reflection paper about business intelligence
Order Description
this paper is a part of my portfolio. the course is CIM 605 Business Intelligence , please write a paper reflection about what I did in class such as weekly group work and discussion to reach thes goals:1- Apply appropriate research methods to problems in information and communication and evaluate solutions to problems.2-Articulate the interrelatedness of rhetoric and information sciences, especially decision-making, human behavior in institutional settings, and the impact of advances in technology in communications.

there are what I did i the course

Forum 3: Making today’s business decisions.
1313
Making high-stakes business decisions has always been hard. Eric Bonabeau asserts that in recent decades, it has become tougher than ever. How does he support his assertion? Do you think the author provided sufficient evidence to support his assertion? Do you agree with his view point? Why or why not?

Remember this is a discussion forum and we will use this during class. You must participate and contribute to the discussion during class in a significant way by entering into a dialogue with several of your classmates in order to receive full credit.
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Forum 4: Intuition – Can You Trust Your Gut?
99
The choices facing managers and the data requiring analysis have multiplied even as the time for analyzing them has shrunk. One simple decision-making tool, human intuition, seems to offer a reliable alternative to painstaking fact gathering and analysis.

Eric Bonabeau references the book, Intuition at Work, by decision-making consultant, Gary Klein’s book. In the book Klein expresses the common wisdom that intuition is “at the center of the decision-making process” and that analysis is, at best, a supporting tool for making intuitive decisions. Encouraged by scientific research on intuition, many top managers when faced with complex decisions feel increasingly confident that they can just trust their gut. Bonabeau disagrees:

“The trust in intuition is understandable. But it’s also dangerous. Intuition has its place in decision
making–you should not ignore your instincts any more than you should ignore your conscience–
but anyone who thinks that intuition is a substitute for reason is indulging in a romantic delusion”.

So make a choice. Which view point do you favor? Present an argument supporting your view.
———
M1Q1 – The Second Road of Thought
11
Golsby-Smith states that Aristotle created two roads (paths) of thought: analytics and rhetoric. Each path was meant to address a different problem domain because the problem being addressed was different.

What type of problem does the analytic path produce? What type of solution does the rhetoric path produce? Golsby-Smith claims that western thought invested more heavily in the analytic path (i.e., the scientific approach). Why does he think that is a mistake? Do you agree or not?
——
M1Q2 – Competing on Analytics
11
Davenport is making an argument in favor of using analytics to support decision-making.Do you agree with his overall premise? Why or why not? What were some of his most persuasive arguments?
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M1Q3 – Harrah’s Casino
Why is Loveman’s marketing approach so different from the traditional marketing done by other casinos? What kind of proof of success did he offer?

How does Harrah’s reward their employees? Do you think this method is good for the casino? Is it good for the employees?

Hint: Pay special attention to his use of data.

——–
Drop box for your Module 1 Summary Report

Abstract
Data Analytics is defined as the science of examining data with the goal of discovering, modeling, and transforming useful information. Skilled professionals need to utilize from this process of data analyzing in order for them to make better decisions and thus make a better business.
Methods: First, company should develop a plan which is to determine what data is collected. Data analytics and presentation must find an adequate explanation for the development of solutions. It is must that verify the authenticity of the data recording and contradictions. it is supposed to choose the data collection method and adjusted based on user feedback to get to a convincing and successful analysis.
Results : Having the ability to use IT services along with focusing on business goals will not only provide us with the advantage of creating better communication with IT management, but also will provide the foundation for planning future services. In the article “The Importance of big Data Analytics in Business,” Wagner states that “The majority of CIOs believe the IT department can increase the value it delivers to the organization by improving cost measurement.”
Conclusion: data analytics reduces costs. Data analytics on an ongoing basis is good to help the survival of the company and access to meet the needs of customers. Data analytics helps to make the right decision quickly and to avoid risks of falling. Data analytics show us how to introduce new products or new services and stay in the progress on your competitors.

Data Analytics is defined as the science of examining data with the goal of discovering, modeling, and transforming useful information. Skilled professionals need to utilize from this process of data analyzing in order for them to make better decisions and thus make a better business.
Data analytics is useful in identifying trends and weaknesses. Data analytics also allow us to determine any conditions that can be utilized in making future decisions. Moreover, data analytics can draw out the relevant information so that it can be analyzed, then transformed and used in future decisions. However, we cannot use this process on large amount of data since traditional storage environment and processing times cannot be maintained.
In order to be successful in the work field, the majority of data is collected by IT, which shares data that is useful for the business. IT services is important to prove the value of IT to business. Having the ability to measure the business outcomes by using IT services is more important than measuring the costs. Having the ability to use IT services along with focusing on business goals will not only provide us with the advantage of creating better communication with IT management, but also will provide the foundation for planning future services. In the article “The Importance of big Data Analytics in Business,” Wagner states that “The majority of CIOs believe the IT department can increase the value it delivers to the organization by improving cost measurement.” (Dave Wagner, 2014)
Managing the big data that is trooping in many organizations every day has become a challenge to many companies. Even if the big data offers a chance for the tremendous growth, good data analytics is crucial in maximizing sales. For many years, McDonalds has stood as an American capitalism great success stories. However, the company has faced low sales in the last few years. McDonald’s has been experiencing low sales, and the company has been trying to go back to the track. According to the study by Ton (2014), the company’s problems started sprouting because of the operational mishaps, competition from various companies, for example, Burger King, Shake Shack among others. McDonald’s has been using the data analytics for some time now. For the years they’ve used the model, the company has not increased the sales. On the other hand, Wendy’s has been using the data analytics a tool that has worked for them even though they are also registering low sales.
In June 2015, the company was devastated because of the low sales and they decided to stop reporting their low financial numbers. However, Burger Chain continued reporting their sales and focusing on the long-term progress. Wendy’s are continuing to give their reports on the sales growth since their sales are highly rising. According to the report by Hill et al. (2016), the strategy McDonalds is using to keep their investors in the dark is not working and will not change the McDonald’s fortunes. McDonald’s data analytics failed because they were unable to meet the need for the speed, address the data quality, understand the data and work on the data to display meaningful results (Runkler, 2012).
McDonald’s introduced the data analytics tool to improve their customer care and the sales that had been dropping. The company implemented the software in all their operational areas, and they thought it will solve the statistically oriented issues. Unlike the McDonald’s, Wendy’s has been using the data analytics that has worked, but their sales have continued to fall. The data analytic software is helping them in analyzing the daily sales and labor costs. Some of the few elements that have made the company to succeed in their data analytics are that they replaced their MyMicros with their WebFOCUS. One of the biggest mistakes that the company has been making regarding the data analytics is the customer service. The annual customer service satisfaction shows that the company does not use the data analytics to upgrade on their customer care services (Runkler, 2012).
The data analytics has been working because it is often updated. According to Hills & Jones (2016), Wendy’s data analytics are refreshed six times daily that assist the managers to keep safely the metrics. Wendy’s data analytics did not work overnight but took a lot of time to practice and update.
Companies should use the data analytics for various reasons. According to the study that was conducted by Minelli, Chambers & Dhiraj (2013), the data analytics reduces the costs. The data analytics assist the companies to keep large amounts of data that assist them in reaching their customers and manage their sales. Secondly, the data analytics should be used to make fast decisions. Big companies review their data when making decisions that affect their company. A company that has highly succeeded in the use of the data analytics is Caesars in making faster decisions. Data analytics can also be used when introducing new products and services and staying ahead of the competitors.
To improve the quality and funding requirements, the company must develop a clear plan to data collection and analysis. First, it should develop a plan which is to determine what data is collected. It is important to confirm the quality of the data collected. The development of data is about known how the data will be collected.
Data analytics and presentation must find an adequate explanation for the development of solutions. It must that find an adequate explanation for the development of solutions by data analytics and presentation. To reach a suitable solution must verify the validity of the data. It is must that verify the authenticity of the data recording and contradictions. it is supposed to choose the data collection method and adjusted based on user feedback to get to a convincing and successful analysis.
If employees capacity can make good decision, the company do not have to conduct data analytics. There are employees understand the needs of customers. They have sufficient information to make appropriate decisions quickly. On addition, they know the customer tastes.
Companies with a culture of evidence-based decision making see to it that business rules are continually assessed and improved by articulating them clearly and ensuring consistency across the company ( Jeanne, 2013). There is evidence explicit and clear need to evaluate and improve only. On the other hand, do not need to do data analytics. Sometimes there are clear expectations for modification and development. In addition, there is a clear plan to improve the performance of each individual.
Data analytic is important to fix any problem in company. In addition, decision-makers have to focus on specific data which is relevant to make a good decision. It is important to search about right information which allowed seeing the problem, and tells there is a problem or there are chances to improve or to be unique. Also, updating information as fast as possible is assist to get data which gives a good indication to take a successful decision.
When making the decision, it must consider the implications of this decision from all respects. While decision-maker collected data to make a perfect decision, they have to make sure that decision make the company in advance for the future, not only so far because it is important to avoid future obstacles.
When considering the number of times a repeat purchase, it means a lot. From this point it can be troubleshooting and the reason for the lack of sales. If the sales go down in some product that means it is not good and maker-decision have to know there are problems. On the other hand, they try to found it. It needs to be amended or add something to be unique. By this data must discover what is problem and try to solve it.
Finally, all company should use data analytics because it is a better way to found problem and fix it faster. Company should update the data analytics on a daily basis to get to the best and fastest results. This is one of the most important things you must found within your company to not only understand your business, but to drive your business forward and keep it unique.
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M2Q1 – Data Warehouses and Data Mining
Jerzy Surma’s description of a data warehouse is a fairly technical paper. It is geared toward technical practitioners. Notice the difference in tone between his paper and mine (see my description of a data warehouse). Which do you think is more appropriate for a CIM student?

Take a moment to reflecting back on the Harrah’s article from Module 1. How did they use their data warehouse to shape their business strategy?
———–
Drop Box for Module 2 Summary Report

Data Warehousing, Data Mining, Normal Curve and Simple Regression and Herb Simon
Abstract: Decision makers can employ a variety of techniques such as data warehousing, normal curve and simple regression to aid them in their decision making processes. Although such techniques are based on a common assumption that people are always seeking to maximize their gains, Herbert Simon’s theory of bounded rationality argues otherwise.
Data mining equips individuals who seek to use it with appropriate information for decisions making. Zaki & Meira (2014) also advises individuals to be careful especially when using the concept in anticipating unpredictable outcomes such as the level of societal changes that are likely to occur on a specific aspect such as technology. Predicting the future may not turn out as people anticipate. Warehousing should be done based on the need of the organizations to increase access to their centers and minimize reliance on other institutions facilities. Linear Regression is often applied in the development of the trend line as it accounts for variables data components at the same time. Linear regression should often be done when there exists data which offers a pattern that is almost similar and this will allow an individual to choose the best fit (Seber & Lee, 2003).
According to Chernoff & Moses (2012), individuals should avoid using decision theory especially if they are focusing on unknowns. However, should the need arise; individuals should use the various models to find a solution that is even and which is best chosen by all decision models.
We can apply different techniques in decision making trying to maximize our benefits. However, it is absolutely impossible to achieve such outcomes since we are bounded by our cognitive constraints. Therefore, we always end up making decisions that are good enough (satisfactory) for us regardless of what techniques we apply.
Decision making involves numerous risks, which may lead to undesirable outcomes. However, decision makers can employ a variety of techniques such as data warehousing, normal curve and simple regression to aid them in their decision making processes. Although such techniques are based on a common assumption that people are always seeking to maximize their gains, Herbert Simon’s theory of bounded rationality argues otherwise.
”Data warehouses have had staying power because the concept of a central data repository—fed by dozens or hundreds of databases, applications, and other source systems—continues to be the best, most efficient way for companies to get an enterprise-wide view of their customers, supply chains, sales, and operations.”(John, 2014)
Data warehouse combines data from multiple data sources and it can be used to analysis in a particular subject area such as sales. Data warehouses are designed to assist how analyze data. By using Data warehouses, you will learn more about your company’s sales data, you can know more about your customer for this item last year, and you can makes the data warehouse subject oriented. Data warehouse help in support of management’s decision making process.
Data warehouse can keep Historical data. For example, data warehouse can keep all information about customer without any change. So, it should never change historical data in the data warehouse because it is for query and analysis. It has interesting advantages which is copied data, processed, integrated, annotated, summarized and restructured in semantic data store in advance, and provide high performance.
Data warehousing is an effective platform for inquiry and analysis of workload data, but not for transaction processing. However, historical data obtain from transaction information is included in data warehousing, although other sources can also acts as sources of data. Thus, data warehousing separates transaction workload from analysis workload, and helps decision makers to combine data obtained from different sources.
Warehousing is simply a concept of storage and in the modern world, it has transformed from the storage of materials and products in large houses to storage of data in servers. Warehousing should be done based on the need of the organizations to increase access to their centers and minimize reliance on other institutions facilities. However, according to Ackerman (2013), institutions should avoid warehousing especially if they cannot afford the price of facilities and can rely on outsourcing. Today, warehousing can be done by choosing the appropriate centers where such facilities can be located.
”For the most part, data mining tells us about very large and complex data sets, the kinds of information that would be readily apparent about small and simple things. For example, it can tell us that “one of these things is not like the other” a la Sesame Street or it can show us categories and then sort things into pre-determined categories. But what’s simple with 5 datapoints is not so simple with 5 billion datapoints.”(Alexander, 2012)
Data mining is a subsection multitasking of computer science. It is a process used by companies to convert data into useful information. Key points of the process of data mining are to extract the data and turn it to information concepts to be used. It means that the extraction of knowledge from large amounts of data. It is usually applied to a form of data or information processing (collection, extraction, warehousing, analysis, and statistics) as well as business intelligence. The expression data mining was added for marketing reasons. The data mining determine multiple groups in the data, and then can be used to get results more accurate prediction through the use of a system of decision making.
According to Leskovec, Rajaraman & Ullman (2014), data mining refers to the practice of identifying data patterns and information which may be used by both individuals and businesses. Data mining is often unique as it equips individuals who seek to use it with appropriate information for decisions making. This is why Zaki & Meira (2014) recommends data mining to be done in anticipating predictable patterns.
Data mining have main functions such as automatic discovery of patterns, to predict possible outcomes, establish process information, focusing on a wide range of data and data mining can answer questions and reporting techniques.
Data mining is a good way to assistance you find patterns and relationships within your data. On the other hand, data mining cannot work by itself. In this case, it important to know your business, understanding the data, and understanding analytical methods to analysis data. Data mining reveal hidden information. On the other hand, it cannot evaluate the company if is success or not.
In the modern world, individuals can apply the concept by using technology which can monitor the aspects they would like to review. Despite all the benefits that accrue from using it, Zaki & Meira (2014) also advises individuals to be careful especially when using the concept in anticipating unpredictable outcomes such as the level of societal changes that are likely to occur on a specific aspect such as technology. Predicting the future may not turn out as people anticipate.
” Linear regression is the most basic and commonly used predictive analysis. Regression estimates are used to describe data and to explain the relationship between one dependent variable and one or more independent variables.” (2013)
There are other names for dependent variable such as a criterion variable, endogenous variable, prognostic variable, or regressand. Furthermore, there are other names for independent variables such as exogenous variables, predictor variables or regressors.
There are three important reasons to use regression analysis. Firstly, linear regression could be used to find out the strength of the effect that the independent variable(s) have on a dependent variable. It reveals that what the strength of relationship between sales and marketing spend. Secondly, linear regression could be used to predict, and this is regression analysis what happens when changing one or more independent variables. Thirdly, linear regression could be used to analysis predicts trends and future price for products.

We can make different key assumptions such as linear relationship, multivariate normality, no auto-correlation, homoscedasticity, and no or little multicollineraity from simple regression. However, in order to make such assumptions, simple regression must have minimum of two metric variables such as interval or ratio. Simple regression can help to determine correlation between different (dependent and independent) variables, which may serve as the source of data.
Linear Regression is a model used in establishing a relationship between one or more variables. The model is often applied as it assists in choosing a solution that cuts across all the outcomes. The model is often applied in the development of the trend line as it accounts for variables data components at the same time. Linear regression should often be done when there exists data which offers a pattern that is almost similar and this will allow an individual to choose the best fit (Seber & Lee, 2003).
”The Normal Distribution is actually created by the researcher. It is common scientific practice to successively isolate and control for all important explanatory variables, each of which causes the original distribution not to resemble the Normal. The appearance of the Normal Distribution only indicates that most of the major explanatory variables have been allowed for, and the rest of the many relevant variables have small influence—the point at which the scientist ends his labors. This is what the Normal Curve “means.” ” (Julian, 2015)
Normal curve can be used to test multivariate normality in simple regression. That is, a normal curve can be applied to determine the linearity between different (usually two) variables such as sales, production, and inventory, among others. This is accomplished by plotting values of one variable on vertical axis and values of the second variable on the horizontal axis.
Despite of the above techniques, Herbert Simon argued that we cannot maximize our benefits from a certain course of action. This is because we unable not only to assimilate, but also to synthesize all information needed to maximize the gains. We cannot also obtain all necessary information, and if we can, our minds are incapable to process the information appropriately. (Herbert, 2009)
Decision theory refers to the act of applying techniques to anticipate uncertainties or to aid in making of choices especially where there exist many outcomes. Applying decision theory is essential as it enables individuals to optimize on outcomes especially when there exist many outcomes. According to Chernoff & Moses (2012), individuals should avoid using decision theory especially if they are focusing on unknowns. However, should the need arise, individuals should use the various models to find a solution that is even and which is best chosen by all decision models.
We can apply different techniques in decision making trying to maximize our benefits. However, it is absolutely impossible to achieve such outcomes since we are bounded by our cognitive constraints. Therefore, we always end up making decisions that are good enough (satisfactory) for us regardless of what techniques we apply.
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M3Q1 Game Theory Forum
No unread replies. No replies.
In the Simon article he made the argument against normative theories that always assumed that Man made the optimal decision. He argued that since the optimal decision was assumed that these theorists did not have to assess how man really made decisions. Simon argued something we already knew — that Man did not always make optimal decisions. Simon argued that Game Theory, as a decision-making technique was a normative theory.
Does Coughlin agree with the normative theorists or with Simon’s viewpoint? Provide proof to support your position.
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M3Q2 – DELTA Forum
Evaluate your current company or if you are in transition, your last company in terms of the DELTA framework.

You should be able to assess where they are currently positioned using the DELTA framework. You should also be able to use the framework to suggest necessary upgrades for each of the five DELTA components.

Once you have made your initial assessment and then laid out a road map for improving your company’s analytic capabilities you should understand the reason why Davenport created his framework. Now that you have seen how the framework can be used, do you think this will be a valuable tool for you in the future?

If you do not have any career corporate experience yet- do this assignment with someone who has- and consider the same viewpoints.
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Drop box for your Regression Solution
This assignment was locked Feb 14 at 11:59pm.
Submit your Excel file here.

Make sure you have included your name in the excel file.

Your whole solution should be on the first page of the spreadsheet
R-squared is a statistical measure of how close the data are to the fitted regression line. It is also known as the coefficient of determination, or the coefficient of multiple determination for multiple regression.In this case, R^2 value is depicting that the error prediction percentage is high with respect to linear regression line as the points are not close with the regression line.In this field, R-squared values will be reasonable. R-squared values are higher than 50%. simply we can predict for Y the marginal increase in sales. Furthermore, R-squared values mean that there is a relationship between advertising budget and the marginal increase in sales.
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creat ExcelTable Solutions
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and the final Reflection Paper

reflection paper about business intelligence
Order Description
this paper is a part of my portfolio. the course is CIM 605 Business Intelligence , please write a paper reflection about what I did in class such as weekly group work and discussion to reach thes goals:1- Apply appropriate research methods to problems in information and communication and evaluate solutions to problems.2-Articulate the interrelatedness of rhetoric and information sciences, especially decision-making, human behavior in institutional settings, and the impact of advances in technology in communications.

there are what I did i the course

Forum 3: Making today’s business decisions.
1313
Making high-stakes business decisions has always been hard. Eric Bonabeau asserts that in recent decades, it has become tougher than ever. How does he support his assertion? Do you think the author provided sufficient evidence to support his assertion? Do you agree with his view point? Why or why not?

Remember this is a discussion forum and we will use this during class. You must participate and contribute to the discussion during class in a significant way by entering into a dialogue with several of your classmates in order to receive full credit.
———-
Forum 4: Intuition – Can You Trust Your Gut?
99
The choices facing managers and the data requiring analysis have multiplied even as the time for analyzing them has shrunk. One simple decision-making tool, human intuition, seems to offer a reliable alternative to painstaking fact gathering and analysis.

Eric Bonabeau references the book, Intuition at Work, by decision-making consultant, Gary Klein’s book. In the book Klein expresses the common wisdom that intuition is “at the center of the decision-making process” and that analysis is, at best, a supporting tool for making intuitive decisions. Encouraged by scientific research on intuition, many top managers when faced with complex decisions feel increasingly confident that they can just trust their gut. Bonabeau disagrees:

“The trust in intuition is understandable. But it’s also dangerous. Intuition has its place in decision
making–you should not ignore your instincts any more than you should ignore your conscience–
but anyone who thinks that intuition is a substitute for reason is indulging in a romantic delusion”.

So make a choice. Which view point do you favor? Present an argument supporting your view.
———
M1Q1 – The Second Road of Thought
11
Golsby-Smith states that Aristotle created two roads (paths) of thought: analytics and rhetoric. Each path was meant to address a different problem domain because the problem being addressed was different.

What type of problem does the analytic path produce? What type of solution does the rhetoric path produce? Golsby-Smith claims that western thought invested more heavily in the analytic path (i.e., the scientific approach). Why does he think that is a mistake? Do you agree or not?
——
M1Q2 – Competing on Analytics
11
Davenport is making an argument in favor of using analytics to support decision-making.Do you agree with his overall premise? Why or why not? What were some of his most persuasive arguments?
—–
M1Q3 – Harrah’s Casino
Why is Loveman’s marketing approach so different from the traditional marketing done by other casinos? What kind of proof of success did he offer?

How does Harrah’s reward their employees? Do you think this method is good for the casino? Is it good for the employees?

Hint: Pay special attention to his use of data.

——–
Drop box for your Module 1 Summary Report

Abstract
Data Analytics is defined as the science of examining data with the goal of discovering, modeling, and transforming useful information. Skilled professionals need to utilize from this process of data analyzing in order for them to make better decisions and thus make a better business.
Methods: First, company should develop a plan which is to determine what data is collected. Data analytics and presentation must find an adequate explanation for the development of solutions. It is must that verify the authenticity of the data recording and contradictions. it is supposed to choose the data collection method and adjusted based on user feedback to get to a convincing and successful analysis.
Results : Having the ability to use IT services along with focusing on business goals will not only provide us with the advantage of creating better communication with IT management, but also will provide the foundation for planning future services. In the article “The Importance of big Data Analytics in Business,” Wagner states that “The majority of CIOs believe the IT department can increase the value it delivers to the organization by improving cost measurement.”
Conclusion: data analytics reduces costs. Data analytics on an ongoing basis is good to help the survival of the company and access to meet the needs of customers. Data analytics helps to make the right decision quickly and to avoid risks of falling. Data analytics show us how to introduce new products or new services and stay in the progress on your competitors.

Data Analytics is defined as the science of examining data with the goal of discovering, modeling, and transforming useful information. Skilled professionals need to utilize from this process of data analyzing in order for them to make better decisions and thus make a better business.
Data analytics is useful in identifying trends and weaknesses. Data analytics also allow us to determine any conditions that can be utilized in making future decisions. Moreover, data analytics can draw out the relevant information so that it can be analyzed, then transformed and used in future decisions. However, we cannot use this process on large amount of data since traditional storage environment and processing times cannot be maintained.
In order to be successful in the work field, the majority of data is collected by IT, which shares data that is useful for the business. IT services is important to prove the value of IT to business. Having the ability to measure the business outcomes by using IT services is more important than measuring the costs. Having the ability to use IT services along with focusing on business goals will not only provide us with the advantage of creating better communication with IT management, but also will provide the foundation for planning future services. In the article “The Importance of big Data Analytics in Business,” Wagner states that “The majority of CIOs believe the IT department can increase the value it delivers to the organization by improving cost measurement.” (Dave Wagner, 2014)
Managing the big data that is trooping in many organizations every day has become a challenge to many companies. Even if the big data offers a chance for the tremendous growth, good data analytics is crucial in maximizing sales. For many years, McDonalds has stood as an American capitalism great success stories. However, the company has faced low sales in the last few years. McDonald’s has been experiencing low sales, and the company has been trying to go back to the track. According to the study by Ton (2014), the company’s problems started sprouting because of the operational mishaps, competition from various companies, for example, Burger King, Shake Shack among others. McDonald’s has been using the data analytics for some time now. For the years they’ve used the model, the company has not increased the sales. On the other hand, Wendy’s has been using the data analytics a tool that has worked for them even though they are also registering low sales.
In June 2015, the company was devastated because of the low sales and they decided to stop reporting their low financial numbers. However, Burger Chain continued reporting their sales and focusing on the long-term progress. Wendy’s are continuing to give their reports on the sales growth since their sales are highly rising. According to the report by Hill et al. (2016), the strategy McDonalds is using to keep their investors in the dark is not working and will not change the McDonald’s fortunes. McDonald’s data analytics failed because they were unable to meet the need for the speed, address the data quality, understand the data and work on the data to display meaningful results (Runkler, 2012).
McDonald’s introduced the data analytics tool to improve their customer care and the sales that had been dropping. The company implemented the software in all their operational areas, and they thought it will solve the statistically oriented issues. Unlike the McDonald’s, Wendy’s has been using the data analytics that has worked, but their sales have continued to fall. The data analytic software is helping them in analyzing the daily sales and labor costs. Some of the few elements that have made the company to succeed in their data analytics are that they replaced their MyMicros with their WebFOCUS. One of the biggest mistakes that the company has been making regarding the data analytics is the customer service. The annual customer service satisfaction shows that the company does not use the data analytics to upgrade on their customer care services (Runkler, 2012).
The data analytics has been working because it is often updated. According to Hills & Jones (2016), Wendy’s data analytics are refreshed six times daily that assist the managers to keep safely the metrics. Wendy’s data analytics did not work overnight but took a lot of time to practice and update.
Companies should use the data analytics for various reasons. According to the study that was conducted by Minelli, Chambers & Dhiraj (2013), the data analytics reduces the costs. The data analytics assist the companies to keep large amounts of data that assist them in reaching their customers and manage their sales. Secondly, the data analytics should be used to make fast decisions. Big companies review their data when making decisions that affect their company. A company that has highly succeeded in the use of the data analytics is Caesars in making faster decisions. Data analytics can also be used when introducing new products and services and staying ahead of the competitors.
To improve the quality and funding requirements, the company must develop a clear plan to data collection and analysis. First, it should develop a plan which is to determine what data is collected. It is important to confirm the quality of the data collected. The development of data is about known how the data will be collected.
Data analytics and presentation must find an adequate explanation for the development of solutions. It must that find an adequate explanation for the development of solutions by data analytics and presentation. To reach a suitable solution must verify the validity of the data. It is must that verify the authenticity of the data recording and contradictions. it is supposed to choose the data collection method and adjusted based on user feedback to get to a convincing and successful analysis.
If employees capacity can make good decision, the company do not have to conduct data analytics. There are employees understand the needs of customers. They have sufficient information to make appropriate decisions quickly. On addition, they know the customer tastes.
Companies with a culture of evidence-based decision making see to it that business rules are continually assessed and improved by articulating them clearly and ensuring consistency across the company ( Jeanne, 2013). There is evidence explicit and clear need to evaluate and improve only. On the other hand, do not need to do data analytics. Sometimes there are clear expectations for modification and development. In addition, there is a clear plan to improve the performance of each individual.
Data analytic is important to fix any problem in company. In addition, decision-makers have to focus on specific data which is relevant to make a good decision. It is important to search about right information which allowed seeing the problem, and tells there is a problem or there are chances to improve or to be unique. Also, updating information as fast as possible is assist to get data which gives a good indication to take a successful decision.
When making the decision, it must consider the implications of this decision from all respects. While decision-maker collected data to make a perfect decision, they have to make sure that decision make the company in advance for the future, not only so far because it is important to avoid future obstacles.
When considering the number of times a repeat purchase, it means a lot. From this point it can be troubleshooting and the reason for the lack of sales. If the sales go down in some product that means it is not good and maker-decision have to know there are problems. On the other hand, they try to found it. It needs to be amended or add something to be unique. By this data must discover what is problem and try to solve it.
Finally, all company should use data analytics because it is a better way to found problem and fix it faster. Company should update the data analytics on a daily basis to get to the best and fastest results. This is one of the most important things you must found within your company to not only understand your business, but to drive your business forward and keep it unique.
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M2Q1 – Data Warehouses and Data Mining
Jerzy Surma’s description of a data warehouse is a fairly technical paper. It is geared toward technical practitioners. Notice the difference in tone between his paper and mine (see my description of a data warehouse). Which do you think is more appropriate for a CIM student?

Take a moment to reflecting back on the Harrah’s article from Module 1. How did they use their data warehouse to shape their business strategy?
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Drop Box for Module 2 Summary Report

Data Warehousing, Data Mining, Normal Curve and Simple Regression and Herb Simon
Abstract: Decision makers can employ a variety of techniques such as data warehousing, normal curve and simple regression to aid them in their decision making processes. Although such techniques are based on a common assumption that people are always seeking to maximize their gains, Herbert Simon’s theory of bounded rationality argues otherwise.
Data mining equips individuals who seek to use it with appropriate information for decisions making. Zaki & Meira (2014) also advises individuals to be careful especially when using the concept in anticipating unpredictable outcomes such as the level of societal changes that are likely to occur on a specific aspect such as technology. Predicting the future may not turn out as people anticipate. Warehousing should be done based on the need of the organizations to increase access to their centers and minimize reliance on other institutions facilities. Linear Regression is often applied in the development of the trend line as it accounts for variables data components at the same time. Linear regression should often be done when there exists data which offers a pattern that is almost similar and this will allow an individual to choose the best fit (Seber & Lee, 2003).
According to Chernoff & Moses (2012), individuals should avoid using decision theory especially if they are focusing on unknowns. However, should the need arise; individuals should use the various models to find a solution that is even and which is best chosen by all decision models.
We can apply different techniques in decision making trying to maximize our benefits. However, it is absolutely impossible to achieve such outcomes since we are bounded by our cognitive constraints. Therefore, we always end up making decisions that are good enough (satisfactory) for us regardless of what techniques we apply.
Decision making involves numerous risks, which may lead to undesirable outcomes. However, decision makers can employ a variety of techniques such as data warehousing, normal curve and simple regression to aid them in their decision making processes. Although such techniques are based on a common assumption that people are always seeking to maximize their gains, Herbert Simon’s theory of bounded rationality argues otherwise.
”Data warehouses have had staying power because the concept of a central data repository—fed by dozens or hundreds of databases, applications, and other source systems—continues to be the best, most efficient way for companies to get an enterprise-wide view of their customers, supply chains, sales, and operations.”(John, 2014)
Data warehouse combines data from multiple data sources and it can be used to analysis in a particular subject area such as sales. Data warehouses are designed to assist how analyze data. By using Data warehouses, you will learn more about your company’s sales data, you can know more about your customer for this item last year, and you can makes the data warehouse subject oriented. Data warehouse help in support of management’s decision making process.
Data warehouse can keep Historical data. For example, data warehouse can keep all information about customer without any change. So, it should never change historical data in the data warehouse because it is for query and analysis. It has interesting advantages which is copied data, processed, integrated, annotated, summarized and restructured in semantic data store in advance, and provide high performance.
Data warehousing is an effective platform for inquiry and analysis of workload data, but not for transaction processing. However, historical data obtain from transaction information is included in data warehousing, although other sources can also acts as sources of data. Thus, data warehousing separates transaction workload from analysis workload, and helps decision makers to combine data obtained from different sources.
Warehousing is simply a concept of storage and in the modern world, it has transformed from the storage of materials and products in large houses to storage of data in servers. Warehousing should be done based on the need of the organizations to increase access to their centers and minimize reliance on other institutions facilities. However, according to Ackerman (2013), institutions should avoid warehousing especially if they cannot afford the price of facilities and can rely on outsourcing. Today, warehousing can be done by choosing the appropriate centers where such facilities can be located.
”For the most part, data mining tells us about very large and complex data sets, the kinds of information that would be readily apparent about small and simple things. For example, it can tell us that “one of these things is not like the other” a la Sesame Street or it can show us categories and then sort things into pre-determined categories. But what’s simple with 5 datapoints is not so simple with 5 billion datapoints.”(Alexander, 2012)
Data mining is a subsection multitasking of computer science. It is a process used by companies to convert data into useful information. Key points of the process of data mining are to extract the data and turn it to information concepts to be used. It means that the extraction of knowledge from large amounts of data. It is usually applied to a form of data or information processing (collection, extraction, warehousing, analysis, and statistics) as well as business intelligence. The expression data mining was added for marketing reasons. The data mining determine multiple groups in the data, and then can be used to get results more accurate prediction through the use of a system of decision making.
According to Leskovec, Rajaraman & Ullman (2014), data mining refers to the practice of identifying data patterns and information which may be used by both individuals and businesses. Data mining is often unique as it equips individuals who seek to use it with appropriate information for decisions making. This is why Zaki & Meira (2014) recommends data mining to be done in anticipating predictable patterns.
Data mining have main functions such as automatic discovery of patterns, to predict possible outcomes, establish process information, focusing on a wide range of data and data mining can answer questions and reporting techniques.
Data mining is a good way to assistance you find patterns and relationships within your data. On the other hand, data mining cannot work by itself. In this case, it important to know your business, understanding the data, and understanding analytical methods to analysis data. Data mining reveal hidden information. On the other hand, it cannot evaluate the company if is success or not.
In the modern world, individuals can apply the concept by using technology which can monitor the aspects they would like to review. Despite all the benefits that accrue from using it, Zaki & Meira (2014) also advises individuals to be careful especially when using the concept in anticipating unpredictable outcomes such as the level of societal changes that are likely to occur on a specific aspect such as technology. Predicting the future may not turn out as people anticipate.
” Linear regression is the most basic and commonly used predictive analysis. Regression estimates are used to describe data and to explain the relationship between one dependent variable and one or more independent variables.” (2013)
There are other names for dependent variable such as a criterion variable, endogenous variable, prognostic variable, or regressand. Furthermore, there are other names for independent variables such as exogenous variables, predictor variables or regressors.
There are three important reasons to use regression analysis. Firstly, linear regression could be used to find out the strength of the effect that the independent variable(s) have on a dependent variable. It reveals that what the strength of relationship between sales and marketing spend. Secondly, linear regression could be used to predict, and this is regression analysis what happens when changing one or more independent variables. Thirdly, linear regression could be used to analysis predicts trends and future price for products.

We can make different key assumptions such as linear relationship, multivariate normality, no auto-correlation, homoscedasticity, and no or little multicollineraity from simple regression. However, in order to make such assumptions, simple regression must have minimum of two metric variables such as interval or ratio. Simple regression can help to determine correlation between different (dependent and independent) variables, which may serve as the source of data.
Linear Regression is a model used in establishing a relationship between one or more variables. The model is often applied as it assists in choosing a solution that cuts across all the outcomes. The model is often applied in the development of the trend line as it accounts for variables data components at the same time. Linear regression should often be done when there exists data which offers a pattern that is almost similar and this will allow an individual to choose the best fit (Seber & Lee, 2003).
”The Normal Distribution is actually created by the researcher. It is common scientific practice to successively isolate and control for all important explanatory variables, each of which causes the original distribution not to resemble the Normal. The appearance of the Normal Distribution only indicates that most of the major explanatory variables have been allowed for, and the rest of the many relevant variables have small influence—the point at which the scientist ends his labors. This is what the Normal Curve “means.” ” (Julian, 2015)
Normal curve can be used to test multivariate normality in simple regression. That is, a normal curve can be applied to determine the linearity between different (usually two) variables such as sales, production, and inventory, among others. This is accomplished by plotting values of one variable on vertical axis and values of the second variable on the horizontal axis.
Despite of the above techniques, Herbert Simon argued that we cannot maximize our benefits from a certain course of action. This is because we unable not only to assimilate, but also to synthesize all information needed to maximize the gains. We cannot also obtain all necessary information, and if we can, our minds are incapable to process the information appropriately. (Herbert, 2009)
Decision theory refers to the act of applying techniques to anticipate uncertainties or to aid in making of choices especially where there exist many outcomes. Applying decision theory is essential as it enables individuals to optimize on outcomes especially when there exist many outcomes. According to Chernoff & Moses (2012), individuals should avoid using decision theory especially if they are focusing on unknowns. However, should the need arise, individuals should use the various models to find a solution that is even and which is best chosen by all decision models.
We can apply different techniques in decision making trying to maximize our benefits. However, it is absolutely impossible to achieve such outcomes since we are bounded by our cognitive constraints. Therefore, we always end up making decisions that are good enough (satisfactory) for us regardless of what techniques we apply.
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M3Q1 Game Theory Forum
No unread replies. No replies.
In the Simon article he made the argument against normative theories that always assumed that Man made the optimal decision. He argued that since the optimal decision was assumed that these theorists did not have to assess how man really made decisions. Simon argued something we already knew — that Man did not always make optimal decisions. Simon argued that Game Theory, as a decision-making technique was a normative theory.
Does Coughlin agree with the normative theorists or with Simon’s viewp01oint? Provide proof to support your position.
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M3Q2 – DELTA Forum
Evaluate your current company or if you are in transition, your last company in terms of the DELTA framework.

You should be able to assess where they are currently positioned using the DELTA framework. You should also be able to use the framework to suggest necessary upgrades for each of the five DELTA components.

Once you have made your initial assessment and then laid out a road map for improving your company’s analytic capabilities you should understand the reason why Davenport created his framework. Now that you have seen how the framework can be used, do you think this will be a valuable tool for you in the future?

If you do not have any career corporate experience yet- do this assignment with someone who has- and consider the same viewp01oints.
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Drop box for your Regression Solution
This assignment was locked Feb 14 at 11:59pm.
Submit your Excel file here.

Make sure you have included your name in the excel file.

Your whole solution should be on the first page of the spreadsheet
R-squared is a statistical measure of how close the data are to the fitted regression line. It is also known as the coefficient of determination, or the coefficient of multiple determination for multiple regression.In this case, R^2 value is depicting that the error prediction percentage is high with respect to linear regression line as the points are not close with the regression line.In this field, R-squared values will be reasonable. R-squared values are higher than 50%. simply we can predict for Y the marginal increase in sales. Furthermore, R-squared values mean that there is a relationship between advertising budget and the marginal increase in sales.
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creat ExcelTable Solutions
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and the final Reflection Paper
Order Description
this paper is a part of my portfolio. the course is CIM 605 Business Intelligence , please write a paper reflection about what I did in class such as weekly group work and discussion to reach thes goals:1- Apply appropriate research methods to problems in information and communication and evaluate solutions to problems.2-Articulate the interrelatedness of rhetoric and information sciences, especially decision-making, human behavior in institutional settings, and the impact of advances in technology in communications.

there are what I did i the course

Forum 3: Making today’s business decisions.
1313
Making high-stakes business decisions has always been hard. Eric Bonabeau asserts that in recent decades, it has become tougher than ever. How does he support his assertion? Do you think the author provided sufficient evidence to support his assertion? Do you agree with his view point? Why or why not?

Remember this is a discussion forum and we will use this during class. You must participate and contribute to the discussion during class in a significant way by entering into a dialogue with several of your classmates in order to receive full credit.
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Forum 4: Intuition – Can You Trust Your Gut?
99
The choices facing managers and the data requiring analysis have multiplied even as the time for analyzing them has shrunk. One simple decision-making tool, human intuition, seems to offer a reliable alternative to painstaking fact gathering and analysis.

Eric Bonabeau references the book, Intuition at Work, by decision-making consultant, Gary Klein’s book. In the book Klein expresses the common wisdom that intuition is “at the center of the decision-making process” and that analysis is, at best, a supporting tool for making intuitive decisions. Encouraged by scientific research on intuition, many top managers when faced with complex decisions feel increasingly confident that they can just trust their gut. Bonabeau disagrees:

“The trust in intuition is understandable. But it’s also dangerous. Intuition has its place in decision
making–you should not ignore your instincts any more than you should ignore your conscience–
but anyone who thinks that intuition is a substitute for reason is indulging in a romantic delusion”.

So make a choice. Which view point do you favor? Present an argument supporting your view.
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M1Q1 – The Second Road of Thought
11
Golsby-Smith states that Aristotle created two roads (paths) of thought: analytics and rhetoric. Each path was meant to address a different problem domain because the problem being addressed was different.

What type of problem does the analytic path produce? What type of solution does the rhetoric path produce? Golsby-Smith claims that western thought invested more heavily in the analytic path (i.e., the scientific approach). Why does he think that is a mistake? Do you agree or not?
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M1Q2 – Competing on Analytics
11
Davenport is making an argument in favor of using analytics to support decision-making.Do you agree with his overall premise? Why or why not? What were some of his most persuasive arguments?
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M1Q3 – Harrah’s Casino
Why is Loveman’s marketing approach so different from the traditional marketing done by other casinos? What kind of proof of success did he offer?

How does Harrah’s reward their employees? Do you think this method is good for the casino? Is it good for the employees?

Hint: Pay special attention to his use of data.

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Drop box for your Module 1 Summary Report

Abstract
Data Analytics is defined as the science of examining data with the goal of discovering, modeling, and transforming useful information. Skilled professionals need to utilize from this process of data analyzing in order for them to make better decisions and thus make a better business.
Methods: First, company should develop a plan which is to determine what data is collected. Data analytics and presentation must find an adequate explanation for the development of solutions. It is must that verify the authenticity of the data recording and contradictions. it is supposed to choose the data collection method and adjusted based on user feedback to get to a convincing and successful analysis.
Results : Having the ability to use IT services along with focusing on business goals will not only provide us with the advantage of creating better communication with IT management, but also will provide the foundation for planning future services. In the article “The Importance of big Data Analytics in Business,” Wagner states that “The majority of CIOs believe the IT department can increase the value it delivers to the organization by improving cost measurement.”
Conclusion: data analytics reduces costs. Data analytics on an ongoing basis is good to help the survival of the company and access to meet the needs of customers. Data analytics helps to make the right decision quickly and to avoid risks of falling. Data analytics show us how to introduce new products or new services and stay in the progress on your competitors.

Data Analytics is defined as the science of examining data with the goal of discovering, modeling, and transforming useful information. Skilled professionals need to utilize from this process of data analyzing in order for them to make better decisions and thus make a better business.
Data analytics is useful in identifying trends and weaknesses. Data analytics also allow us to determine any conditions that can be utilized in making future decisions. Moreover, data analytics can draw out the relevant information so that it can be analyzed, then transformed and used in future decisions. However, we cannot use this process on large amount of data since traditional storage environment and processing times cannot be maintained.
In order to be successful in the work field, the majority of data is collected by IT, which shares data that is useful for the business. IT services is important to prove the value of IT to business. Having the ability to measure the business outcomes by using IT services is more important than measuring the costs. Having the ability to use IT services along with focusing on business goals will not only provide us with the advantage of creating better communication with IT management, but also will provide the foundation for planning future services. In the article “The Importance of big Data Analytics in Business,” Wagner states that “The majority of CIOs believe the IT department can increase the value it delivers to the organization by improving cost measurement.” (Dave Wagner, 2014)
Managing the big data that is trooping in many organizations every day has become a challenge to many companies. Even if the big data offers a chance for the tremendous growth, good data analytics is crucial in maximizing sales. For many years, McDonalds has stood as an American capitalism great success stories. However, the company has faced low sales in the last few years. McDonald’s has been experiencing low sales, and the company has been trying to go back to the track. According to the study by Ton (2014), the company’s problems started sprouting because of the operational mishaps, competition from various companies, for example, Burger King, Shake Shack among others. McDonald’s has been using the data analytics for some time now. For the years they’ve used the model, the company has not increased the sales. On the other hand, Wendy’s has been using the data analytics a tool that has worked for them even though they are also registering low sales.
In June 2015, the company was devastated because of the low sales and they decided to stop reporting their low financial numbers. However, Burger Chain continued reporting their sales and focusing on the long-term progress. Wendy’s are continuing to give their reports on the sales growth since their sales are highly rising. According to the report by Hill et al. (2016), the strategy McDonalds is using to keep their investors in the dark is not working and will not change the McDonald’s fortunes. McDonald’s data analytics failed because they were unable to meet the need for the speed, address the data quality, understand the data and work on the data to display meaningful results (Runkler, 2012).
McDonald’s introduced the data analytics tool to improve their customer care and the sales that had been dropping. The company implemented the software in all their operational areas, and they thought it will solve the statistically oriented issues. Unlike the McDonald’s, Wendy’s has been using the data analytics that has worked, but their sales have continued to fall. The data analytic software is helping them in analyzing the daily sales and labor costs. Some of the few elements that have made the company to succeed in their data analytics are that they replaced their MyMicros with their WebFOCUS. One of the biggest mistakes that the company has been making regarding the data analytics is the customer service. The annual customer service satisfaction shows that the company does not use the data analytics to upgrade on their customer care services (Runkler, 2012).
The data analytics has been working because it is often updated. According to Hills & Jones (2016), Wendy’s data analytics are refreshed six times daily that assist the managers to keep safely the metrics. Wendy’s data analytics did not work overnight but took a lot of time to practice and update.
Companies should use the data analytics for various reasons. According to the study that was conducted by Minelli, Chambers & Dhiraj (2013), the data analytics reduces the costs. The data analytics assist the companies to keep large amounts of data that assist them in reaching their customers and manage their sales. Secondly, the data analytics should be used to make fast decisions. Big companies review their data when making decisions that affect their company. A company that has highly succeeded in the use of the data analytics is Caesars in making faster decisions. Data analytics can also be used when introducing new products and services and staying ahead of the competitors.
To improve the quality and funding requirements, the company must develop a clear plan to data collection and analysis. First, it should develop a plan which is to determine what data is collected. It is important to confirm the quality of the data collected. The development of data is about known how the data will be collected.
Data analytics and presentation must find an adequate explanation for the development of solutions. It must that find an adequate explanation for the development of solutions by data analytics and presentation. To reach a suitable solution must verify the validity of the data. It is must that verify the authenticity of the data recording and contradictions. it is supposed to choose the data collection method and adjusted based on user feedback to get to a convincing and successful analysis.
If employees capacity can make good decision, the company do not have to conduct data analytics. There are employees understand the needs of customers. They have sufficient information to make appropriate decisions quickly. On addition, they know the customer tastes.
Companies with a culture of evidence-based decision making see to it that business rules are continually assessed and improved by articulating them clearly and ensuring consistency across the company ( Jeanne, 2013). There is evidence explicit and clear need to evaluate and improve only. On the other hand, do not need to do data analytics. Sometimes there are clear expectations for modification and development. In addition, there is a clear plan to improve the performance of each individual.
Data analytic is important to fix any problem in company. In addition, decision-makers have to focus on specific data which is relevant to make a good decision. It is important to search about right information which allowed seeing the problem, and tells there is a problem or there are chances to improve or to be unique. Also, updating information as fast as possible is assist to get data which gives a good indication to take a successful decision.
When making the decision, it must consider the implications of this decision from all respects. While decision-maker collected data to make a perfect decision, they have to make sure that decision make the company in advance for the future, not only so far because it is important to avoid future obstacles.
When considering the number of times a repeat purchase, it means a lot. From this point it can be troubleshooting and the reason for the lack of sales. If the sales go down in some product that means it is not good and maker-decision have to know there are problems. On the other hand, they try to found it. It needs to be amended or add something to be unique. By this data must discover what is problem and try to solve it.
Finally, all company should use data analytics because it is a better way to found problem and fix it faster. Company should update the data analytics on a daily basis to get to the best and fastest results. This is one of the most important things you must found within your company to not only understand your business, but to drive your business forward and keep it unique.
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M2Q1 – Data Warehouses and Data Mining
Jerzy Surma’s description of a data warehouse is a fairly technical paper. It is geared toward technical practitioners. Notice the difference in tone between his paper and mine (see my description of a data warehouse). Which do you think is more appropriate for a CIM student?

Take a moment to reflecting back on the Harrah’s article from Module 1. How did they use their data warehouse to shape their business strategy?
———–
Drop Box for Module 2 Summary Report

Data Warehousing, Data Mining, Normal Curve and Simple Regression and Herb Simon
Abstract: Decision makers can employ a variety of techniques such as data warehousing, normal curve and simple regression to aid them in their decision making processes. Although such techniques are based on a common assumption that people are always seeking to maximize their gains, Herbert Simon’s theory of bounded rationality argues otherwise.
Data mining equips individuals who seek to use it with appropriate information for decisions making. Zaki & Meira (2014) also advises individuals to be careful especially when using the concept in anticipating unpredictable outcomes such as the level of societal changes that are likely to occur on a specific aspect such as technology. Predicting the future may not turn out as people anticipate. Warehousing should be done based on the need of the organizations to increase access to their centers and minimize reliance on other institutions facilities. Linear Regression is often applied in the development of the trend line as it accounts for variables data components at the same time. Linear regression should often be done when there exists data which offers a pattern that is almost similar and this will allow an individual to choose the best fit (Seber & Lee, 2003).
According to Chernoff & Moses (2012), individuals should avoid using decision theory especially if they are focusing on unknowns. However, should the need arise; individuals should use the various models to find a solution that is even and which is best chosen by all decision models.
We can apply different techniques in decision making trying to maximize our benefits. However, it is absolutely impossible to achieve such outcomes since we are bounded by our cognitive constraints. Therefore, we always end up making decisions that are good enough (satisfactory) for us regardless of what techniques we apply.
Decision making involves numerous risks, which may lead to undesirable outcomes. However, decision makers can employ a variety of techniques such as data warehousing, normal curve and simple regression to aid them in their decision making processes. Although such techniques are based on a common assumption that people are always seeking to maximize their gains, Herbert Simon’s theory of bounded rationality argues otherwise.
”Data warehouses have had staying power because the concept of a central data repository—fed by dozens or hundreds of databases, applications, and other source systems—continues to be the best, most efficient way for companies to get an enterprise-wide view of their customers, supply chains, sales, and operations.”(John, 2014)
Data warehouse combines data from multiple data sources and it can be used to analysis in a particular subject area such as sales. Data warehouses are designed to assist how analyze data. By using Data warehouses, you will learn more about your company’s sales data, you can know more about your customer for this item last year, and you can makes the data warehouse subject oriented. Data warehouse help in support of management’s decision making process.
Data warehouse can keep Historical data. For example, data warehouse can keep all information about customer without any change. So, it should never change historical data in the data warehouse because it is for query and analysis. It has interesting advantages which is copied data, processed, integrated, annotated, summarized and restructured in semantic data store in advance, and provide high performance.
Data warehousing is an effective platform for inquiry and analysis of workload data, but not for transaction processing. However, historical data obtain from transaction information is included in data warehousing, although other sources can also acts as sources of data. Thus, data warehousing separates transaction workload from analysis workload, and helps decision makers to combine data obtained from different sources.
Warehousing is simply a concept of storage and in the modern world, it has transformed from the storage of materials and products in large houses to storage of data in servers. Warehousing should be done based on the need of the organizations to increase access to their centers and minimize reliance on other institutions facilities. However, according to Ackerman (2013), institutions should avoid warehousing especially if they cannot afford the price of facilities and can rely on outsourcing. Today, warehousing can be done by choosing the appropriate centers where such facilities can be located.
”For the most part, data mining tells us about very large and complex data sets, the kinds of information that would be readily apparent about small and simple things. For example, it can tell us that “one of these things is not like the other” a la Sesame Street or it can show us categories and then sort things into pre-determined categories. But what’s simple with 5 datapoints is not so simple with 5 billion datapoints.”(Alexander, 2012)
Data mining is a subsection multitasking of computer science. It is a process used by companies to convert data into useful information. Key points of the process of data mining are to extract the data and turn it to information concepts to be used. It means that the extraction of knowledge from large amounts of data. It is usually applied to a form of data or information processing (collection, extraction, warehousing, analysis, and statistics) as well as business intelligence. The expression data mining was added for marketing reasons. The data mining determine multiple groups in the data, and then can be used to get results more accurate prediction through the use of a system of decision making.
According to Leskovec, Rajaraman & Ullman (2014), data mining refers to the practice of identifying data patterns and information which may be used by both individuals and businesses. Data mining is often unique as it equips individuals who seek to use it with appropriate information for decisions making. This is why Zaki & Meira (2014) recommends data mining to be done in anticipating predictable patterns.
Data mining have main functions such as automatic discovery of patterns, to predict possible outcomes, establish process information, focusing on a wide range of data and data mining can answer questions and reporting techniques.
Data mining is a good way to assistance you find patterns and relationships within your data. On the other hand, data mining cannot work by itself. In this case, it important to know your business, understanding the data, and understanding analytical methods to analysis data. Data mining reveal hidden information. On the other hand, it cannot evaluate the company if is success or not.
In the modern world, individuals can apply the concept by using technology which can monitor the aspects they would like to review. Despite all the benefits that accrue from using it, Zaki & Meira (2014) also advises individuals to be careful especially when using the concept in anticipating unpredictable outcomes such as the level of societal changes that are likely to occur on a specific aspect such as technology. Predicting the future may not turn out as people anticipate.
” Linear regression is the most basic and commonly used predictive analysis. Regression estimates are used to describe data and to explain the relationship between one dependent variable and one or more independent variables.” (2013)
There are other names for dependent variable such as a criterion variable, endogenous variable, prognostic variable, or regressand. Furthermore, there are other names for independent variables such as exogenous variables, predictor variables or regressors.
There are three important reasons to use regression analysis. Firstly, linear regression could be used to find out the strength of the effect that the independent variable(s) have on a dependent variable. It reveals that what the strength of relationship between sales and marketing spend. Secondly, linear regression could be used to predict, and this is regression analysis what happens when changing one or more independent variables. Thirdly, linear regression could be used to analysis predicts trends and future price for products.

We can make different key assumptions such as linear relationship, multivariate normality, no auto-correlation, homoscedasticity, and no or little multicollineraity from simple regression. However, in order to make such assumptions, simple regression must have minimum of two metric variables such as interval or ratio. Simple regression can help to determine correlation between different (dependent and independent) variables, which may serve as the source of data.
Linear Regression is a model used in establishing a relationship between one or more variables. The model is often applied as it assists in choosing a solution that cuts across all the outcomes. The model is often applied in the development of the trend line as it accounts for variables data components at the same time. Linear regression should often be done when there exists data which offers a pattern that is almost similar and this will allow an individual to choose the best fit (Seber & Lee, 2003).
”The Normal Distribution is actually created by the researcher. It is common scientific practice to successively isolate and control for all important explanatory variables, each of which causes the original distribution not to resemble the Normal. The appearance of the Normal Distribution only indicates that most of the major explanatory variables have been allowed for, and the rest of the many relevant variables have small influence—the point at which the scientist ends his labors. This is what the Normal Curve “means.” ” (Julian, 2015)
Normal curve can be used to test multivariate normality in simple regression. That is, a normal curve can be applied to determine the linearity between different (usually two) variables such as sales, production, and inventory, among others. This is accomplished by plotting values of one variable on vertical axis and values of the second variable on the horizontal axis.
Despite of the above techniques, Herbert Simon argued that we cannot maximize our benefits from a certain course of action. This is because we unable not only to assimilate, but also to synthesize all information needed to maximize the gains. We cannot also obtain all necessary information, and if we can, our minds are incapable to process the information appropriately. (Herbert, 2009)
Decision theory refers to the act of applying techniques to anticipate uncertainties or to aid in making of choices especially where there exist many outcomes. Applying decision theory is essential as it enables individuals to optimize on outcomes especially when there exist many outcomes. According to Chernoff & Moses (2012), individuals should avoid using decision theory especially if they are focusing on unknowns. However, should the need arise, individuals should use the various models to find a solution that is even and which is best chosen by all decision models.
We can apply different techniques in decision making trying to maximize our benefits. However, it is absolutely impossible to achieve such outcomes since we are bounded by our cognitive constraints. Therefore, we always end up making decisions that are good enough (satisfactory) for us regardless of what techniques we apply.
——————-
M3Q1 Game Theory Forum
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In the Simon article he made the argument against normative theories that always assumed that Man made the optimal decision. He argued that since the optimal decision was assumed that these theorists did not have to assess how man really made decisions. Simon argued something we already knew — that Man did not always make optimal decisions. Simon argued that Game Theory, as a decision-making technique was a normative theory.
Does Coughlin agree with the normative theorists or with Simon’s viewp01oint? Provide proof to support your position.
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M3Q2 – DELTA Forum
Evaluate your current company or if you are in transition, your last company in terms of the DELTA framework.

You should be able to assess where they are currently positioned using the DELTA framework. You should also be able to use the framework to suggest necessary upgrades for each of the five DELTA components.

Once you have made your initial assessment and then laid out a road map for improving your company’s analytic capabilities you should understand the reason why Davenport created his framework. Now that you have seen how the framework can be used, do you think this will be a valuable tool for you in the future?

If you do not have any career corporate experience yet- do this assignment with someone who has- and consider the same viewp01oints.
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Drop box for your Regression Solution
This assignment was locked Feb 14 at 11:59pm.
Submit your Excel file here.

Make sure you have included your name in the excel file.

Your whole solution should be on the first page of the spreadsheet
R-squared is a statistical measure of how close the data are to the fitted regression line. It is also known as the coefficient of determination, or the coefficient of multiple determination for multiple regression.In this case, R^2 value is depicting that the error prediction percentage is high with respect to linear regression line as the points are not close with the regression line.In this field, R-squared values will be reasonable. R-squared values are higher than 50%. simply we can predict for Y the marginal increase in sales. Furthermore, R-squared values mean that there is a relationship between advertising budget and the marginal increase in sales.
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creat ExcelTable Solutions
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and the final Reflection Paper