Computer Science
Golden Gate University San Francisco CH12 COSO Framework of Internal Controls Paper

Golden Gate University San Francisco

Question Description

I’m trying to study for my Computer Science course and I need some help to understand this question.

The COSO framework of internal controls is practiced within companies around the world. The objectives of the COSO framework are closely related to its five components. For this week’s activity, please discuss these five components of the COSO framework. Be sure to include each components’ impact on each of the COSO framework objectives. What do you feel an auditor would most be concerned with during an IT audit? Lastly, discuss suggestions for integrating COSO framework compliance into a company in which you are familiar.

Your paper should meet the following requirements:

• Be approximately 3-5 pages in length, not including the required cover page and reference page, aprox 1500 words

• Follow APA6 guidelines. Your paper should include an introduction, a body with fully developed content, and a conclusion.

• Support your answers with the readings from the course and at least two scholarly journal articles to support your positions, claims, and observations, in addition to your textbook. The UC Library is a great place to find resources.

• Be clearly and well-written, concise, and logical, using excellent grammar and style techniques. You are being graded in part on the quality of your writing.

Chapter 12, “Business Intelligence, Knowledge Management, and Analytics”

Dong-Hui Jin, & Hyun-Jung Kim. (2018). Integrated Understanding of Big Data, Big Data Analysis, and Business Intelligence: A Case Study of Logistics. Sustainability, (10), 3778. Retrieved from https://doi.org/10.3390/su10103778

Unformatted Attachment Preview

Managing and Using Information Systems: A Strategic Approach – Sixth Edition Keri Pearlson, Carol Saunders, and Dennis Galletta © Copyright 2016 John Wiley & Sons, Inc. Chapter 12 Knowledge Management, Business Intelligence, and Analytics Opening Case: Netflix • What gave Netflix assurance that House of Cards would be a success? • What gives Netflix a competitive advantage? © 2016 John Wiley & Sons, Inc. 3 More Real World Examples • Caesar’s and Capital One both collect and analyze customer data. • Result: They can determine who are the most profitable customers and then follow up with them. • Caesar’s: frequent gamblers • Capital One: charge a lot and pay off slowly • They provide products that would appeal to the profitable customers. © 2016 John Wiley & Sons, Inc. 4 A Real World Example from Sports • Oakland As and Boston Red Sox baseball teams • Crunched the numbers on the potential players, such as on-base percentage • Others who did not do the analysis failed to recognize the talent © 2016 John Wiley & Sons, Inc. 5 Five Ways Data Analytics can Help an Organization (McKinsey and Co.) • Making data more transparent and usable more quickly • Exposing variability and boosting performance • Tailoring products and services • Improving decision-making • Improving products © 2016 John Wiley & Sons, Inc. 6 Terminology • Knowledge management: The processes needed to generate, capture, codify and transfer knowledge across the organization to achieve competitive advantage • Business intelligence: The set of technologies and processes that use data to understand and analyze business performance • Business analytics: The use of quantitative and predictive models, algorithms, and evidence-based management to drive decisions © 2016 John Wiley & Sons, Inc. 7 Data, Information, and Knowledge (reprise) © 2016 John Wiley & Sons, Inc. 8 The Value of Managing Knowledge Value Sources of Value Sharing best practices • • Avoid reinventing the wheel Build on valuable work and expertise Sustainable competitive advantage • • Shorten innovation life cycle Promote long term results and returns Managing overload • • Filter data to find relevant knowledge Organize and store for easy retrieval Rapid change • • • Build on/customize previous work for agility Streamline and build dynamic processes Quick response to changes Embedded knowledge from products • • • Smart products can gather information Blur distinction between manufacturing/service Add value to products Globalization • • • Decrease cycle times by sharing knowledge globally Manage global competitive pressures Adapt to local conditions Insurance for downsizing • • • Protect against loss of knowledge when departures occur Provide portability for workers who change roles Reduce time to acquire knowledge © 2016 John Wiley & Sons, Inc. 9 Dimensions of Knowledge Explicit ▪ Teachable ▪ Articulable ▪ Observable in use ▪ Scripted ▪ Simple ▪ Documented Tacit ▪ Not teachable ▪ Not articulable ▪ Not observable ▪ Rich ▪ Complex ▪ Undocumented Examples: • Explicit steps • Procedure manuals Examples: • Estimating work • Deciding best action © 2016 John Wiley & Sons, Inc. 10 Four Modes of Knowledge Conversion (and examples) Transferring by mentoring, apprenticeship Learning by doing; studying manuals © 2016 John Wiley & Sons, Inc. Transferring by models, metaphors Obtaining and following manuals 11 Knowledge Management – Four Processes • Generate – discover “new” knowledge • Capture – scan, organize, and package it • Codify – represent it for easy access and transfer (even as simple as using hash tags to create a folksonomy) • Transfer – transmit it from one person to another to absorb it © 2016 John Wiley & Sons, Inc. 12 Measures of KM Project Success • Example of specific benefits of a KM project: • • • • • • • Enhanced effectiveness Revenue generated from extant knowledge assets Increased value of extant products and services Increased organizational adaptability More efficient re-use of knowledge assets Reduced costs Reduced cycle time © 2016 John Wiley & Sons, Inc. 13 Components of Business Analytics Component Definition Example Data Sources Data streams and repositories Applications and processes for statistical analysis, forecasting, predictive modeling, and optimization Organizational environment that creates and sustains the use of analytics tools Data warehouses; weather data Data mining process; forecasting software package Software Tools Data-Driven Environment Skilled Workforce Workforce that has the training, experience, and capability to use the analytics tools © 2016 John Wiley & Sons, Inc. Reward system that encourages the use of the analytics tools; willingness to test or experiment Data scientists, chief data officers, chief analytics officers, analysts, etc. Netflix, Caesars and Capital One have these skills 14 Data Sources for Analytics • Structured (customers, weather patterns) or unstructured (Tweets, YouTube videos) • Internal or external • Data warehouses full of a variety of information • Real-time information such as stock market prices © 2016 John Wiley & Sons, Inc. 15 Data Mining • Combing through massive amounts of customer data, usually focused on: • Buying patterns/habits (for cross-selling) • Preferences (to help identify new products/ features/enhancements to products) • Unusual purchases (spotting theft) • It also identifies previously unknown relationships among data. • Complex statistics can uncover clusters on many dimensions not known previously • (e.g., People who like movie x also like movie y) © 2016 John Wiley & Sons, Inc. 16 Four Categories of Data Mining Tools • Statistical analysis: Answers questions such as “Why is this happening?” • Forecasting/Extrapolation: Answers questions such as “What if these trends continue?” • Predictive modeling: Answers questions such as “What will happen next?” • Optimization: Answers questions such as “What is the best that can happen?” © 2016 John Wiley & Sons, Inc. 17 How to be Successful • Achieve a data driven culture • Develop skills for data mining • Use a Chief Analytics Officer (CAO) or Chief Data Officer (CDO) • Shoot for high maturity level (see next slide) © 2016 John Wiley & Sons, Inc. 18 Five Maturity Levels of Analytical Capabilities Level Description Source of Business Value 1 – Reporting What happened? Reduce costs of summarizing, printing 2 – Analyzing Why did it happen? Understanding root causes 3 – Describing What is happening now Real-time understanding & corrective action 4 – Predicting What will happen? Can take best action 5 – Prescribing How should we respond? Dynamic correction © 2016 John Wiley & Sons, Inc. 19 BI and Competitive Advantage • There is a very large amount of data in databases. • Big data: techniques and technologies that make it economical to deal with very large datasets at the extreme end of the scale: e.g., 1021 data items • Large datasets can uncover potential trends and causal issues • Specialized computers and tools are needed to mine the data. • Big data emerged because of the rich, unstructured data streams that are created by social IT. © 2016 John Wiley & Sons, Inc. 20 Practical Example • Asthma outbreaks can be predicted by U. of Arizona researchers with 70% accuracy • They examine tweets and Google searches for words and phrases like • “wheezing” “sneezing” “inhaler” “can’t breathe” • Relatively rare words (1% of tweets) but 15,000/day • They examine the context of the words: • “It was so romantic I couldn’t catch my breath” vs • “After a run I couldn’t catch my breath” • Helps hospitals make work scheduling decisions © 2016 John Wiley & Sons, Inc. 21 Sentiment Analysis • Can analyze tweets and Facebook likes for • Real-time customer reactions to products • Spotting trends in reactions • Useful for politicians, advertisers, software versions, sales opportunities © 2016 John Wiley & Sons, Inc. 22 Google Analytics and Salesforce.com • Listening to the community: Identifying and monitoring all conversations in the social Web on a particular topic or brand. • Learning who is in the community: Identifying demographics such as age, gender, location, and other trends to foster closer relationships. • Engaging people in the community: Communicating directly with customers on social platforms such as Facebook, YouTube, LinkedIn, and Twitter using a single app. • Tracking what is being said: Measuring and tracking demographics, conversations, sentiment, status, and customer voice using a dashboard and other reporting tools. • Building an audience: Using algorithms to analyze data from internal and external sources to understand customer attributes, behaviors, and profiles, then to find new similar customers © 2016 John Wiley & Sons, Inc. 23 Google Analytics • Web site testing and optimizing: Understanding traffic to Web sites and optimizing a site’s content and design for increasing traffic. • Search optimization: Understanding how Google sees an organization’s Web site, how other sites link to it, and how specific search queries drive traffic to it. • Search term interest and insights: Understanding interests in particular search terms globally, as well as regionally, top searches for similar terms, and popularity over time. • Advertising support and management: Identifying the best ways to spend advertising resources for online media. © 2016 John Wiley & Sons, Inc. 24 Internet of Things (IoT) • Much big data comes from IoT • Sensor data in products can allow the products to: • • • • • Call for service (elevators, heart monitors) Parallel park, identify location/speed (cars) Alert you to the age of food (refrigerator) Waters the lawn when soil is dry (sprinklers) Self-driving cars find best route (Google) © 2016 John Wiley & Sons, Inc. 25 Intellectual Capital vs Intellectual Property • Intellectual Capital: the process for managing knowledge • Intellectual Property: the outputs; the desired product for the process • Intellectual Property rights differ remarkably by country © 2016 John Wiley & Sons, Inc. 26 Closing Caveats • These are emerging concepts and disciplines • Sometimes knowledge should remain hidden (tacit) for protection • We should remain focused on future events, not just look over the past • A supportive culture is needed in a firm to enable effective KM and BI © 2016 John Wiley & Sons, Inc. 27 Managing and Using Information Systems: A Strategic Approach – Sixth Edition Keri Pearlson, Carol Saunders, and Dennis Galletta © Copyright 2016 John Wiley & Sons, Inc. sustainability Case Report Integrated Understanding of Big Data, Big Data Analysis, and Business Intelligence: A Case Study of Logistics Dong-Hui Jin and Hyun-Jung Kim * Seoul Business School, aSSIST, 46 Ewhayeodae 2-gil, Seodaemun-gu, Seoul 03767, Korea; yutajin002@gmail.com * Correspondence: hjkim@assist.ac.kr; Tel.: +82-70-7012-2722 Received: 5 October 2018; Accepted: 17 October 2018; Published: 19 October 2018   Abstract: Efficient decision making based on business intelligence (BI) is essential to ensure competitiveness for sustainable growth. The rapid development of information and communication technology has made collection and analysis of big data essential, resulting in a considerable increase in academic studies on big data and big data analysis (BDA). However, many of these studies are not linked to BI, as companies do not understand and utilize the concepts in an integrated way. Therefore, the purpose of this study is twofold. First, we review the literature on BI, big data, and BDA to show that they are not separate methods but an integrated decision support system. Second, we explore how businesses use big data and BDA practically in conjunction with BI through a case study of sorting and logistics processing of a typical courier enterprise. We focus on the company’s cost efficiency as regards to data collection, data analysis/simulation, and the results from actual application. Our findings may enable companies to achieve management efficiency by utilizing big data through efficient BI without investing in additional infrastructure. It could also give them indirect experience, thereby reducing trial and error in order to maintain or increase competitiveness. Keywords: business application; big data; big data analysis; business intelligence; logistics; courier service 1. Introduction A growing number of corporations depend on various and continuously evolving methods of extracting valuable information through big data and big data analysis (BDA) for business intelligence (BI) to make better decisions. The term “big data” refers to large amounts of information or data at a certain point in time and within a particular scope. However, big data have a short lifecycle with rapidly decreasing effective value, which makes it difficult for academic research to keep up with their fast pace. In addition, big data have no limits regarding their type, form, or scale, and their scope is too vast to narrow them down to a specific area of study. Big data can also simply refer to a huge amount of complex data, but their type, characteristics, scale, quality, and depth vary depending on the capabilities and purpose of each company. The same holds for the reliability and usability of the results gathered from analysis of the data. Previous studies generally agree on three main properties that define big data, namely, volume, velocity, and variety, or the “3Vs” [1–4], which have recently been expanded to “5Vs” with the addition of veracity/verification and value [5–10]. There are numerous multi-dimensional methods for choosing how much data to gather and how to analyze and utilize the data. In brief, the methodology for extracting valuable information and taking full advantage of it could be more important than the data’s quality and quantity. A substantial amount of research has been devoted to establishing and developing theories concerning big data, Sustainability 2018, 10, 3778; doi:10.3390/su10103778 www.mdpi.com/journal/sustainability Sustainability 2018, 10, 3778 2 of 15 BDA, and BI to address this need, but it is still challenging for a company to find, understand, integrate, and use the findings of these studies, which are often conducted independently and cover only select aspects of the subject. BDA refers to the overall process of applying advanced analytic skills, such as data mining, statistical analysis, and predictive analysis, to identify patterns, correlations, trends, and other useful techniques [11–15]. BDA contributes to increasing the operational efficiency and business profits, and is becoming essential to businesses as big data spreads and grows rapidly. BI is a decision support system that includes the overall process of gathering extensive data, extracting useful data, and providing analytical applications. In general, BI has three common technological elements: a data warehouse integrating an online transaction processing system; a database addressing specific topics; online analytical processing that is used to analyze data in multi-dimensions in order to use those data; and data mining, which involves a series of technological methods for extracting useful knowledge from the gathered data [16–20]. Some areas of BI and BDA, such as data analysis and data mining, overlap. This is to be expected, as the raw data in BI have recently expanded to become big data in volume and scope. This has necessitated reorganization of the field and concepts of BI to provide business insights and enable better decision making based on BDA [21]. Although BI and BDA are generally studied independently, it is challenging and often unnecessary to distinguish between the two concepts when performing business tasks. Given the cost of gathering and analyzing big data, it is important to identify what data to collect, the range of the data, and the most cost-effective purpose of the data using BI. For this purpose, it is effective to understand and apply the methodology based on experiences of companies shared through a case study. Therefore, the present study has the following aims. First, we explore the meaning of BI, big data, and BDA through a literature review and show that they are not separate methods, but rather an organically connected and integrated decision support system. Second, we use a case study to examine how big data and BDA are applied in practice through BI for greater understanding of the topic. The case study is conducted on a large and rapidly growing courier service in the logistics industry, which has a long history of research. In particular, we examine how the company efficiently allocates vehicles in hub terminals by collecting, analyzing, and applying big data to make informed decisions quickly, as well as uses BI to enhance productivity and cost-effectiveness. The rest of the paper proceeds as follows. Section 2 reviews the research background and literature related to BI, big data, and BDA. Section 3 presents the case study for the company and industry and discusses the case in detail. Finally, Section 4 concludes by discussing the implications and directions for future research. 2. Literature Review Big data have become a subject of growing importance, especially since Manyika et al. pointed out that they should be regarded as a key factor to increase corporate productivity and competitiveness [22]. Many researchers have shown interest in big data, as the rapid development of information and communication technology (ICT) generates a significant amount of data. This has led to lively discussions about the collection, storage, and application of such data. In 2012, Kang et al. argued that the value of big data lies in making forecasts by recognizing situations, creating new value, simulating different scenarios, and analyzing patterns through analysis of the data on a massive scale [23]. In 2011, only 38 studies related to big data and BDA were listed in the Science Citation Index Expanded (SCIE), Social Science Citation Index (SSCI), Arts & Humanities Citation Index (AHCI), and Emerging Sources Citation Index (ESCI), but in 2012, this number increased to 92, and then rapidly increased to 1009 in 2015 and 3890 in 2017 [24]. Sustainability 2018, 10, 3778 3 of 15 2.1. Toward an Integrated Understanding of Big Data, BDA, and BI The research boom regarding big data has led to the development of BDA, through which valuable information is extracted from a company’s data. Companies are well aware of the increasing importance and investment need for BDA, as shown by Tankard [25], who claimed that a company can secure higher market share than its rivals and has the potential to increase its operating profit margin ratio by up to 60% by using big data effectively [25,26]. In the logistics industry, big data are used more widely than ever for supporting and optimizing operational processes, including supply chain management. Big data have been instrumental in developing new products and services, planning supply, managing inventory and risks, and providing customized services [26–29]. BI has a longer history of research than that of big data. In 1865, Richard Millar Devens mentioned the concept in the Cyclopaedia of Commercial and Business Anecdotes [30], after which Luhn began using it in its modern meaning in 1958 [31]. Thereafter, Vitt et al. defined BI as an information system and method for decision making that in ...
Purchase answer to see full attachment
Student has agreed that all tutoring, explanations, and answers provided by the tutor will be used to help in the learning process and in accordance with Studypool's honor code & terms of service.

Final Answer

Attached.

Outline for COSO framework
Introduction
In this section, an introduction has been made regarding the COSO framework.
Components of COSO Framework
The components of COSO have been discussed as required.
What an auditor would consist most during an IT audit
In this section, communication and information have been discussed.
Suggestions of integrating COSO framework compliance Walmart Company
In this section, the suggestions of COSO integration in Walmart have been discussed such as risk
management and control.
Conclusion
In this section, a conclusion has been made as required.
References
The sources are enlisted in this section.


Running head: COSO FRAMEWORK

1

COSO Framework
Details
Name
Institutional Affiliation
Date

COSO FRAMEWORK

2
Introduction

The Committee of Sponsoring Organizations of the Tread way Commission (COSO) launched a
model that was useful in evaluation of organizational internal controls (Trautman & Kimbell,
2018). The model is good and has major components that help a great deal in the reduction of
challenges and promotion of a good perspectives recommended (Hamdan 2019). The following
is a discussion regarding the COSO framework and its use in the auditing of internal
organizational functions:
Components of COSO Framework
The COSO framework has five major components and they include the following:
a. Control environment
This is a component of COSO that deals with the setting of administrative rules and control of
the organizational environment (Trautman & Kimbell, 2018). The organization must operate
based on instructions and other frameworks that are crucial for the success of the programs as
required. Strategically, control environment issues direction of the organization and ensures that
instructions are followed to the later for effective organizational functioning. Control
environment gives administrative direction that assists in the internal control and effective
steering as recommended (Hamdan 2019). Based on the COSO objectives, the component assists
in the control of confusion and unclear instructions that bar quality of processes hence making
the processes good and comprehensive as required.
b. Risk assessment
This is a component of the framework that deals with identification of risks and working on
plans to mitigate those (Trautman & Kimbell, 2018). Strategically, there is a need to identify the
vulnerabilities and work on avenues of solving them to reduce the challenges and ensure the

COSO FRAMEWORK

3

issues that cause vulnerabilities are controlled. In every entity, the COSO framework component
deals with scrutiny of the organizations to generate genuine approaches and regulate adversities
for stability and comprehensive organizational development (Hamdan 2019). The objectives of
COSO are based on organizational stability and the objective helps in accomplishment whereby
any form of risk is identified and controlled before it is worse and assists in the elimination of
challenges to reduce the adversity in a good and quality way as well.
c. Control activities
Control activities are concerned with formulation of rules and guidelines that issue directions as
required (Trautman & Kimbell, 2018). Strategically, there is a need to formulate directives and
control measures that ensures that the control approaches are implemented as required. Control
activities issues command to the organization and ensures that the members follow the guidelines
for a direction. Based on the leadership and flow of command, control activities define code of
behavior and expected approaches to reduce the rates and level of confusion (Hamdan 2019).
Control is good for promoting a good outcome to ensure that basic principles and strategies are
implemented in the control of challenges as recommended. As per the COSO objectives, an
organization must have internal control whereby the directions are issued and better objectives
accomplished. The component achieves the objective by providing necessary services to assist in
the reduction of challenges and identification of quality mechanisms as required.
d. Information and communication
In every organization, there must be information as an internal control. The controls offer
guidelines and necessary strategies for reduction of challenges for effective elimination and
pr...

JohnNyamboche (8665)
UC Berkeley

Anonymous
Top quality work from this tutor! I’ll be back!

Anonymous
It’s my second time using SP and the work has been great back to back :) The one and only resource on the Interwebs for the work that needs to be done!

Anonymous
Thanks, good work

Studypool
4.7
Trustpilot
4.5
Sitejabber
4.4