Running title: TRENDS IN COMPUTER INFORMATION SYSTEMS 1
TRENDS IN COMPUTER INFORMATION SYSTEMS 4
Trends in Computer Information Systems, and the Rise to Business Intelligence
Shad Martin
School for Professional Studies
St. Louis University
ENG 2005 Dr. Rebecca Wood
November 23, 2016
Introduction
Our quest to increase our knowledge of Computer Information Systems has produced a number of benefits to humanity. The innovation humans have discovered in Computer Information Systems has led to new sub-areas of study for students and professionals to continue their progression to master all that Computer Information Systems has to offer. Amy Web of the Harvard Business Review reported 8 Tech Trends to Watch in 2016, She noted, “In order to chart the best way forward, you must understand emerging trends: what they are, what they aren’t, and how they operate. Such trends are more than shiny objects; they’re manifestations of sustained changes within an industry sector, society, or human behavior. Trends are a way of seeing and interpreting our current reality, providing a useful framework to organize our thinking, especially when we’re hunting for the unknown. Fads pass. Trends help us forecast the future” (Harvard Business Review, 2015). In short, Amy’s reference to understanding the emerging trends in Computer Information can provide a framework from which, students, professionals, and scientists to conscientiously create a path towards optimizing their efforts. Ensuring we have a fundamental approach to analyze data will enhance our understanding of this subject further.
In this paper I will expound on three of the top trends used to provide insight into the data produced from the advancements in Computer Information Systems. These trends or methods are taking place in my workplace within a financial institution, and in many other industries. It is important to note this paper does not provide an inclusive list of all methodologies that exist. Individuals can now leverage analytics to synthesize insights from data to identify emerging risk, manage operational risks, identify trends, improve compliance, and customer satisfaction. Data in and by itself is not always useful. Regardless of the data source, trained professional must understand the best approach to structure the data to make it more useful. In this paper, I will touch on three popular methodology trends occurring in Computer Information Systems. Students and professionals who work with large data would benefit from having a solid understanding of the fundamental principles of Business Intelligence as data scientific approach and when to use these methodologies.
The rise of Business Intelligence
Computer Information Systems allow many companies to gather and generate large amounts of data on their customers, business activities, potential merger targets, and risks found in their organization. These large sets of data have given rise to vari.
FOUR TYPES OF BUSINESS ANALYTICS TO KNOWBUSINESS ANALYTICSby AJeanmarieColbert3
FOUR TYPES OF BUSINESS ANALYTICS TO KNOW
BUSINESS ANALYTICS
by Anushka Mehta October 13, 2017
For different stages of business analytics huge amount of data is processed at various steps. Depending on the stage of the workflow and the requirement of data analysis, there are four main kinds of analytics – descriptive, diagnostic, predictive and prescriptive. These four types together answer everything a company needs to know- from what’s going on in the company to what solutions to be adopted for optimizing the functions.
The four types of analytics are usually implemented in stages and no one type of analytics is said to be better than the other. They are interrelated and each of these offers a different insight. With data being important to so many diverse sectors- from manufacturing to energy grids, most of the companies rely on one or all of these types of analytics. With the right choice of analytical techniques, big data can deliver richer insights for the companies
Before diving deeper into each of these, let’s define the four types of analytics:
1) Descriptive Analytics:Describing or summarizing the existing data using existing business intelligence tools to better understand what is going on or what has happened.
2) Diagnostic Analytics: Focus on past performance to determine what happened and why. The result of the analysis is often an analytic dashboard.
3) Predictive Analytics:Emphasizes on predicting the possible outcome using statistical models and machine learning techniques.
4) Prescriptive Analytics:It is a type of predictive analytics that is used to recommend one or more course of action on analyzing the data.
Let’s understand these in a bit more depth.
1. Descriptive Analytics
This can be termed as the simplest form of analytics. The mighty size of big data is beyond human comprehension and the first stage hence involves crunching the data into understandable chunks. The purpose of this analytics type is just to summarize the findings and understand what is going on.
Among some frequently used terms, what people call as advanced analytics or business intelligence is basically usage of descriptive statistics (arithmetic operations, mean, median, max, percentage, etc.) on existing data. It is said that 80% of business analytics mainly involves descriptions based on aggregations of past performance. It is an important step to make raw data understandable to investors, shareholders and managers. This way it gets easy to identify and address the areas of strengths and weaknesses such that it can help in strategizing.
The two main techniques involved are data aggregation and data mining stating that this method is purely used for understanding the underlying behavior and not to make any estimations. By mining historical data, companies can analyze the consumer behaviors and engagements with their businesses that could be helpful in targeted marketing, service improvement, etc. The tools used in this phase are MS Excel, MATLAB ...
This document provides an overview of data analytics including:
- The basics of data analytics including analytics definitions and the need for data analytics due to increasing data volumes.
- Descriptions of different types of analytics including descriptive, diagnostic, predictive, and prescriptive analytics and their purposes.
- An overview of the data analytics lifecycle including phases such as data preparation, model planning, model building, and communication of results.
Data science and data analytics professionals enable organizations to utilize the potential of predictive analytics to make informed decisions & help in transforming analytics maturity model of the organization.
This document provides an introduction to data literacy for beginners. It defines key terms like data science, data analytics, and data literacy. It explains that data science involves building and structuring datasets, while data analytics refers to analyzing data to gain insights. The document then covers foundational concepts like the data ecosystem and lifecycle, data privacy and ethics, and data integrity. Finally, it discusses seven skills needed for data and analytics success, such as critical thinking, data visualization, and machine learning, and how readers can improve their skills. The overall document aims to give beginners a foundational understanding of data concepts to build their data literacy.
what is ..how to process types and methods involved in data analysisData analysis ireland
Data analysis is the process of cleaning, transforming, and processing raw data in order to extract useful and actionable information that can assist businesses in making better decisions.
Sit717 enterprise business intelligence 2019 t2 copy1NellutlaKishore
This document discusses data mining techniques and business intelligence. It begins with an introduction to different data mining techniques like clustering, statistical analysis, visualization, classification, neural networks, rules, and decision trees. It then provides more detail on statistical techniques, explaining that they help analyze large datasets. The document evaluates how big data and business intelligence are related, concluding that while they are different concepts, they need to work together to effectively analyze data and make smart business decisions. Big data provides the large datasets, while business intelligence extracts useful information from those datasets.
The need, applications, challenges, new trends and
a consulting perspective
(Why is Big Data a strategic need for optimization of organizational processes especially in the business domains and what is the consultant’s role?)
With every transaction and activity, organizations churn out data. This process happens even in the case of idle operation. Hence, data needs to be effectively analyzed to manage all processes better. Data can be used to make sense of the current situation and predict outcomes. It also can be used to optimize business processes and operations. This is easier said than done as data is being produced at an unprecedented rate, huge volumes and a high degree of variety. For the outcome of the data analysis to be relevant, all the data sets must be factored in to the analysis and predictions. This is where big data analysis comes in with its sophisticated tools that are also now easy on the pocket if one prefers the open source.
The future of high potential marketing lead generation would be based on big data. Virtually every business vertical can benefit from big data initiatives. Even those without deep pockets can use the cloud model for business analytics/big data analysis.
Some challenges remain to be addressed to engender large scale adoption but the current benefits outweigh the concerns.
India has seen a massive growth in big data adoption and the trend will grow though it is generally amongst the bigger players. As quality of data improves and customer reluctance to being honest when they volunteer data reduces, the forecasts will become more accurate and Big Data will have come to its rightful place as a key enabler.
FOUR TYPES OF BUSINESS ANALYTICS TO KNOWBUSINESS ANALYTICSby AJeanmarieColbert3
FOUR TYPES OF BUSINESS ANALYTICS TO KNOW
BUSINESS ANALYTICS
by Anushka Mehta October 13, 2017
For different stages of business analytics huge amount of data is processed at various steps. Depending on the stage of the workflow and the requirement of data analysis, there are four main kinds of analytics – descriptive, diagnostic, predictive and prescriptive. These four types together answer everything a company needs to know- from what’s going on in the company to what solutions to be adopted for optimizing the functions.
The four types of analytics are usually implemented in stages and no one type of analytics is said to be better than the other. They are interrelated and each of these offers a different insight. With data being important to so many diverse sectors- from manufacturing to energy grids, most of the companies rely on one or all of these types of analytics. With the right choice of analytical techniques, big data can deliver richer insights for the companies
Before diving deeper into each of these, let’s define the four types of analytics:
1) Descriptive Analytics:Describing or summarizing the existing data using existing business intelligence tools to better understand what is going on or what has happened.
2) Diagnostic Analytics: Focus on past performance to determine what happened and why. The result of the analysis is often an analytic dashboard.
3) Predictive Analytics:Emphasizes on predicting the possible outcome using statistical models and machine learning techniques.
4) Prescriptive Analytics:It is a type of predictive analytics that is used to recommend one or more course of action on analyzing the data.
Let’s understand these in a bit more depth.
1. Descriptive Analytics
This can be termed as the simplest form of analytics. The mighty size of big data is beyond human comprehension and the first stage hence involves crunching the data into understandable chunks. The purpose of this analytics type is just to summarize the findings and understand what is going on.
Among some frequently used terms, what people call as advanced analytics or business intelligence is basically usage of descriptive statistics (arithmetic operations, mean, median, max, percentage, etc.) on existing data. It is said that 80% of business analytics mainly involves descriptions based on aggregations of past performance. It is an important step to make raw data understandable to investors, shareholders and managers. This way it gets easy to identify and address the areas of strengths and weaknesses such that it can help in strategizing.
The two main techniques involved are data aggregation and data mining stating that this method is purely used for understanding the underlying behavior and not to make any estimations. By mining historical data, companies can analyze the consumer behaviors and engagements with their businesses that could be helpful in targeted marketing, service improvement, etc. The tools used in this phase are MS Excel, MATLAB ...
This document provides an overview of data analytics including:
- The basics of data analytics including analytics definitions and the need for data analytics due to increasing data volumes.
- Descriptions of different types of analytics including descriptive, diagnostic, predictive, and prescriptive analytics and their purposes.
- An overview of the data analytics lifecycle including phases such as data preparation, model planning, model building, and communication of results.
Data science and data analytics professionals enable organizations to utilize the potential of predictive analytics to make informed decisions & help in transforming analytics maturity model of the organization.
This document provides an introduction to data literacy for beginners. It defines key terms like data science, data analytics, and data literacy. It explains that data science involves building and structuring datasets, while data analytics refers to analyzing data to gain insights. The document then covers foundational concepts like the data ecosystem and lifecycle, data privacy and ethics, and data integrity. Finally, it discusses seven skills needed for data and analytics success, such as critical thinking, data visualization, and machine learning, and how readers can improve their skills. The overall document aims to give beginners a foundational understanding of data concepts to build their data literacy.
what is ..how to process types and methods involved in data analysisData analysis ireland
Data analysis is the process of cleaning, transforming, and processing raw data in order to extract useful and actionable information that can assist businesses in making better decisions.
Sit717 enterprise business intelligence 2019 t2 copy1NellutlaKishore
This document discusses data mining techniques and business intelligence. It begins with an introduction to different data mining techniques like clustering, statistical analysis, visualization, classification, neural networks, rules, and decision trees. It then provides more detail on statistical techniques, explaining that they help analyze large datasets. The document evaluates how big data and business intelligence are related, concluding that while they are different concepts, they need to work together to effectively analyze data and make smart business decisions. Big data provides the large datasets, while business intelligence extracts useful information from those datasets.
The need, applications, challenges, new trends and
a consulting perspective
(Why is Big Data a strategic need for optimization of organizational processes especially in the business domains and what is the consultant’s role?)
With every transaction and activity, organizations churn out data. This process happens even in the case of idle operation. Hence, data needs to be effectively analyzed to manage all processes better. Data can be used to make sense of the current situation and predict outcomes. It also can be used to optimize business processes and operations. This is easier said than done as data is being produced at an unprecedented rate, huge volumes and a high degree of variety. For the outcome of the data analysis to be relevant, all the data sets must be factored in to the analysis and predictions. This is where big data analysis comes in with its sophisticated tools that are also now easy on the pocket if one prefers the open source.
The future of high potential marketing lead generation would be based on big data. Virtually every business vertical can benefit from big data initiatives. Even those without deep pockets can use the cloud model for business analytics/big data analysis.
Some challenges remain to be addressed to engender large scale adoption but the current benefits outweigh the concerns.
India has seen a massive growth in big data adoption and the trend will grow though it is generally amongst the bigger players. As quality of data improves and customer reluctance to being honest when they volunteer data reduces, the forecasts will become more accurate and Big Data will have come to its rightful place as a key enabler.
Unveiling the Power of Data Analytics Transforming Insights into Action.pdfKajal Digital
Data analytics is the process of examining raw data to discover patterns, correlations, trends, and other valuable information. Its significance lies in its ability to transform data into actionable insights, ultimately leading to informed decision-making and improved business outcomes. From optimizing operational processes to enhancing customer experiences, data analytics offers a plethora of benefits across various sectors.
Analytics can help organizations better understand and manage their workforce. It is an ongoing process that provides insights into how various factors interact and affect outcomes like employee performance, satisfaction, and retention. While organizations can start with simple ad hoc reports, more sophisticated uses of analytics involve testing hypotheses, predictive modeling, and understanding how different parts of the organization influence overall performance. Both deductive and inductive techniques are used, with deductive starting with a hypothesis and inductive deriving theories from large data sets. Analytics provides a more comprehensive view than typical HR metrics by showing dynamic relationships between variables over time.
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Why Data Science is Getting Popular in 2023?kavyagaur3
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The document discusses how companies that are leading in analytics use data and analytics to gain competitive advantages and innovate. It profiles "Analytical Innovators" - companies that rely on analytics to compete and innovate. These companies share a belief that data is a core asset, make effective use of more data for faster results, and have senior management support for data-driven decision making. The document provides examples of companies in different industries that are successfully using analytics and a framework for other companies to also become more analytical.
The document discusses six analytics trends that are likely to influence business in coming years:
1. Analytics is expanding across enterprises as organizations move towards becoming insight-driven.
2. Cognitive technologies and machines are evolving to work alongside humans in complementing roles.
3. Cybersecurity is becoming more predictive and proactive to anticipate threats.
4. The Internet of Things is enabling new innovations through aggregating and analyzing sensor data.
5. Companies are taking creative steps to address the shortage of analytics talent.
6. Analytics success requires a mix of both new and familiar topics as analytics becomes embedded in decision making.
Machine Learning for Business - Eight Best Practices for Getting StartedBhupesh Chaurasia
This document provides an overview of best practices for organizations getting started with machine learning. It discusses 8 best practices: 1) Learn the predictive thought process, 2) Focus on specific use cases, 3) Look for the right predictive tooling, 4) Get training on machine learning techniques, 5) Remember that good quality data is important, 6) Establish model governance processes, 7) Put machine learning models into action, and 8) Manage, monitor and optimize models continuously. The document provides details and examples for each best practice to help organizations successfully implement machine learning.
Big Data Analytics: Challenges And Applications For Text, Audio, Video, And S...IJSCAI Journal
All types of machine automated systems are generating large amount of data in different forms like
statistical, text, audio, video, sensor, and bio-metric data that emerges the term Big Data. In this paper we
are discussing issues, challenges, and application of these types of Big Data with the consideration of big
data dimensions. Here we are discussing social media data analytics, content based analytics, text data
analytics, audio, and video data analytics their issues and expected application areas. It will motivate
researchers to address these issues of storage, management, and retrieval of data known as Big Data. As
well as the usages of Big Data analytics in India is also highlighted
BIG DATA ANALYTICS: CHALLENGES AND APPLICATIONS FOR TEXT, AUDIO, VIDEO, AND S...gerogepatton
All types of machine automated systems are generating large amount of data in different forms likestatistical, text, audio, video, sensor, and bio-metric data that emerges the term Big Data. In this paper weare discussing issues, challenges, and application of these types of Big Data with the consideration of bigdata dimensions. Here we are discussing social media data analytics, content based analytics, text dataanalytics, audio, and video data analytics their issues and expected application areas. It will motivateresearchers to address these issues of storage, management, and retrieval of data known as Big Data. Aswell as the usages of Big Data analytics in India is also highlighted.
BIG DATA ANALYTICS: CHALLENGES AND APPLICATIONS FOR TEXT, AUDIO, VIDEO, AND S...ijscai
All types of machine automated systems are generating large amount of data in different forms like
statistical, text, audio, video, sensor, and bio-metric data that emerges the term Big Data. In this paper we
are discussing issues, challenges, and application of these types of Big Data with the consideration of big
data dimensions. Here we are discussing social media data analytics, content based analytics, text data
analytics, audio, and video data analytics their issues and expected application areas. It will motivate
researchers to address these issues of storage, management, and retrieval of data known as Big Data. As
well as the usages of Big Data analytics in India is also highlighted.
BIG DATA ANALYTICS: CHALLENGES AND APPLICATIONS FOR TEXT, AUDIO, VIDEO, AND S...ijscai
All types of machine automated systems are generating large amount of data in different forms like
statistical, text, audio, video, sensor, and bio-metric data that emerges the term Big Data. In this paper we
are discussing issues, challenges, and application of these types of Big Data with the consideration of big
data dimensions. Here we are discussing social media data analytics, content based analytics, text data
analytics, audio, and video data analytics their issues and expected application areas. It will motivate
researchers to address these issues of storage, management, and retrieval of data known as Big Data. As
well as the usages of Big Data analytics in India is also highlighted.
BIG DATA ANALYTICS: CHALLENGES AND APPLICATIONS FOR TEXT, AUDIO, VIDEO, AND S...gerogepatton
All types of machine automated systems are generating large amount of data in different forms like
statistical, text, audio, video, sensor, and bio-metric data that emerges the term Big Data. In this paper we
are discussing issues, challenges, and application of these types of Big Data with the consideration of big
data dimensions. Here we are discussing social media data analytics, content based analytics, text data
analytics, audio, and video data analytics their issues and expected application areas. It will motivate
researchers to address these issues of storage, management, and retrieval of data known as Big Data. As
well as the usages of Big Data analytics in India is also highlighted.
BIG DATA ANALYTICS: CHALLENGES AND APPLICATIONS FOR TEXT, AUDIO, VIDEO, AND S...gerogepatton
All types of machine automated systems are generating large amount of data in different forms like
statistical, text, audio, video, sensor, and bio-metric data that emerges the term Big Data. In this paper we
are discussing issues, challenges, and application of these types of Big Data with the consideration of big
data dimensions. Here we are discussing social media data analytics, content based analytics, text data
analytics, audio, and video data analytics their issues and expected application areas. It will motivate
researchers to address these issues of storage, management, and retrieval of data known as Big Data. As
well as the usages of Big Data analytics in India is also highlighted.
BIG DATA ANALYTICS: CHALLENGES AND APPLICATIONS FOR TEXT, AUDIO, VIDEO, AND S...gerogepatton
All types of machine automated systems are generating large amount of data in different forms likestatistical, text, audio, video, sensor, and bio-metric data that emerges the term Big Data. In this paper weare discussing issues, challenges, and application of these types of Big Data with the consideration of bigdata dimensions. Here we are discussing social media data analytics, content based analytics, text dataanalytics, audio, and video data analytics their issues and expected application areas. It will motivateresearchers to address these issues of storage, management, and retrieval of data known as Big Data. Aswell as the usages of Big Data analytics in India is also highlighted.
Traditional approaches to handling disruptive change like big data analytics, such as resisting change or protecting existing business models, are ineffective in today's digital economy. By rapidly processing vast amounts of structured and unstructured data using big data tools, businesses can test new strategies faster through analytical sandboxes to better meet customer demands. Superfast in-memory computing is transforming industries by enabling new data-driven business models in areas like transportation. The ability to analyze unprecedented types and volumes of data in real time using tools like Apache Hadoop and Spark makes it possible to build more accurate predictive models and realize future gains.
Cognitive Explorers have adopted cognitive systems to gain competitive advantages in areas like revenue forecasting, supply chain management, and customer service. While only 4% of organizations currently have cognitive systems operational, 74% have the data and analytics capabilities needed to implement cognitive approaches. Cognitive Explorers outperform competitors on metrics like revenue, effectiveness, profitability, and innovation. Building a cognitive mindset through strategy and governance is key to successfully adopting cognitive systems. Cognitive Explorers also invest more in technologies that support data ingestion, integration, and analysis from a variety of sources needed for cognitive applications.
Visual and wizard-driven paradigms for analytics can empower more business users to explore data and develop analytic workflows without extensive coding expertise. The webinar demonstrated how SAS solutions provide intuitive visual discovery of data, visual programming to develop analytic workflows through a drag-and-drop interface, and guided wizards for model development. These capabilities make analytics more accessible, help spread capabilities across organizations, and free quantitative experts to focus on more complex issues.
Data science and data analytics major similarities and distinctions (1)Robert Smith
Those working in the field of technology hear the terms ‘Data Science’ and ‘Data Analytics’ probably all the time. These two words are often used interchangeably. Big data is a major component in the tech world today due to the actionable insights and results it offers for businesses. In order to study the data that your organization is producing, it is important to use the proper tools needed to comprehend big data to uncover the right information. To help you optimize your analytics, it is important for you to examine both the similarities and differences of data science and data analytics.
…if one of the primary purposes of education is to teach young .docxanhlodge
“…if one of the primary purposes of education is to teach young people the skills, knowledge, and critical awareness to become productive members of a diverse and democratic society, a broadly conceptualize multicultural education can have a decisive influence.” Textbook page 338.
What steps do you think schools can or should take to promote our democracy in today’s very diverse country?
Food festivals and celebrating a cultural holiday will not be accepted as an answer. Those are examples of tokenism to make the dominant culture feel like they are doing something. These two activities are fun and interesting, but not how we will strengthen our democracy.
.
✍Report OverviewIn this assignment, you will Document an.docxanhlodge
✍
Report Overview
In this assignment, you will
Document and reflect on your university education and on learning experiences outside of the university;
Articulate how your upper-level coursework is an integrated and individualized curriculum built around your interests; and
Highlight the experiences, skills, and projects that show what you can do.
A successful report submission will be the product of many hours of work over several weeks.
A report earning maximum available points will be a carefully curated and edited explanation of your work that provides tangible evidence of—and insights into—your competencies and capabilities over time. In each section of this report, you are (1) telling a story about your own abilities, and (2) providing specific examples and evidence that illustrate and support your claims.
✍
Required Report Sections
Here the sections are listed as they must appear in your final graded submission. You’ll arrange the sections in this order when
submitting
the final report BUT you won’t follow this order when
writing
drafts of each section.
Note that each section description contains a Pro Tip that tells you how to proceed with the work – what to attempt first, second, and third, etc.
❖ I. Statement of Purpose ❖
Step 1.
Read these four very different
examples of successful Statement of Purpose sections
.
Step 2.
Consider the differences in tone, style, level of detail etc. Your own statement of purpose may resemble one of these. Indeed, writing a first draft based on an example or combination of examples is a good idea. BUT don’t let these examples limit your thinking or personal expression. You may want to begin with a quote from a famous person, use a quote from your mom, or skip the quote. You may want to discuss your personal motivations or get right down to the facts. You may want to list your classes or discuss how your work-life led you to this path.
Step 3.
Write a rough draft – let’s call that Statement of Purpose 1.0. Write Statement of Purpose 1.0 as quickly as you can and then put it away until after you have completed most of the report. Forget about Statement of Purpose 1.0 until most of your report is at least in draft form.
Step 4.
Once you have a draft of all sections of your report, you are in a good position to revise Statement of Purpose 1. You are ready for Step 4. Take Statement of Purpose 1.0 out its dusty vault and hold it up to the sun. Ah. Now read your report draft and compare it to the claims you made in Statement of Purpose 1.0. Ask yourself these questions:
Does Statement of Purpose 1.0. accurately introduce my report?
Are there important ideas or representative experiences in the report that should be highlighted in the Statement of Purpose but aren’t? Remember this isn’t a treasure hunt where its your reader’s job to figure out what matters. It’s your job to show the reader what matters.
If Statement of Purpose 1.0. isn’t the best map it can be for th.
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2. Cognitive technologies and machines are evolving to work alongside humans in complementing roles.
3. Cybersecurity is becoming more predictive and proactive to anticipate threats.
4. The Internet of Things is enabling new innovations through aggregating and analyzing sensor data.
5. Companies are taking creative steps to address the shortage of analytics talent.
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are discussing issues, challenges, and application of these types of Big Data with the consideration of big
data dimensions. Here we are discussing social media data analytics, content based analytics, text data
analytics, audio, and video data analytics their issues and expected application areas. It will motivate
researchers to address these issues of storage, management, and retrieval of data known as Big Data. As
well as the usages of Big Data analytics in India is also highlighted
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All types of machine automated systems are generating large amount of data in different forms likestatistical, text, audio, video, sensor, and bio-metric data that emerges the term Big Data. In this paper weare discussing issues, challenges, and application of these types of Big Data with the consideration of bigdata dimensions. Here we are discussing social media data analytics, content based analytics, text dataanalytics, audio, and video data analytics their issues and expected application areas. It will motivateresearchers to address these issues of storage, management, and retrieval of data known as Big Data. Aswell as the usages of Big Data analytics in India is also highlighted.
BIG DATA ANALYTICS: CHALLENGES AND APPLICATIONS FOR TEXT, AUDIO, VIDEO, AND S...ijscai
All types of machine automated systems are generating large amount of data in different forms like
statistical, text, audio, video, sensor, and bio-metric data that emerges the term Big Data. In this paper we
are discussing issues, challenges, and application of these types of Big Data with the consideration of big
data dimensions. Here we are discussing social media data analytics, content based analytics, text data
analytics, audio, and video data analytics their issues and expected application areas. It will motivate
researchers to address these issues of storage, management, and retrieval of data known as Big Data. As
well as the usages of Big Data analytics in India is also highlighted.
BIG DATA ANALYTICS: CHALLENGES AND APPLICATIONS FOR TEXT, AUDIO, VIDEO, AND S...ijscai
All types of machine automated systems are generating large amount of data in different forms like
statistical, text, audio, video, sensor, and bio-metric data that emerges the term Big Data. In this paper we
are discussing issues, challenges, and application of these types of Big Data with the consideration of big
data dimensions. Here we are discussing social media data analytics, content based analytics, text data
analytics, audio, and video data analytics their issues and expected application areas. It will motivate
researchers to address these issues of storage, management, and retrieval of data known as Big Data. As
well as the usages of Big Data analytics in India is also highlighted.
BIG DATA ANALYTICS: CHALLENGES AND APPLICATIONS FOR TEXT, AUDIO, VIDEO, AND S...gerogepatton
All types of machine automated systems are generating large amount of data in different forms like
statistical, text, audio, video, sensor, and bio-metric data that emerges the term Big Data. In this paper we
are discussing issues, challenges, and application of these types of Big Data with the consideration of big
data dimensions. Here we are discussing social media data analytics, content based analytics, text data
analytics, audio, and video data analytics their issues and expected application areas. It will motivate
researchers to address these issues of storage, management, and retrieval of data known as Big Data. As
well as the usages of Big Data analytics in India is also highlighted.
BIG DATA ANALYTICS: CHALLENGES AND APPLICATIONS FOR TEXT, AUDIO, VIDEO, AND S...gerogepatton
All types of machine automated systems are generating large amount of data in different forms like
statistical, text, audio, video, sensor, and bio-metric data that emerges the term Big Data. In this paper we
are discussing issues, challenges, and application of these types of Big Data with the consideration of big
data dimensions. Here we are discussing social media data analytics, content based analytics, text data
analytics, audio, and video data analytics their issues and expected application areas. It will motivate
researchers to address these issues of storage, management, and retrieval of data known as Big Data. As
well as the usages of Big Data analytics in India is also highlighted.
BIG DATA ANALYTICS: CHALLENGES AND APPLICATIONS FOR TEXT, AUDIO, VIDEO, AND S...gerogepatton
All types of machine automated systems are generating large amount of data in different forms likestatistical, text, audio, video, sensor, and bio-metric data that emerges the term Big Data. In this paper weare discussing issues, challenges, and application of these types of Big Data with the consideration of bigdata dimensions. Here we are discussing social media data analytics, content based analytics, text dataanalytics, audio, and video data analytics their issues and expected application areas. It will motivateresearchers to address these issues of storage, management, and retrieval of data known as Big Data. Aswell as the usages of Big Data analytics in India is also highlighted.
Traditional approaches to handling disruptive change like big data analytics, such as resisting change or protecting existing business models, are ineffective in today's digital economy. By rapidly processing vast amounts of structured and unstructured data using big data tools, businesses can test new strategies faster through analytical sandboxes to better meet customer demands. Superfast in-memory computing is transforming industries by enabling new data-driven business models in areas like transportation. The ability to analyze unprecedented types and volumes of data in real time using tools like Apache Hadoop and Spark makes it possible to build more accurate predictive models and realize future gains.
Cognitive Explorers have adopted cognitive systems to gain competitive advantages in areas like revenue forecasting, supply chain management, and customer service. While only 4% of organizations currently have cognitive systems operational, 74% have the data and analytics capabilities needed to implement cognitive approaches. Cognitive Explorers outperform competitors on metrics like revenue, effectiveness, profitability, and innovation. Building a cognitive mindset through strategy and governance is key to successfully adopting cognitive systems. Cognitive Explorers also invest more in technologies that support data ingestion, integration, and analysis from a variety of sources needed for cognitive applications.
Visual and wizard-driven paradigms for analytics can empower more business users to explore data and develop analytic workflows without extensive coding expertise. The webinar demonstrated how SAS solutions provide intuitive visual discovery of data, visual programming to develop analytic workflows through a drag-and-drop interface, and guided wizards for model development. These capabilities make analytics more accessible, help spread capabilities across organizations, and free quantitative experts to focus on more complex issues.
Data science and data analytics major similarities and distinctions (1)Robert Smith
Those working in the field of technology hear the terms ‘Data Science’ and ‘Data Analytics’ probably all the time. These two words are often used interchangeably. Big data is a major component in the tech world today due to the actionable insights and results it offers for businesses. In order to study the data that your organization is producing, it is important to use the proper tools needed to comprehend big data to uncover the right information. To help you optimize your analytics, it is important for you to examine both the similarities and differences of data science and data analytics.
Similar to Running title TRENDS IN COMPUTER INFORMATION SYSTEMS1TRENDS I.docx (20)
…if one of the primary purposes of education is to teach young .docxanhlodge
“…if one of the primary purposes of education is to teach young people the skills, knowledge, and critical awareness to become productive members of a diverse and democratic society, a broadly conceptualize multicultural education can have a decisive influence.” Textbook page 338.
What steps do you think schools can or should take to promote our democracy in today’s very diverse country?
Food festivals and celebrating a cultural holiday will not be accepted as an answer. Those are examples of tokenism to make the dominant culture feel like they are doing something. These two activities are fun and interesting, but not how we will strengthen our democracy.
.
✍Report OverviewIn this assignment, you will Document an.docxanhlodge
✍
Report Overview
In this assignment, you will
Document and reflect on your university education and on learning experiences outside of the university;
Articulate how your upper-level coursework is an integrated and individualized curriculum built around your interests; and
Highlight the experiences, skills, and projects that show what you can do.
A successful report submission will be the product of many hours of work over several weeks.
A report earning maximum available points will be a carefully curated and edited explanation of your work that provides tangible evidence of—and insights into—your competencies and capabilities over time. In each section of this report, you are (1) telling a story about your own abilities, and (2) providing specific examples and evidence that illustrate and support your claims.
✍
Required Report Sections
Here the sections are listed as they must appear in your final graded submission. You’ll arrange the sections in this order when
submitting
the final report BUT you won’t follow this order when
writing
drafts of each section.
Note that each section description contains a Pro Tip that tells you how to proceed with the work – what to attempt first, second, and third, etc.
❖ I. Statement of Purpose ❖
Step 1.
Read these four very different
examples of successful Statement of Purpose sections
.
Step 2.
Consider the differences in tone, style, level of detail etc. Your own statement of purpose may resemble one of these. Indeed, writing a first draft based on an example or combination of examples is a good idea. BUT don’t let these examples limit your thinking or personal expression. You may want to begin with a quote from a famous person, use a quote from your mom, or skip the quote. You may want to discuss your personal motivations or get right down to the facts. You may want to list your classes or discuss how your work-life led you to this path.
Step 3.
Write a rough draft – let’s call that Statement of Purpose 1.0. Write Statement of Purpose 1.0 as quickly as you can and then put it away until after you have completed most of the report. Forget about Statement of Purpose 1.0 until most of your report is at least in draft form.
Step 4.
Once you have a draft of all sections of your report, you are in a good position to revise Statement of Purpose 1. You are ready for Step 4. Take Statement of Purpose 1.0 out its dusty vault and hold it up to the sun. Ah. Now read your report draft and compare it to the claims you made in Statement of Purpose 1.0. Ask yourself these questions:
Does Statement of Purpose 1.0. accurately introduce my report?
Are there important ideas or representative experiences in the report that should be highlighted in the Statement of Purpose but aren’t? Remember this isn’t a treasure hunt where its your reader’s job to figure out what matters. It’s your job to show the reader what matters.
If Statement of Purpose 1.0. isn’t the best map it can be for th.
☰Menu×NURS 6050 Policy and Advocacy for Improving Population H.docxanhlodge
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NURS 6050 Policy and Advocacy for Improving Population Health
Back to Course Home
Course Calendar
Syllabus
Course Information
Resource List
Support, Guidelines, and Policies
Module 1
Module 2
Module 3
Module 4
Module 5
Module 6
.
▪ Learning Outcomes1.Understand the basic concepts and termin.docxanhlodge
▪
Learning Outcomes:1.
Understand the basic concepts and terminology used in Strategic Management. (Lo 1.2)2.
Understand the Corporation Social Responsibility
(Lo 1.4).3.
Explain how executive leadership is an important part of strategic management (Lo 3.4)
✓
Question 1
: How does strategic management typically evolve in a corporation? (
1Mark)
✓
Question 2
: Discuss the influence of globalization, social responsibility and environmental sustainability on strategic management of a corporation.(
2 Marks
)
✓
Question 3:
In what ways can a corporation’s structure and culture be internal strengths or weaknesses? Justify your answer by examples from real market. (
1Mark)
✓
Question 4:
When does a corporation need a board of directors? Justify your answer by an example from Saudi market.
(1 Mark)
Notes:
-
Your answers
(for the
4
questions)
MUST include at least
three scholarly peer-reviewed references
,
using a proper referencing style (APA).
Keep in mind that these scholarly references
can be found
in the
Saudi Digital Library (SDL).
-
Make sure to support your statements with logic and argument, citing all sources referenced.
Your answers should not include m
.
● What are some of the reasons that a MNE would choose internationa.docxanhlodge
● What are some of the reasons that a MNE would choose international expansion through an acquisition? An IJV? An alliance?
● What are the variables that would influence the decision?
● Which choice do you believe is best for the likely benefit of the firm? (Cite and reference).
.
▶︎ Prompt 1 Think about whether you identify with either Blue or .docxanhlodge
▶︎ Prompt 1:
Think about whether you identify with either Blue or Red or "Left vs. Right" characteristics of conservative or liberal, left or right America. Do you see yourself, or the people in the place you grew up, on either side of the divide, or perhaps in a different political category? Share some ways in which you identify with some of the descriptions, or ways in which they seem foreign to you.
I'll attach the picture below
.
⁞ InstructionsChoose only ONE of the following options .docxanhlodge
⁞ Instructions
Choose only
ONE
of the following options below and, in your post, write a paraphrase that avoids plagiarism of the paragraph you have chosen. Your paraphrase can be as long as the excerpt you have chosen, but should not duplicate any phrasing from the excerpt. If you must, you can quote up to three words in a phrase.
Choose to paraphrase ONE of the excerpts below:
Option 1
Morrison began writing Sula in 1969, a time of great activism among African Americans and others who were working toward equal civil rights and opportunities. The book addresses issues of racism, bigotry, and suppression of African Americans; it depicts the despair people feel when they can't get decent jobs, and the determination of some to survive. Eva, for example, cuts off her leg in order to get money to raise her family. Morrison shows how, faced with racist situations, some people had to grovel to whites simply to get by, as Helene does on a train heading through the South. Others, however, fought back, as Sula does when she threatens some white boys who are harassing her and Nel.
or
Option 2
In 1993, Morrison was awarded the Nobel Prize for literature, and thus became the first African American and only the eighth woman ever to win the award. According to Maureen O'Brien in Publishers Weekly, Morrison said, "What is most wonderful for me personally is to know that the Prize has at last been awarded to an African American. I thank God that my mother is alive to see this day." In 1996, she received the National Book Foundation Medal for Distinguished Contribution to American Letters.
.
⁞ InstructionsChoose only ONE of the following options below.docxanhlodge
⁞ Instructions
Choose only
ONE
of the following options below and, in your post, write a paraphrase that avoids plagiarism of the paragraph you have chosen. Your paraphrase can be as long as the excerpt you have chosen, but should not duplicate any phrasing from the excerpt. If you must, you can quote up to three words in a phrase.
When you are done posting your paraphrase, reply to at least one classmate’s paraphrase, commenting on what s/he has done well and what s/he can improve with the wording. Your response should be written in no fewer than 75 words.
Choose to paraphrase ONE of the excerpts below:
Option 1
Morrison began writing Sula in 1969, a time of great activism among African Americans and others who were working toward equal civil rights and opportunities. The book addresses issues of racism, bigotry, and suppression of African Americans; it depicts the despair people feel when they can't get decent jobs, and the determination of some to survive. Eva, for example, cuts off her leg in order to get money to raise her family. Morrison shows how, faced with racist situations, some people had to grovel to whites simply to get by, as Helene does on a train heading through the South. Others, however, fought back, as Sula does when she threatens some white boys who are harassing her and Nel.
or
Option 2
In 1993, Morrison was awarded the Nobel Prize for literature, and thus became the first African American and only the eighth woman ever to win the award. According to Maureen O'Brien in Publishers Weekly, Morrison said, "What is most wonderful for me personally is to know that the Prize has at last been awarded to an African American. I thank God that my mother is alive to see this day." In 1996, she received the National Book Foundation Medal for Distinguished Contribution to American Letters.
Your discussion post will be graded according to the following criteria:
- Clear paraphrase the selected text in your own words with minimal use of quotations
.
⁞ InstructionsAfter reading The Metamorphosis by Frank .docxanhlodge
⁞ Instructions
After reading
The Metamorphosis
by Frank Kafka , choose
one
of the following assertions and write a 200-word response supporting why you agree or disagree with it.
Gregor’s transformation highlights his isolation and alienation before his metamorphosis.
Or
Despite having become an insect, Gregor is more humane and sensitive than his family.
Or
If Gregor had been a stronger person, he would have been able to avoid all of the suffering and alienation he endures.
.
⁞ InstructionsAfter reading all of Chapter 5, please se.docxanhlodge
⁞ Instructions:
After reading all of
Chapter 5
, please select
ONE
of the following
primary source readings
:
“Utilitarianism” by John Stuart Mill
(starting on page 111)
-or-
“A Theory of Justice” by John Rawls
(starting on page 115)
-or-
“The Entitlement Theory of Justice” by Robert Nozick
(starting on page 122)
Write a short, objective summary of
250-500 words
which summarizes the main ideas being put forward by the author in this selection. Your summary should include no direct quotations from any author. Instead, summarize in your own words, and include a citation to the original. Format your Reading Summary assignment according to either MLA or APA formatting standards, and attach as either a .doc, .docx, or .rtf filetype. Other filetypes, or assignments that are merely copy/pasted into the box will be returned ungraded.
.
⁞ InstructionsAfter reading all of Chapter 2, please select.docxanhlodge
⁞ Instructions:
After reading all of
Chapter 2
, please select
ONE
of the following
primary source readings
:
“Anthropology and the Abnormal” by Ruth Benedict
(starting on page 33)
-or-
“Trying Out One’s New Sword” by Mary Midgley
(starting on page 35)
Write a short, objective summary of
250
which summarizes the main ideas being put forward by the author in this selection.
Write a short summary that identifies the thesis and outlines the main argument.
Reading summaries are not about your opinion or perspective – they are expository essays that explain the content of the reading.
All reading summaries must include substantive content based on the students reading of the material.
Reading Material: Doing Ethics
ORIGINIAL WORK. NO PLAGIARISM
.
⁞ Instructions After reading all of Chapter 9, please .docxanhlodge
⁞ Instructions:
After reading all of
Chapter 9
, please select the following
primary source reading
:
“A Defense of Abortion” by Judith Jarvis Thomson
(starting on page 237)
Write a short, objective summary of
250-500 words
which summarizes the main ideas being put forward by the author in this selection. Your summary should include no direct quotations from any author. Instead, summarize in your own words, and include a citation to the original. Format your Reading Summary assignment according to either MLA or APA formatting standards, and attach as either a .doc, .docx, or .rtf filetype. Other filetypes, or assignments that are merely copy/pasted into the box will be returned ungraded.
.
…Multiple intelligences describe an individual’s strengths or capac.docxanhlodge
“…Multiple intelligences describe an individual’s strengths or capacities; learning styles describe an individual’s traits that relate to where and how one best learns” (textbook quote, [H2] Learning Styles].
This week you’ve read about the importance of getting to know your students in order to create relevant and engaging lesson plans that cater to multiple intelligences and are multimodal.
Assignment Instructions:
A. Using
SurveyMonkey
, create a survey that has:
At least five questions based on Gardner’s theory
Five questions on individual learning style inventory
A specific targeted student population grade level (elementary/ middle/ high school/adults)
Include the survey link for your peers
B. Post a minimum 150 word introduction to your survey, using at least one research-based article (cited in APA format) explaining how it will:
Evaluate students’ readiness
Assist in the creation of differentiated lesson plans.
.
••• JONATHAN LETHEM CRITICS OFTEN USE the word prolifi.docxanhlodge
- Jonathan Lethem is known for publishing many novels, stories, essays and other works across different genres. He is described as a "protean" or shape-shifting writer.
- Lethem believes creativity comes from influence and interaction with other works, not isolated originality. He celebrates the "ecstasy of influence" where culture is built upon what came before through borrowing and remixing.
- Many artists, including musicians, visual artists and writers, engage in practices that borrow and reuse elements from other works but these practices are seen as essential to creativity rather than plagiarism. Appropriation and remixing are at the core of cultural production.
•••••iA National Profile ofthe Real Estate Industry and.docxanhlodge
•••••i
A National Profile of
the Real Estate Industry and
the Appraisal Profession
by J. Reid Cummings and Donald R. Epley, PhD, MAI, SRA
FEATURES
T
J- he
he real estate industry has been devastated on many fronts' in the years
following the Great Recession, whieh began in 2007^ due to the bursting of the
housing bubble and the subsequent finaneial crisis relating to the mortgage
market meltdown.' The implosion of the mortgage markets initially began when
two Bear Stearns mortgage-backed securities hedge funds, holding nearly $10
billion in assets, disintegrated into nothing.* Panie quickly spread to financial
institutions that could not hide the extent of their toxic, subprime exposures, and
a massive, worldwide credit squeeze ensued; outright fear soon replaced panic.
Subsequent eredit tightening and substantial illiquidity in the financial markets
rapidly and severely affected the housing and construction markets.' Throughout
the United States, properties of all kinds saw dramatic value declines.
In thousands of cases, real estate foreclosures disrupted people's lives,
forced businesses to close, eaused financial institutions to falter, capsized wbole
market segments, devastated entire industries, and squeezed municipal and state
government budgets dependent upon use and property tax revenues.* While the
effeets of property value declines and the waves of foreclosures in markets across
the country captured most of the headlines, one significant impact of the upheaval
in US real estate markets has gone largely unreported: its impact on employment
in the real estate industry, and specifically, the real estate appraisal profession.
This article presents a
current employment
profile of the US real
estate industry, with
special attention given
to appraisal profes-
sionals. It serves as an
informative picture of
the appraisal profession
for use as a benchmark
for future assessment
of growth. As a
component of the real
estate industry, the
appraisal profession
ranks as the smallest
in employment, is
highly correlated to
movements in empioy-
ment of brokers and
agents, and relies on
commerciai banking,
credit, and real estate
lessors and managers
to deliver its products.
1. James R. DeLisle, "At the Crossroads of Expansion and Recession," TheAppraisalJournal 75, no. 4 (Fall 2007):
314-322; James R. DeLisle, "The Perfect Storm Rippiing Over to Reai Estate," The Appraisal Journal 76, no,
3 (Summer 2008): 200-210.
2. Randaii W. Eberts, "When Wiii US Empioyment Recover from tiie Great Recession?" International Labor Brief
9, no. 2 (2011): 4-12 (W. E. Upjohn Institute for Employment Research): Chad R. Wilkerson, "Recession and
Recovery Across the Nation: Lessons from History," Economic Review 94, no. 2 (2009): 5-24.
3. Kataiina M. Bianco, The Subprime Lending Crisis: Causes and Effects of the Mortgage Meltdown (New York:
CCH, inc., 2008): Lawrence H. White, "Fédérai Reserve Policy and the Housing Bubbie," in Lessons Fro.
Let us consider […] a pair of cases which I shall call Rescue .docxanhlodge
“Let us consider […] a pair of cases which I shall call Rescue I and Rescue II. In the first Rescue story we are hurrying in our jeep to save some people – let there be five of them – who are imminently threatened by the ocean tide. We have not a moment to spare, so when we hear of a single person who also needs rescuing from some other disaster we say regretfully that we cannot rescue him, but must leave him to die. To most of us, this seems clear […]. This is Rescue I and with it I contrast Rescue II. In this second story we are again hurrying to the place where the tide is coming in in order to rescue the party of people, but this time it is relevant that the road is narrow and rocky. In this version, the lone individual is trapped (do not ask me how) on the path. If we are to rescue the five we would have to drive over him. But can we do so? If we stop he will be all right eventually: he is in no danger unless from us. But of course, all five of the others will be drowned. As in the first story, our choice is between a course of action that will leave one man dead and five alive at the end of the day and a course of action which will have the opposite result. (Philippa Foot, “Killing and Letting Die,” from Abortion and Legal Perspectives, eds. Garfield and Hennessey, 2004, University of Massachusetts Press)
1. What would Mill tell the rescuer to do, in Rescue I and Rescue II, according to his theory of utilitarianism? Be clear in explaining Mill’s recommendation, and how he would justify it. In doing so, you must include a discussion of the following:
o The Principle of Utility and how it would specifically apply in this situation—who gets “counted” and how?
2. What would Kant tell the rescuer to do, in Rescue I and Rescue II, according to his deontological theory? Be clear in explaining Kant’s recommendation and how he would justify it. In doing so, you must include a discussion of the following:
o The first version of the Categorical Imperative and how it would specifically apply in these two situations (hint, you have to say what the maxim would be and what duty would be generated according to it).
o The second version of the Categorical Imperative and how it would specifically apply in this situation.
3. Explain one criticism of both Mill and Kant. Afterward, argue for which ethical approach, on your view is superior. Be specific and provide reasons for your claim.
.
• Enhanced eText—Keeps students engaged in learning on th.docxanhlodge
• Enhanced eText—Keeps students engaged in learning on their own time,
while helping them achieve greater conceptual understanding of course
material. The worked examples bring learning to life, and algorithmic practice
allows students to apply the very concepts they are reading about. Combining
resources that illuminate content with accessible self-assessment, MyLab
with Enhanced eText provides students with a complete digital learning
experience—all in one place.
• MediaShare for Business—Consisting of a curated collection of business
videos tagged to learning outcomes and customizable, auto-scored
assignments, MediaShare for Business helps students understand why they
are learning key concepts and how they will apply those in their careers.
Instructors can also assign favorite YouTube clips or original content and
employ MediaShare’s powerful repository of tools to maximize student
accountability and interactive learning, and provide contextualized feedback
for students and teams who upload presentations, media, or business plans.
• Writing Space—Better writers make great
learners who perform better in their courses.
Designed to help you develop and assess concept
mastery and critical thinking, the Writing Space
offers a single place to create, track, and grade
writing assignments, provide resources, and
exchange meaningful, personalized feedback with
students, quickly and easily. Thanks to auto-graded, assisted-graded, and create-your-own assignments, you
decide your level of involvement in evaluating students’ work. The auto-graded option allows you to assign
writing in large classes without having to grade essays by hand. And because of integration with Turnitin®,
Writing Space can check students’ work for improper citation or plagiarism.
• Branching, Decision-Making Simulations—Put your students in the
role of manager as they make a series of decisions based on a realistic
business challenge. The simulations change and branch based on their
decisions, creating various scenario paths. At the end of each simulation,
students receive a grade and a detailed report of the choices they made
with the associated consequences included.
Engage, Assess, Apply
• Learning Catalytics™—Is an interactive, student response tool that
uses students’ smartphones, tablets, or laptops to engage them in
more sophisticated tasks and thinking. Now included with MyLab
with eText, Learning Catalytics enables you to generate classroom
discussion, guide your lecture, and promote peer-to-peer learning
with real-time analytics.
• LMS Integration—You can now link from Blackboard Learn, Brightspace
by D2L, Canvas, or Moodle to MyManagementLab. Access assignments,
rosters, and resources, and synchronize grades with your LMS gradebook.
For students, single sign-on provides access to all the personalized
learning resources that make studying more efficient and effective.
• Reporting Dashboard—View, analyze, and re.
• Here’s the approach you can take for this paperTitle.docxanhlodge
This document outlines the structure for a 15-20 page paper on risk management for an organization. It should include an introduction providing background on the selected organization, descriptions of 3 risks with their impacts and recommendations for managing each risk, a conclusion, and references. The paper needs a title page and should follow APA style formatting.
•Your team will select a big data analytics project that is intr.docxanhlodge
•Your team will select a big data analytics project that is introduced to an organization of your choice … please address the following items:
•Provide a background of the company chosen.
•Determine the problems or opportunities that that this project will solve. What is the value of the project?
•Describe the impact of the problem. In other words, is the organization suffering financial losses? Are there opportunities that are not exploited?
•Provide a clear description regarding the metrics your team will use to measure performance. Please include a discussion pertaining to the key performance indicators (KPIs).
•Recommend a big data tool that will help you solve your problem or exploit the opportunity, such as Hadoop, Cloudera, MongoDB, or Hive.
•Evaluate the data requirements. Here are questions to consider: What type of data is needed? Where can you find the data? How can the data be collected? How can you verify the integrity of the data?
•Discuss the gaps that you will need to bridge. Will you need help from vendors to do this work? Is it necessary to secure the services of other subject matter experts (SMEs)?
•What type of project management approach will you use this initiative? Agile? Waterfall? Hybrid? Please provide a justification for the selected approach.
•Provide a summary and conclusion.
.
How to Manage Reception Report in Odoo 17Celine George
A business may deal with both sales and purchases occasionally. They buy things from vendors and then sell them to their customers. Such dealings can be confusing at times. Because multiple clients may inquire about the same product at the same time, after purchasing those products, customers must be assigned to them. Odoo has a tool called Reception Report that can be used to complete this assignment. By enabling this, a reception report comes automatically after confirming a receipt, from which we can assign products to orders.
Level 3 NCEA - NZ: A Nation In the Making 1872 - 1900 SML.pptHenry Hollis
The History of NZ 1870-1900.
Making of a Nation.
From the NZ Wars to Liberals,
Richard Seddon, George Grey,
Social Laboratory, New Zealand,
Confiscations, Kotahitanga, Kingitanga, Parliament, Suffrage, Repudiation, Economic Change, Agriculture, Gold Mining, Timber, Flax, Sheep, Dairying,
A Free 200-Page eBook ~ Brain and Mind Exercise.pptxOH TEIK BIN
(A Free eBook comprising 3 Sets of Presentation of a selection of Puzzles, Brain Teasers and Thinking Problems to exercise both the mind and the Right and Left Brain. To help keep the mind and brain fit and healthy. Good for both the young and old alike.
Answers are given for all the puzzles and problems.)
With Metta,
Bro. Oh Teik Bin 🙏🤓🤔🥰
Leveraging Generative AI to Drive Nonprofit InnovationTechSoup
In this webinar, participants learned how to utilize Generative AI to streamline operations and elevate member engagement. Amazon Web Service experts provided a customer specific use cases and dived into low/no-code tools that are quick and easy to deploy through Amazon Web Service (AWS.)
This document provides an overview of wound healing, its functions, stages, mechanisms, factors affecting it, and complications.
A wound is a break in the integrity of the skin or tissues, which may be associated with disruption of the structure and function.
Healing is the body’s response to injury in an attempt to restore normal structure and functions.
Healing can occur in two ways: Regeneration and Repair
There are 4 phases of wound healing: hemostasis, inflammation, proliferation, and remodeling. This document also describes the mechanism of wound healing. Factors that affect healing include infection, uncontrolled diabetes, poor nutrition, age, anemia, the presence of foreign bodies, etc.
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Andreas Schleicher presents PISA 2022 Volume III - Creative Thinking - 18 Jun...EduSkills OECD
Andreas Schleicher, Director of Education and Skills at the OECD presents at the launch of PISA 2022 Volume III - Creative Minds, Creative Schools on 18 June 2024.
Running title TRENDS IN COMPUTER INFORMATION SYSTEMS1TRENDS I.docx
1. Running title: TRENDS IN COMPUTER INFORMATION
SYSTEMS 1
TRENDS IN COMPUTER INFORMATION SYSTEMS 4
Trends in Computer Information Systems, and the Rise to
Business Intelligence
Shad Martin
School for Professional Studies
St. Louis University
ENG 2005 Dr. Rebecca Wood
November 23, 2016
2. Introduction
Our quest to increase our knowledge of Computer
Information Systems has produced a number of benefits to
humanity. The innovation humans have discovered in Computer
Information Systems has led to new sub-areas of study for
students and professionals to continue their progression to
master all that Computer Information Systems has to offer. Amy
Web of the Harvard Business Review reported 8 Tech Trends to
Watch in 2016, She noted, “In order to chart the best way
forward, you must understand emerging trends: what they are,
what they aren’t, and how they operate. Such trends are more
than shiny objects; they’re manifestations of sustained changes
within an industry sector, society, or human behavior. Trends
are a way of seeing and interpreting our current reality,
providing a useful framework to organize our thinking,
especially when we’re hunting for the unknown. Fads pass.
Trends help us forecast the future” (Harvard Business Review,
2015). In short, Amy’s reference to understanding the emerging
trends in Computer Information can provide a framework from
which, students, professionals, and scientists to conscientiously
create a path towards optimizing their efforts. Ensuring we have
a fundamental approach to analyze data will enhance our
understanding of this subject further.
In this paper I will expound on three of the top trends used to
provide insight into the data produced from the advancements in
Computer Information Systems. These trends or methods are
taking place in my workplace within a financial institution, and
in many other industries. It is important to note this paper does
3. not provide an inclusive list of all methodologies that exist.
Individuals can now leverage analytics to synthesize insights
from data to identify emerging risk, manage operational risks,
identify trends, improve compliance, and customer satisfaction.
Data in and by itself is not always useful. Regardless of the data
source, trained professional must understand the best approach
to structure the data to make it more useful. In this paper, I will
touch on three popular methodology trends occurring in
Computer Information Systems. Students and professionals who
work with large data would benefit from having a solid
understanding of the fundamental principles of Business
Intelligence as data scientific approach and when to use these
methodologies.
The rise of Business Intelligence
Computer Information Systems allow many companies to gather
and generate large amounts of data on their customers, business
activities, potential merger targets, and risks found in their
organization. These large sets of data have given rise to various
forms of intelligence, often referred to Business Intelligence or
BI. Business Intelligence is the next best thing to an
organization having a crystal ball into the future. Within
Business Intelligence there are specific techniques used to
examine the data. Based on my research there are three widely
fundamental concepts of Business Intelligence: Predictive,
Descriptive and Prescriptive Analytics. These methods used by
many Business Intelligence professionals allow individuals and
companies detailed insight into understanding what has
happened, which could help companies predict what could
happen. In this paper, I will touch on these three methods.
Predictive Analytics
Predictive analytics is an emerging field of study used to mine
data from various systems. It entails extracting actionable
information from data. Actionable information is widely
considered to be information that could lead to organizations to
make better business decisions and implement strategic business
approaches. Bersin by Deloitte, defines this subject as:
4. “[a]ctionable information” provides data that can be used to
make specific business decisions. Actionable information is
specific, consistent, and credible.” Bersin provided one of the
best examples on this topic. Bersin goes on to describe a related
example, in which he cites the following, “[f]or example, a
report which shows trends in "employee retention" is important
and interesting, but not necessarily actionable. However, a
dashboard or simple red / yellow / green report which shows
managers the turnover rate by department, accompanied by the
"top three reasons for leaving the company," is far more
actionable. In any HR or L&D data and reporting program, it is
always important to drive toward giving managers data which is
not only interesting, but actionable” (Bersin).
Business Intelligence utilizes many techniques to derive
actionable information from datasets, one of them is referred to
as Predictive Analytics. Predictive Analytics can help
organizations and companies predict behavioral patterns, trends,
forecasting, and probabilities into various business activities,
e.g., customer buying patterns or healthcare costs. These
methods are being applied to a number of areas impacting our
daily lives; e.g., engineering, biometrics, social media, city
planning, and a number of others areas. This methodology has
seen widespread implementation within many companies. A
multitude of universities have also begun to offer classes on this
topic to help students strengthen their understanding of this
discipline. Predictive Analytics uses historical data understand
what has happened in the past to predict what might happen in
the future. This forward looking approach is a new trend many
companies are using to improve their decision making and
create a stronger competitive advantage. Predictive Analytics is
be a valuable tool for companies to evaluate current business
process and identify areas where risks exist, or are in need a
business process redesign.
Descriptive Analytics
Descriptive Analytics is another fundamental principle
(methodology) to evaluate data for deeper insight. Dr. Michael
5. Wu, a renowned chief scientist of San Francisco-based Lithium
Technologies (2), gave a simplistic definition of the Descriptive
Analytics in a March 2013 blog series on this topic. According
to Wu, who believes descriptive analytics "the simplest class of
analytics," one that allows you to condense big data into
smaller, more useful nuggets of information (Wu,
InformationWeek 2013). This approach allows students and
professional to manipulate large datasets to be scaled to levels
where useful granular information can be shared more easily.
This approach is critical when providing information about the
data to the highest levels of an organization or the general
public. Especially, since many senior leaders simply do not
have the time to read through numerous pages of data to get to
the main point of the information. The general public also does
not have the appetite to consume large amounts of information
found in data. Concise issues from data is the best way to share
information to the general public.
Prescriptive Analytics
Prescriptive Analytics is the last method included in this paper.
Prescriptive Analytics is used to help companies chart a
decision on which way a company or organization should go,
based on the data. In short, it is used to ‘prescribe’ an answer
for an organization. This data driven approach is critical
because Predictive, and Descriptive Analytics can plot multiple
scenarios. When used correctly it can generate several solid,
fact based information paths from the data. As Wu describes
“Predictive Analytics is used to predict the possible
consequences” (Wu. InformationWeek, 2013), based on
different choices an organization, company, or professional
chooses. Just as in the medical field, once the data is analyzed,
a diagnosis is needed.
Conclusion
In the future, Business intelligence will be less about
investigative patterns from past events, rather, Business
6. Intelligence will be more about predicting the future. All data
has a story, and the employing the correct approaches can help
the audience understand what the information in the data is
saying. We cannot tell the future by looking at the past; this
assumes that conditions have or will remain the same. The three
most common analytical methods: Predictive, Descriptive, and
Prescriptive analytics, can help us approach data in a targeted
fashion. This will help us understanding the information of the
past to better predict the future. Business Intelligence will
continue to exist as an emerging area of new methodologies for
data mining, extracting information from data and using it to
predict trends, and one day human behavior patterns. Although
this paper named three of the Business Intelligence to Computer
Information Systems, these approaches, and many others should
not be considered in isolation. Each one has an important roles
based on the objective of the data evaluation goal. The
challenge for us will be how to communicate the best
information within the data in the most concise, effective, and
coherent manner that best demonstrates a forward-looking
approach.
References
Web, Amy. “8 Tech Trends to Watch in 2016.” Harvard
Business Review, retrieved 12, November, 2016. Web.
From: https://hbr.org/2015/12/8-tech-trends-to-watch-in-2016
Bertolucci, Jeff. “Big Data Analytics: Descriptive Vs.
Predictive Vs. Prescriptive.” InformationWeek. December,
2013. Retrieved 14, November, 2016. Web.
From: http://www.informationweek.com/big-data/big-data-
7. analytics/big-data-analytics-descriptive-vs-predictive-vs-
prescriptive/d/d-id/1113279
Parashar, Manish and George Thiruvathukal. “Extreme Data.”
Computing in Science Engineering. Vol. 16, No. 04. Pp: 8-10
July-Aug, 2014. Retrieved 14, November, 2016. Web.
From:
http://doi.ieeecomputersociety.org/10.1109/MCSE.2014.83
Abbott, Dean. “Applied Predictive Analytics: Principles and
Techniques for the Professional Data Analyst.” Wiley. Pp 6-30.
17, November, 2016.
Bibliographies
Holland, P.R. (2015). SAS Programming and Data
Visualization Techniques. New York, NY: Springer
Science+Business Media.
The SAS Programming and Data Visualization Techniques
book provides insight into one of the many software systems
utilized by Computer Information System professions to gain
insight from large quantity of data within various Computer
Information Systems. The book details the best approaches for a
user of this software to become proficient in coding efficiency.
The book also provided rationale for which charts, or graphs
should be used to visualize data. SAS as known as Statistical
Analysis Software is a power tool used to extract extremely
large datasets from various data warehouses within many large
financial, insurance, and technological institutions. The book
explains data like epidemiology, “[i]n epidemiology, most data
8. sets from SAS and other databases are big, and SAS
programmers need particular skills to work with data.” Holland
explains, an undisciplined approach to large data extracts can
lead to many issues. Holland asserts, “[c]reating reports
containing multiple output can result in files that are too large
to e-mail or that contain too many individual files to transfer
together.” In the book, Holland details a number of proven
techniques to help users gain a stronger understanding of the
many programming techniques needed to gain a proficient
knowledge of this tool. The book details many the best approach
to displaying information extracted from large datasets. Some of
the techniques mentioned in the book were: industry wide
accepted analytic approaches, which will help novice users
understand which software applications are the most effective to
use based on the size of the data.
Provost, F. and Tom Fawcett. (2013). Data Science for
Business. Sebastopol, CA: O’Reilly Media.
This book provides excellent information on the importance of
Business Intelligence in today’s digital age, and the associating
methods needed to approach large data sets strategically. The
book was not over technical. The author has a pragmatic
approach, which helped me understand the content and convey
the information in a relatable fashion. The detailed examples
provided in the book helped me frame the message. Data
Science for Business offers a fundamental approach to digest
large datasets. Understanding data will continue to be an
important step for business “[i]f solving the business problem is
the goal, the data comprise the available raw material from
which the solution will be built. It is important to understand
the strength and limitations of the data because rarely is there
an exact match with the problems” (Provost, 2013, p. 28). This
book highlights great point about data. It strongly infers that
not all data is good data. In Data Science for Business, Foster
Provost explains the importance of a strategic approach to
9. evaluate data, since most data is backward looking. In the book,
Provost asserts that “[h]istorical data often are collected for
purposes unrelated to the current business problem, or for no
explicit purpose at all” (26). This message is critical in
determining what data to capture and which approach could
yield the most insight. This book highlights the most effective
Business Intelligence methods, which helps unfamiliar people
more competent on this topic.
Abbot, D. (2014). Applied Predictive Analytics: Principles and
Techniques for the Professional Data Analyst. Indianapolis, IN
John Wiley & Sons.
This book provides excellent foundation from which I was able
to understand Business Intelligence and why it is so important
for users who analyze data. The book provides easy to follow
examples of why various analytic approaches should be
considered. “Analytics is the process of using computational
methods to discover and report influential patterns in data,”
(Abbot, p.3, 2014). Business Intelligence is often used
interchangeably with analytics. Abbot’s approach to introducing
the reader to foundational principles of Business Intelligence
helped ensure the readers comprehension on this topic. Business
Intelligence is a huge area of study for many professionals, and
students. Business Intelligence will remain an important aspect
in our everyday lives. The detailed examples on Business
Intelligence and analytics provided in the book helped me select
the key strategic approaches to highlight in this paper. Datasets
are also discussed in the book. The size of a dataset can
determine which Business Intelligence approach should be used.
Conversely, the book does a great job allowing the reader to
participant in the learning exercises. The overall theme of the
book was to explain the logic-driven methods in a practical
manner. The book achieved this goal.
10. Schniederjans. M, Dara Schniederjans, and Christpoer Starkey.
(2014). Business Analytic – Principles, Concepts, and
Application. Upper Saddle River, NJ. Pearson Education.
This book instrumental to understanding the logic behind the
various Business Intelligence approaches. The book explained
why exploratory data needs to be visualized. The book
presented operational problems to help guide the reader through
the various problem solving steps. The book also does a really
good job of explaining the undertaking of Descriptive
Analytics. Schniederjans asserts an explanation of data in his
book, “regardless of the Business Analysis assignment, the first
step is one of exploring data and revealing new, unique, and
relevant information to help the organization advance its goals,”
(Schniederjans, 2014, p. 63). The book explains the importance
of selecting the appropriate analytical method based on what
one’s initial observation of the data produces. The book also
explains why it is important to first study the data and later
come back to select the best approach to reveal the insight.
11. Fall Semester II | Shad Martin
Module 4 Assignment: Becoming a Scholar of Change-
Elementary Reading
Createan 7-slide, narrated multimedia presentation (e.g.,
PowerPoint, Prezi, etc.), using the notes section to create an
ADA-compliant transcript, which includes the following:
· An explanation of a problem or challenge in your school
setting or community and how it fits within the global
educational climate
· A plan for how you intend to be an agent for positive social
change with regard to the problem or challenge you identified.
· An explanation of the impact of positive social change on your
local environment you hope to make through the implementation
of your plan.
Your presentation must include:
· A cover slide and reference slides (in APA format).
· Citations within in the presentation to researched information
outside of the Learning Resources for this course.
· Color and graphics that demonstrate that your presentation is
professional in content and context.
Helpful reference within this course.
DuFour, R. (2004). Schools as learning communities: What is a
“professional learning community?”Educational
Leadership, 61(8), 6–11.
Niesz, T. (2007). Why teacher networks (can) work. Phi Delta
Kappan, 88, 605–610.
Walden University. (n.d.e). Social change. Retrieved March 18,
2016, from http://www.waldenu.edu/about/social-change
12. Running title: IMPORTANT ISSUES IN THE ADAVNCEMENT
OF COMPUTER INFORMATION SYSTEMS 1
IMPORTANT ISSUES IN THE ADAVNCEMENT OF
COMPUTER INFORMATION SYSTEMS 7
Important Issues in the Advancement of Computer Information
Systems
Shad Martin
School for Professional Studies
St. Louis University
13. ENG 2005 Dr. Rebecca Wood
November 14, 2016
Introduction
Computer Information Systems is rapidly changing. The
pace of this change creates an ambiguity of implications on the
overall society of humankind. It is not heavily regulated, and as
a result it has flourished. The advancement in computer
information systems or science has elevated economies and
stimulated growth in the world. However, this new found
economic growth carries great risks. The speed of the computer
information systems advancement can possibly leave behind
untold scores of people. Access to the new technology, and
training to understand the deeply complicated embedded
analytics that consist of computer information systems is
intimidating in itself. Of the many examples shared in this
paper, mobile devices is one area where we have seen
tremendous growth. “Mobile devices to become more powerful
have made possible through the technological breakthroughs in
the miniaturization of processors, networking technologies,
memory, displays and sensors. A smart device refers to an
electronic wireless, mobile, always connected (via WiFi, 3G,
4G, etc.) and is capable of voice and video communication,
internet browsing, "geo-location" (for search purposes) and that
can operate to some extent autonomously,” (International
Journal of Multimedia and Ubiquitous Engineering, 2013).
14. Mobile devices have led to the creation of countless
applications. It has introduced so many users (people that use
the device), to new, and exciting technologies. This paper will
endeavor to look into the challenges facing computer
information systems, reasons why some of challenges exists,
and why it is so important to make this topic a focal point for
people, regardless of their age. Thereafter, some of the risks are
inherent to the discipline of computer information systems and
everyone must remain vigilant to be aware of the every
changing landscape.
Important Issues in the Advancement of Computer Information
Systems
Advancements in Computer technology is changing faster than
anyone could ever have imagined. The advancements made in
Computer Information Systems begin with an idea that begins
the journey. It could be an idea for a new feature, application,
service, or business. As a civilization, we have advanced
beyond the photographs, print or scribed books, and newspaper
columns; as a source of current events, columnist opinions, and
historical readings. We now have the ability to post our
thoughts - as we have them - on various types via social media.
In 2016, we have seen many new emerging technologies in our
daily lives. The new dichotomy of Computer Science has
changed the way we go about our daily lives. In the last four
years alone, we now have automobiles with voice recognition,
bank tellers who can engage their customers virtually, and
autonomous driving automobiles.
We have new data environments, which literally store data
everywhere, known as the ‘Cloud.’ Artificial Intelligence (AI)
will become more integrated into the fabric of how humans
engage each other and the world. There are billions of smart
phones that can process requests for data and quickly dispense
information at our finger tips. We can now ‘live stream’ for all
15. of our friends to see what we are doing, feeling or have to say.
We have the ability to sign into our phones or software
applications with biometric methods. This new technology
(biometric) allows us to sign into systems, applications, and our
phones with a finger print, retinal scan, or voice recognition.
These technological advances have lead industry leaders to
predict how more advanced these new technologies are far more
intertwined in the fabric of our lives. As Andrew Drazin, of
Theron LLP, proclaimed; “Working in technology is going to be
a far more interesting and challenging place to work in than it
has been to date. There are going to be more upsides than
downsides.” In this paper I will expound on many types of the
challenges humans can expect as technology continues to
advance. In 2016, it has become a common place to store
information, or data – everywhere.
Access to the new technology
Therefore it is clear from the above, that there will be
inevitable challenges with these new technological
advancements and how some will be significant for the end
users. Innovations in computer science will consequentially
leave many users behind. Many will be unable to comprehend,
or embrace the ever changing computer information revolution.
There will also be people who struggle to gain access to this
new technology due to poverty, or geopolitical governance.
Especially those in developing countries and individuals whose
governments cannot afford to purchase the latest advancements
in technology. Others will struggle with the changes being made
in computer information systems, particularly our aging
population of citizens. This will present many dangers for
everyone. For example, if people are not properly introduced to
new and emerging technologies in computer science there could
be significant human errors that lead to hundreds if not
thousands of casualties. Information security breaches could
occur more frequently, and cost millions, if not billions of
16. dollars in stolen credit card data and associating losses. As
expressed above, it is essential to educate and prepare our
society for the changing technological landscape.
Training
For those who can afford access to the new technology in
computer information systems, they will concentrate their
cognitive abilities on programming and coding software boot
camps, academic specific genres related to computer systems,
and advanced degrees that specialize in Computer Information
Systems. For many in the wealthiest countries of the world this
will come easy. From a training perspective, some of the
essential qualities and characteristics successful individuals will
need highlights by “Reich’s four key skills: abstract thinking,
systems thinking, experimentation, and collaboration,”(MIS
Essentials). These four critical skills sets are needed to help in
the design/planning phase of the new computer information
system, application, software, hardware, etc., being created. A
successful product should be designed with the end user’s
perspective that aligns with the end state of the finish product.
If the proper process, procedures, and precautions in the
planning are not followed, then unintentional disasters could
happen. Therefore it is very clear that the developers of these
new technologies have a forward looking approach to problem
solving.
Conclusion
The evolution of Computer Information Systems is an essential
aspect of any country, organization, and individual, as it has
become a major driver in manufacturing and service industries.
Due to its significance in our lives, it is vital that we ensure
people are properly prepared for the changes that are to come
and that they are properly trained for this technology. Also,
ensuring that every user of this technology understands the risk
17. and rewards of a more computerized environment. It is evident
that this technological genre will have a prolonged life and
prosperity as the technology has enormous potential. Many
people, especially, the younger generation will need to be
stirred in to this technological sector.
As mentioned earlier, customers have to find value in learning
more about this technology in order to remain competitive. Our
country would thrive from large scale training of the new
revolution. Many industries will become more efficient,
reducing staff, which could lead to the creation of large scale
unemployable people. There is a very wide array of industries
that have made the advancement of Computer Information
Systems their top priorities. As we witness these changes, its
challenges, and opportunities, we must ensure we are better
prepared to embrace the new and emerging technologies through
access, proper exposure, and training. Out futures literally
depend on the decisions we make today.
References
Drazin, Andrew and Bill Goodwin. “Future Gazing: The Future
of IT in 2020.” Computer Weekly, retrieved 12, November,
2016. Web.
from:
http://www.computerweekly.com/news/2240209132/Future-
18. Gazing-The-Future-of-IT-in-2020
Caytiles, Ronnie and Byungjoo Park. “Future Directions of
Information and Telecommunication Systems Through the
Technological Advancement Convergence.” International
Journal of Multimedia and Ubiquitous Engineering. Vol. 8, No.
1, January, 2013.
Kroenke, David. “The Importance of MIS.” MIS Essential.
Fourth edition. (2014). Chapter 1. pp. 15.
Nickolaisen, Niel “The Constant Evolution of Technology can
have a downside.” Tech Target. SearchCIO, Retrieved 11,
November, 2016. Web.
From: http://searchcio.techtarget.com/tip/The-constant-
evolution-of-technology-can-have-a-downside
Fall Semester II | Shad Martin
Final Paper Proposal
I would like to propose the topic of data and analytics to my
paper two. In paper two, I discussed the challenges facing the
Computer Information Systems, from an education, access to
software, and information security standpoint. Paper two would
benefit from a more granular view of how the evolution of
Computer Information Systems has not only increase the amount
of useful information for various organizations as they seek to
interpret the new datasets. In many cases, the abundance of data
has also led to an information backlog. There is so much
information in the data that the company’s leaders simply do
not understand how to use the all of the data’s content. This
increase of information has caused many users and their
customers to question the data differently than their perceived
notion of the information. For example, if a bank’s automotive
finance division had a lot of complaints in one area. The
company is financing auto loan for consumers, also receives a
higher amount of complaints from their customer’s in the loan
servicing department. How does the company begin to
understand the customer’s pain points? The company needs to
19. understand what their customers are voicing as a concern within
the ‘servicing’ sector of the bank. The data itself can begin to
tell this story, e.g., why their consumer complaints lodged
against the company’s servicing sector exist. This insight into
the data might be useful for the company to redesign their entire
business process model. Conversely, if the company does not
understand the analytic techniques needed to interpret the data,
this could lead to the data being skewed. An example of this is
data not displayed properly or its presentation (charts, Area
plots, Scatter plots, etc.), could cause the company to
misunderstand the information in it further. To gleam insight
from this data the company most also ensure it has the
appropriate tools to perform data analytics on the data. In short,
data and analytics would be a subset of the advancements in
Computer Information Systems.