prevention, and documentation of the process.
Design and Data Collection
The proposal for detecting and preventing fraud with data analytics at Docdigitizer would
include designing a team of website developers in conjunction with the audit team to develop a
process that would detect and prevent fraud cases. The design would use Forensic data analytics
(FDA) tools that are sophisticated. For instance, the IT group would use spreadsheet tools such
as Microsoft Excel to develop a draft and final fraud detection mechanism (Mikalef et al., 2019).
The audit team will use data analytic tools such as python to test the effectiveness of the process.
The process, methods, and strategies for collecting data will include exploratory,
descriptive, and cluster analysis. The team will use the exploratory analysis process to explore
the relations between fraud variables and the Docdigitizer data. Therefore, the team will be able
to develop a hypothesis concerning the implementation of the anti-fraud mechanism. The
descriptive process will try to answer the questions of uncertainty and unpredictability of the
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occurrence of fraud in the company (Alexandru et al., 2020). Moreover, the cluster method will
help determine the most appropriate method to develop a counterbalance mechanism to prevent
fraud cases.
Implementation Methodology and Strategies
We would use the main methods to implement the process, including strategic analysis
and operational analysis. The operational analysis will be used for a short period to develop the
draft mechanism. The strategy uses comparative case analysis and event analysis (Choi et al.,
2018). The operational process will exploit the current information in Docdigitizer Company,
aiming to detect fraudulent change efficiently. The process will improve most of the manual
activities at the company and the conditions that would cause fraud cases.
On the other hand, the strategic analysis will offer a macro overview of fraudulent
mechanisms. The strategic analysis will involve analytic tools and pieces of software that will
improve the mechanism for preventing fraud. Furthermore, the process will enhance the
detection of great variation by utilizing digital statistical tools (Choi et al., 2018). This is because
the strategic analysis methodology uses statistical tools with an extensive intuitive interface and
powerful engines for statistics.
Conclusion
The paper’s purpose was to develop an anti-fraud and proactive data detection mechanism
that Docdigitizer would use to prevent and detect fraud in the company. There are no specific
toolkits developed to detect or prevent fraud. Moreover, there is no specific data method
recommended to counter the occurrences of fraud cases. Therefore, the paper has described the
purpose of the project. Additionally, the paper has outlined the scope of work, the background
and literature review, and the design and data collection. Lastly, the paper has concluded with an
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Implementation Methodology and Strategies that would be used to implement the mechanism for
preventing fraud at Docdigitizer.
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References
Alexandru, V. A., Bolisani, E., Andrei, A. G., Cegarra-Navarro, J. G., Martínez, A. M., Paiola,
M., … & Zieba, M. (2020). Knowledge management approaches of small and mediumsized firms: a cluster analysis. Kybernetes.
Choi, T. M., Wallace, S. W., & Wang, Y. (2018). Big data analytics in operations
management. Production and Operations Management, 27(10), 1868-1883.
Kayalvizhi, R., Khattar, K., & Mishra, P. (2018). A survey on online click fraud execution and
analysis. International Journal of Applied Engineering Research, 13(18), 13812-13816.
Mikalef, P., Boura, M., Lekakos, G., & Krogstie, J. (2019). Big data analytics and firm
performance: Findings from a mixed-method approach. Journal of Business
Research, 98, 261-276.
Tang, J., & Karim, K. E. (2018). Financial fraud detection and big data analytics–implications on
auditors’ use of fraud brainstorming session. Managerial Auditing Journal.
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Integrated Experiential Learning
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Integrated Experiential Learning
Statement of the Problem
When we were developing the XN project for Docdigitizer as our sponsor company, we
consulted various sources to identify the most appropriate problem proposal to attempt to argue
about. In our endeavors, we realized that fraud and cybercrime have been among the major
problems facing modern companies (Tang & Karim, 2018). Therefore, the group decided to
develop fraud as the major problem likely to face our sponsor company. The group also realized
that machine learning and the use of data analytics would be effective in analyzing the problem.
Moreover, the group had done several bibliographies on the appropriate information to assist in
developing the project. According to Tang & Karim (2018), opportunity, pressure, and
rationalization are the factors that determine the potentiality of fraud to occur in most
organizations.
Furthermore, the rise of Bayesian optimization (BO) has become increasingly effective
and high sample efficiency to deal with computationally expensive black-box optimization
problems. This is because much of black-box algorithms’ computational optimization is highly
expensive (Min et al., 2020). As a group, we identified that since Docdigitizer has been
categorized as an automation company thus, it would be effective to integrate such models as
Bayesian Optimization with data analytics.
Scope
The scope of work aimed to develop automated fraud detection systems for Docdigitizer.
The system would use machine-learning algorithms to detect and prevent fraudulent documents
from being entered into the system. The scope of work in the project began by providing an
overview of what it entailed. Next, the project proposal provided the objectives, milestone
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deliverables, and various tasks. The main purpose of the project milestone and deliverables was
to guide users on the steps to follow and to measure the project’s success rate. The project also
consisted of tasks such as developing a classifier for document classification. The classifier
would be trained with examples of fraudulent documents and non-fraudulent documents.
Furthermore, we were developing a feature set for the classifier, consisting of features
such as size, shape, color, and texture. Using these features, we would be able to develop a
model that can predict whether a document is fraudulent. The project scope also entailed
developing visualization tools to visualize our results using Jupyter notebooks.
Background
The project’s background is to develop a system that will detect and prevent fraud at
DocDigitizer. This will be achieved by integrating analytics tools and visuals into their existing
systems and developing new analytics tools for the company. In our group project for XN, we
identified various analytics tools and visualizations that would be used to detect and prevent
fraud at DocDigitizer. While developing the background of the project, we identified several
ideas from the article reviews.
According to Rayner et al. (2022), companies operate with large volumes of data,
especially due to the development of technology, making it necessary to implement continuous
monitoring processes. These continuous processes would help identify the anomalies in the
organization’s behavioral patterns and data streams that are potentially fraudulent. The use of
such a monitoring process would help companies have proper directions and recommend the use
of control activities. The first step in this project will be to research the current state of analytics
tools and their capabilities. We then created a list of potential tools that would be useful for
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detecting and preventing fraud. The second step will be to interview experts in the field to
determine which tools are most effective for detecting and preventing fraud at the company.
Design and Data Collection
The project required a design that would use machine-learning algorithms to predict
whether a document is fraudulent. This would be done by analyzing the text of a document using
NLP tools such as NLTK with other forensic tools like Docdigitizer for data collection. We also
endeavored to develop a tool that will help us analyze the text to decide whether the document is
fraudulent. We analyzed how we can collect the data needed for our analysis using different tools
like Excel or Power BI (Mikalef et al., 2019). Our team also identified several data collection
methods to help us get the most out of our project. We chose to do this in three different phases,
and we will be using four different tools at each phase.
First, we will collect data using Exploratory Data Collection Methods. These methods
involve collecting much data without worrying about which data to include or how it should be
organized. We used Excel tool to create a spreadsheet containing all of our data and then use that
spreadsheet as a guide for how we should organize our final report. Second, we will collect data
using Descriptive Data Collection Methods. These methods involve gathering information about
the variables within an article or study so that we can describe them in detail (Mikalef et al.,
2019). We will use another tool called Excel again, but this time to create detailed descriptions of
each variable in our report. Third, we will collect cluster analysis data using Cluster Analysis
Data Collection Methods. These methods involve grouping variables together based on their
similarities and differences rather than just relying on counts or averages alone; they also allow
us to combine multiple variables into groups to examine their correlations more clearly.
Implementation Methodology
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According to Zhang et al. (2019), Building Information Modeling (BIM) technology is
essential for the long-term success of any project. The implementation methodology that I
propose is a combination of both quantitative and qualitative approaches. For example, we will
use low-cost tools to identify fraudulent activities, such as data collection and validation
techniques. We also intend to use high-cost tools such as sophisticated algorithms to detect
anomalies and fraudulent activities. For example, the sponsor can use log data to identify unusual
activity. The sponsor can then compare this information with the company’s risk management
policy to determine if there is a suspicious pattern. If so, the sponsor should take immediate
action.
What Kind Of Data Will You Propose To The Sponsor?
The kind of data we proposed for the project included text analytics and natural language
processing to detect and prevent fraud at DocDigitizer. Text analytics is a field of computer
science that uses techniques from artificial intelligence (AI) to analyze text data. Linguistic
techniques are used to analyze the language in documents or other types of communication.
Natural language processing (NLP) is a field of computer science dealing with the processing of
natural languages within computers and other machines. These two fields are well suited for
analyzing DocDigitizer’s data because they allow for efficient processing of large quantities of
data and rapid discovery of patterns in such data. They also have been used in many other
contexts, including online advertising, customer service, and e-commerce websites.
What Kind of Analysis Can Be Done with the Data?
We needed to use statistical models to predict outcomes based on past data. These models
would be used for forecasting future trends and predictions based on past data. For example, if
there has been an increase in fraud cases recently, then we should be prepared for more future
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cases and predict when such an event might happen next. Moreover, we would use regression
models analysis to gives a function that depicts the relationship between dependent, response,
and independent variables. According to Rayner et al. (2022), the TCR protocols use two phases:
the identity cluster and the trusted cluster. The DTN works on the proximity principle that
involves the identification of several nodes. Some trusted clusters learn gradually and progress to
better in the later periods. These nodes learn through the five steps of the experiential network;
experience, sharing, process, generalizing, and applying their experience in the future
(Alexandru et al., 2020). Therefore, we would perform both regression and forecasting analysis
using the trusted clusters.
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References
Alexandru, V. A., Bolisani, E., Andrei, A. G., Cegarra-Navarro, J. G., Martínez, A. M., Paiola,
M., … & Zieba, M. (2020). Knowledge management approaches of small and mediumsized firms: a cluster analysis. Kybernetes.
Mikalef, P., Boura, M., Lekakos, G., & Krogstie, J. (2019). Big data analytics and firm
performance: Findings from a mixed-method approach. Journal of Business Research, 98,
261-276.
Min, A. T. W., Gupta, A., & Ong, Y. S. (2020). Generalizing transfer Bayesian optimization to
source-target heterogeneity. IEEE Transactions on Automation Science and Engineering,
18(4), 1754-1765
Rayner, R., Gouldman, C., & Tobin, C. (2022). The Ocean Enterprise 2015‐2020: A Study of US
New Blue Economy Business Activity. Marine Technology Society Journal, 56(1), 29-34
Tang, J., & Karim, K. E. (2018). Financial fraud detection and big data analytics–implications on
auditors’ use of fraud brainstorming session. Managerial Auditing Journal
Zhang, J., Xie, H., Schmidt, K., Xia, B., Li, H., & Skitmore, M. (2019). Integrated experiential
learning-based framework to facilitate project planning in civil engineering and
construction management courses. Journal of Professional Issues in Engineering
Education and Practice, 145(4), 05019005.

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