ITS831 Campbellsville University Components of COSO Framework Research Paper Week 12 Readings Attached Files: Integrated Understanding of Big Data, Big

ITS831 Campbellsville University Components of COSO Framework Research Paper Week 12 Readings

Attached Files:

Integrated Understanding of Big Data, Big Data Analysis, and Business Intelligence.pdf (2.826 MB)

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

Week 12 Research Paper: COSO Framework

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 2-4 pages in length, not including the required cover page and reference page.

• 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. 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
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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
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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…
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