Week 7 and Week 8 Discussion
Brian34
FinalOriginalityReport.pdf
4/18/2020 Originality Report
https://ucumberlands.blackboard.com/webapps/mdb-sa-BB5a31b16bb2c48/originalityReport/ultra?attemptId=40488198-db07-4fb3-8984-c92a4e917c… 1/6
%100
SafeAssign Originality Report Spring 2020 - InfoTech Import in Strat Plan (ITS-831-52) (ITS-831-53) -… • Final Project
%100Total Score: High riskRupesh Pasam Submission UUID: cc841a1a-97ca-3bc7-8acb-a172035946d1
Total Number of Reports
1 Highest Match
100 % FINAL PORTFOLIO PROJECT.docx
Average Match
100 % Submitted on
04/18/20 10:56 PM PDT
Average Word Count
1,916 Highest: FINAL PORTFOLIO PROJECT.docx
%100Attachment 1
Institutional database (4)
Student paper Student paper Student paper
Student paper
Top sources (3)
Excluded sources (0)
View Originality Report - Old Design
Word Count: 1,916 FINAL PORTFOLIO PROJECT.docx
1 4 2
3
1 Student paper 4 Student paper 2 Student paper
Running head: FINAL PORTFOLIO PROJECT 1
FINAL PORTFOLIO PROJECT 5
FINAL PORTFOLIO PROJECT
Rupesh Pasam
ITS-831-53 InfoTech Importance in Strategic Planning
University of the Cumberlands
Dr. Eric Hollis
April 18, 2020
Abstract
Most organizations today rely on knowledge-based management systems. Nevertheless, these systems derive knowledge from big data analysis. Data
warehouses are the core components of knowledge-management systems. The primary purpose of building a data warehouse is to integrate multiple, independent, and distributed data sources within an organization. The historical data is used for analysis to support business decisions at all levels ranging from strategic planning to performance evaluation of a discrete organizational unit. All these components are characterized by high volumes of data and data flows that require continuous analysis and mining. These applications, therefore, require data warehousing and analysis. It also provides a platform for advance and sophisticated data analysis. The fact that big data deals with large amounts of the data, it is still able to manage and uncover hidden patterns and correlations that may be present. With big data, it is possible to analyze enormous amounts of data and get the outcome from it instantly –something that was not possible with the traditional data handling methods.
Final Portfolio Project
We live in a contemporary world where technology is fast outpacing our ideologies. Today, we see business intelligence applications in electronic commerce, telecommunications, and other industries. These applications, therefore, require data warehousing and analysis. As such, this paper provides a detailed analysis of data warehousing and its main components, not forgetting its modern trends. All these components are characterized by high volumes of data and data flows that require continuous analysis and mining. It also provides a summary analysis of big data and, lastly, discusses the green computing technology. Data Warehouse Database
Data warehouse alludes to a data framework that involves recorded and commutative information from single or various sources. In simplifying, it plays a vital
part in the reporting and analysis processes of an organization In other words it is a database containing data that usually represent the business history of an
1
2
3
1
1
4/18/2020 Originality Report
https://ucumberlands.blackboard.com/webapps/mdb-sa-BB5a31b16bb2c48/originalityReport/ultra?attemptId=40488198-db07-4fb3-8984-c92a4e917c… 2/6
part in the reporting and analysis processes of an organization. In other words, it is a database containing data that usually represent the business history of an organization. The data warehouse depends on some key components that make a professional workplace to be utilitarian, reasonable, and available. In online analytical processing, to analyze the data in the data warehouses complex queries are utilized as opposed to handling the exchanges. In the data warehouse,
there are five main components, and they include; Acquisition, Sourcing, Clean-up, Metadata, Transformational Tools (ETL), Query Tools, Data Warehouse Database, and Data warehouse Bus Architecture (Santoso, 2017). This is the foundation of the data warehousing environment and is implemented on the RDBMS technology. For consideration of scalability, it is deployed in corresponding. The database additionally accommodates shared memory on different multiprocessor designs. The reports produced using complex queries inside an information dissemination center are used to shape business decisions. Sourcing, Acquisition, Clean-up and
Transformational Tools (ETL) ETL refers to a data integration function that involves the extraction of data from the source, assuring quality, and data cleaning to deliver data in a physical format that can be useful for further reference. Elimination of outdated or unwanted data in operational databases from stacking into the data warehouse is the essential function of the ETL. According to Santoso (2017), the organization uses the ETL technology to read data from the outside source, clean it up, as well as format it uniformly to load it into a target data warehouse. Metadata
Metadata, as the name suggests, refers to data that defines the data warehouse. The data is utilized to maintain, build just as deal with the data warehouse.
Metadata assumes a vital role in the Data Warehouse architecture as it indicates the features of data warehouse data, usage values, and source. Santos,
Martinho, and Costa (2017) also tell us that Metadata is closely linked to the data warehouse and hence facilitates how data is changed and processed. Therefore, Metadata can be said to be a vital component in the transformation of data into knowledge. Query Tools
As communicated previously, one of the principal goals of data warehousing is to give valuable data to associations to make strategic decisions. To interact with
the data warehouse system, query tools in this way gives an effective platform for the users. The devices referenced here are additionally isolated into four classes, to be specific, data mining tools, Query and reporting tools, application and development tools, as well as OLAP tools. Every one of these tools is essential in permitting clients to connect with the information stockroom framework. To interact with the data warehouse system, every one of these tools is essential in allowing the
users.
1
1
1
1
1
1
Data Warehouse Bus Architecture How data streams into a data warehouse is decided by this component. The stream can be classified as either inflow, outflow,
Meta flow, or upstream. In data marts, an IT manager must think about all facts as well as shared dimensions to design a data warehouse bus. Data marts refer to access layers that are typically used to process user data to the users (Santos et al., 2017). Current Key trends in data warehousing
The modern world generates, uses, as well as retains useful data for future usage. Since the global world is projected to continue to grow for the foreseeable
future, it is approximated by 2026 that the world will generate and replicate 165ZB of data. This will arise as a result of increased use of computers in doing business; hence data will need to be instantly available whenever required. Since Data warehousing solutions came into play, most big companies such as Google BigQuery, Amazon, Panoply, and Redshift have all adopted the use of this tool to manage their data. These organizations manage partitioning as well as the scalability of a data warehouse in a transparent manner. Data warehousing has made it possible for enterprises to set up a petabyte-scale to hold up all data safely without any complexity. Nevertheless, the future looks a lot smarter because working with a suitable data warehousing system has shown to enhance efficiency and effectiveness. Big Data
Big data refers to the massive collection of data that can be analyzed computationally to extract useful information (Santos et al., 2017). The fact that big data
deals with large amounts of the data, it is still able to manage and uncover hidden patterns and correlations that may be present. With big data, it is possible to analyze enormous amounts of data and get the outcome from it instantly –something that was not possible with the traditional data handling methods. Big data assists companies to harness their data more efficiently and use it to identify new opportunities. The ability to work faster and promptly has given these organizations a competitive age they never had before. Moreover, as a result, smarter business moves have been implemented that lead to higher revenues as well as increased levels of customer satisfaction. There are several significant areas that I have detailed below where big data is currently being applied to excel advantage in practice. One of the most common areas where big data is used today is understanding and targeting consumers. Organizations use big data to understand consumer's behavior and preferences. This is achieved through performing data analysis to get a complete picture of their customers, and after that, create predictive models. In the United States, many companies have adopted the use of big data to predict their clients' needs accurately. For instance, Wal-Mart can predict what products
to sell, Telecom companies can predict their customer churn, and car insurance companies understand perfectly how well their clients drive their vehicles. Big data I used in not only the business environment but also other platforms. For example, in government elections where it is widely believed that Obama's Presidential victory in 2012 was primarily due to his campaign team's superior ability in the use of big data. Big data is not only for organizations use alone but for us to use as well, and I am an example of myself as one of the beneficiaries of big data. Through the help of smartwatches, I can collect data activity levels, calorie consumption, as well as sleep patterns, but the actual value is in the analysis of the collected data. Through the study of the gathered data, I can create entirely new ideas and develop a healthy lifestyle. Green Computing
According to Sreenandana, Nair, and Aneesh (2020), the global green IT services market is projected to reach more than 7 billion by 2025, to reflect an annual
growth rate of nearly 7 percent. The growth trend is primarily attributed to green data center initiatives that are not only aimed at reducing environmental pollution but also in managing the ever-increasing energy costs. Several factors play a significant role in loss and carbon footprint reduction, and the major one includes alternative green energy technologies. Assert that there are several ways in which organizations can build and implement green data center initiatives to maximize efficiency and profits (Airehrour, Cherrington, Madanian & Singh, 2019). The first step to achieving this include, conducting a baseline energy audit to provide a real- time assessment of usage and efficiency, and it will also be used as a benchmark for evaluation to guide long term planning. This is significant since data centers are typically comprised of a variety of diverse systems. After the full audit is accomplished, the next step would be to select green friendly and environmental materials such as renewable sources. The third way would be prioritizing the reduction of data center power usage as this is critical in lowering the amount of energy needed to power the IT equipment.
1
4
1
1
1
1
The last step would be to build the green data center infrastructure, and this would include eliminating all the inefficiencies. Microsoft Corporation is an example
of an organization that has already implemented IT green computing successfully. The company has tested the undersea data center through its new research initiative, known as Project Natick. The project has supposedly reduced costs, enhanced environmental sustainability, as well as accelerated deployment. The data center is environment friendly because it does not consume ocean water and runs on energy produced by the water’s movement.
Conclusion
1
4/18/2020 Originality Report
https://ucumberlands.blackboard.com/webapps/mdb-sa-BB5a31b16bb2c48/originalityReport/ultra?attemptId=40488198-db07-4fb3-8984-c92a4e917c… 3/6
Source Matches (38)
Student paper 94%
Student paper 100%
Student paper 65%
Student paper 96%
Student paper 97%
Student paper 100%
Student paper 100%
Student paper 85%
Student paper 99%
Data Warehouse, Big data, and Green computing, once defined, show how they relate with each other. While Big data is a collection of information, the data
warehouse is where all the collected data are stored to help in decision making as well as support the organization’s needs. Business trends are ever-changing, and environmental needs must enhance the system that supports them. Data is vital for organizations, and that is why managers keep this critical resource effectively in the data warehouse to make better decisions and gain competitive advantage. Managers also have to keep track that their business operations are environmental- friendly to improve and optimize business processes.
References
Airehrour, D., Cherrington, M., Madanian, S., & Singh, J. (2019). Reducing ICT carbon footprints through adoption of green computing. In 10.12948/ie2019.
04.17. Academy of Economic Studies in Bucharest. Department of Economic Informatics and Cybernetics. Santoso, L. W. (2017). Data warehouse with big
data technology for higher education. Procedia Computer Science, 124, 93-99. Santos, M. Y., Martinho, B., & Costa, C. (2017). Modelling and implementing
big data warehouses for decision support. Journal of Management Analytics, 4(2), 111-129. Sreenandana, M. V., Nair, G. B., & Aneesh, A. S. (2020). GREEN
COMPUTING: TECHNOLOGY AS GREEN ENABLERS. SUSTAINABILITY, TRANSFORMATION, DEVELOPMENT IN BUSINESS AND MANAGEMENT, 206.
1
1 1
1 1
1 1
1
1
1
Student paper
FINAL PORTFOLIO PROJECT 1 FINAL PORTFOLIO PROJECT 5 FINAL PORTFOLIO PROJECT
Original source
FINAL PORTFOLIO PROJECT 1 FINAL PORTFOLIO PROJECT FINAL PORTFOLIO PROJECT
2
Student paper
University of the Cumberlands
Original source
University of the Cumberlands
3
Student paper
April 18, 2020
Original source
04/18/2020
1
Student paper
Most organizations today rely on knowledge-based management systems. Nevertheless, these systems derive knowledge from big data analysis. Data warehouses are the core components of knowledge-management systems. The primary purpose of building a data warehouse is to integrate multiple, independent, and distributed data sources within an organization.
Original source
The Most organization today rely on knowledge-based management systems Nevertheless, these systems derive knowledge from big data analysis Data warehouses are the core components of knowledge-management systems The primary purpose of the building data warehouse is to integrate multiple, independent, and distributed data sources within an organization
1
Student paper
The historical data is used for analysis to support business decisions at all levels ranging from strategic planning to performance evaluation of a discrete organizational unit. All these components are characterized by high volumes of data and data flows that require continuous analysis and mining. These applications, therefore, require data warehousing and analysis. It also provides a platform for advance and sophisticated data analysis.
Original source
The historical data is used for analysis to support business decisions at all levels ranging from strategic planning to performance evaluation of a discrete organizational unit All these components are characterized by high volumes of data and data flows, that require continuous analysis and mining These applications, therefore require data warehousing and analysis It also provides a platform for advance and complex data analysis
1
Student paper
The fact that big data deals with large amounts of the data, it is still able to manage and uncover hidden patterns and correlations that may be present. With big data, it is possible to analyze enormous amounts of data and get the outcome from it instantly –something that was not possible with the traditional data handling methods. Final Portfolio Project We live in a contemporary world where technology is fast outpacing our ideologies.
Original source
The fact that big data deals with large amounts of the data, it is still able to manage and uncover hidden patterns and correlations that may be present With big data, it is possible to analyze enormous amounts of data and get the outcome from it instantly something that was not possible with the traditional data handling methods FINAL PORTFOLIO PROJECT We live in a contemporary world where technology is fast outpacing our ideologies
1
Student paper
Today, we see business intelligence applications in electronic commerce, telecommunications, and other industries. These applications, therefore, require data warehousing and analysis. As such, this paper provides a detailed analysis of data warehousing and its main components, not forgetting its modern trends. All these components are characterized by high volumes of data and data flows that require continuous analysis and mining.
Original source
Today, we see business intelligence applications in electronic commerce, telecommunications, and other industries These applications, therefore require data warehousing and analysis As such, this paper provides a detailed analysis of data warehousing and its main components, not forgetting its modern trends All these components are characterized by high volumes of data and data flows, that require continuous analysis and mining
1
Student paper
It also provides a summary analysis of big data and, lastly, discusses the green computing technology. Data Warehouse Database
Original source
It also provides a summary analysis of big data Data Warehouse Database
1
Student paper
In simplifying, it plays a vital part in the reporting and analysis processes of an organization. In other words, it is a database containing data that usually represent the business history of an organization.
Original source
It plays a vital part in simplifying the reporting and analysis processes of an organization In other words, it is a database containing data that usually represent the business history of an organization
4/18/2020 Originality Report
https://ucumberlands.blackboard.com/webapps/mdb-sa-BB5a31b16bb2c48/originalityReport/ultra?attemptId=40488198-db07-4fb3-8984-c92a4e917c… 4/6
Student paper 91%
Student paper 94%
Student paper 100%
Student paper 96%
Student paper 79%
Student paper 86%
Student paper 85%
Student paper 100%
Student paper 95%
Student paper 100%
1
Student paper
In the data warehouse, there are five main components, and they include; Acquisition, Sourcing, Clean-up, Metadata, Transformational Tools (ETL), Query Tools, Data Warehouse Database, and Data warehouse Bus Architecture (Santoso, 2017). This is the foundation of the data warehousing environment and is implemented on the RDBMS technology.
Original source
According to Santoso (2017), there are five main components of a data warehouse, and they include Data Warehouse Database, Sourcing, Acquisition, Clean-up, and Transformational Tools (ETL), Metadata, Query Tools, Data warehouse Bus Architecture This is the foundation of the data warehousing environment and is implemented on the RDBMS technology
1
Student paper
Sourcing, Acquisition, Clean-up and Transformational Tools (ETL) ETL refers to a data integration function that involves the extraction of data from the source, assuring quality, and data cleaning to deliver data in a physical format that can be useful for further reference. Elimination of outdated or unwanted data in operational databases from stacking into the data warehouse is the essential function of the ETL. According to Santoso (2017), the organization uses the ETL technology to read data from the outside source, clean it up, as well as format it uniformly to load it into a target data warehouse.
Original source
Sourcing, Acquisition, Clean-up and Transformational Tools (ETL) ETL refers to a data integration function that involves the extraction of data from the source, assuring quality, and data cleaning to deliver data in a physical format that can be useful for further reference The primary function of ETL includes the elimination of outdated or unwanted data in operational databases from loading into the data warehouse According to Santoso (2017), the organization uses the ETL technology to read data from the outside source, clean it up, as well as format it uniformly to load it into a target data warehouse
1
Student paper
Metadata, as the name suggests, refers to data that defines the data warehouse.
Original source
Metadata, as the name suggests, refers to data that defines the data warehouse
1
Student paper
Metadata assumes a vital role in the Data Warehouse architecture as it indicates the features of data warehouse data, usage values, and source. Santos, Martinho, and Costa (2017) also tell us that Metadata is closely linked to the data warehouse and hence facilitates how data is changed and processed. Therefore, Metadata can be said to be a vital component in the transformation of data into knowledge.
Original source
Metadata plays a vital role in the Data Warehouse architecture as it specifies the source, usage values, and features of data warehouse data Santos, Martinho & Costa (2017) also tell us that Metadata is closely linked to the data warehouse and hence facilitates how data is changed and processed Therefore, Metadata can be said to be a vital component in the transformation of data into knowledge
1
Student paper
To interact with the data warehouse system, query tools in this way gives an effective platform for the users. The devices referenced here are additionally isolated into four classes, to be specific, data mining tools, Query and reporting tools, application and development tools, as well as OLAP tools.
Original source
Query tools, therefore, provides an effective platform for users to interact with the data warehouse system The tools mentioned here are further divided into four categories, namely, Query and reporting tools, application and development tools, data mining tools, as well as OLAP tools
1
Student paper
To interact with the data warehouse system, every one of these tools is essential in allowing the users.
Original source
All these tools are essential in allowing users to interact with the data warehouse system
1
Student paper
The stream can be classified as either inflow, outflow, Meta flow, or upstream. In data marts, an IT manager must think about all facts as well as shared dimensions to design a data warehouse bus. Data marts refer to access layers that are typically used to process user data to the users (Santos et al., 2017).
Original source
The flow can be categorized as either outflow, inflow, upstream, or Meta flow To design a Data Warehouse Bus, an IT manager must consider all the shared dimensions as well as facts in data marts Data marts refer to access layers that are typically used to process useful data to the users Santos, Martinho & Costa (2017)
4
Student paper
Current Key trends in data warehousing
Original source
Current key trends in data warehousing
1
Student paper
The modern world generates, uses, as well as retains useful data for future usage. Since the global world is projected to continue to grow for the foreseeable future, it is approximated by 2026 that the world will generate and replicate 165ZB of data. This will arise as a result of increased use of computers in doing business; hence data will need to be instantly available whenever required.
Original source
Current Key trends in data warehousing The modern world generates, uses, as well as retains useful data for future usage Since the global world is projected to continue to grow for the foreseeable future, it is approximated by 2026 that the world will generate and replicate 165ZB of data This will arise as a result of increased use of computers in doing business hence data will need to be instantly available whenever required
1
Student paper
Since Data warehousing solutions came into play, most big companies such as Google BigQuery, Amazon, Panoply, and Redshift have all adopted the use of this tool to manage their data. These organizations manage partitioning as well as the scalability of a data warehouse in a transparent manner. Data warehousing has made it possible for enterprises to set up a petabyte-scale to hold up all data safely without any complexity. Nevertheless, the future looks a lot smarter because working with a suitable data warehousing system has shown to enhance efficiency and effectiveness.
Original source
Since Data warehousing solutions came into play, most big companies such as Google BigQuery, Amazon, Panoply, and Redshift have all adopted the use of this tool to manage their data These organizations manage partitioning as well as the scalability of a data warehouse in a transparent manner Data warehousing has made it possible for enterprises to set up a petabyte-scale to hold up all data safely without any complexity Nevertheless, the future looks a lot smarter because working with a suitable data warehousing system has shown to enhance efficiency and effectiveness
4/18/2020 Originality Report
https://ucumberlands.blackboard.com/webapps/mdb-sa-BB5a31b16bb2c48/originalityReport/ultra?attemptId=40488198-db07-4fb3-8984-c92a4e917c… 5/6
Student paper 97%
Student paper 99%
Student paper 96%
Student paper 93%
Student paper 91%
Student paper 100%
Student paper 98%
1
Student paper
Big data refers to the massive collection of data that can be analyzed computationally to extract useful information (Santos et al., 2017). The fact that big data deals with large amounts of the data, it is still able to manage and uncover hidden patterns and correlations that may be present. With big data, it is possible to analyze enormous amounts of data and get the outcome from it instantly –something that was not possible with the traditional data handling methods. Big data assists companies to harness their data more efficiently and use it to identify new opportunities.
Original source
Big data refers to the massive collection of data that can be analyzed computationally to extract useful information (Santos, Martinho & Costa, 2017) The fact that big data deals with large amounts of the data, it is still able to manage and uncover hidden patterns and correlations that may be present With big data, it is possible to analyze enormous amounts of data and get the outcome from it instantly something that was not possible with the traditional data handling methods Big data assists companies to harness their data more efficiently and use it to identify new opportunities
1
Student paper
The ability to work faster and promptly has given these organizations a competitive age they never had before. Moreover, as a result, smarter business moves have been implemented that lead to higher revenues as well as increased levels of customer satisfaction. There are several significant areas that I have detailed below where big data is currently being applied to excel advantage in practice. One of the most common areas where big data is used today is understanding and targeting consumers.
Original source
The ability to work faster and promptly has given these organizations a competitive age they never had before And as a result, smarter business moves have been implemented that lead to higher revenues as well as increased levels of customer satisfaction There are several significant areas that I have detailed below where big data is currently being applied to excel advantage in practice One of the most common areas where big data is used today is understanding and targeting consumers
1
Student paper
Organizations use big data to understand consumer's behavior and preferences. This is achieved through performing data analysis to get a complete picture of their customers, and after that, create predictive models. In the United States, many companies have adopted the use of big data to predict their clients'
Original source
Organizations use big data to understand consumer's behavior and preferences This is achieved through performing data analysis to get a complete picture of their customers, and after that, create predictive models Many companies in the U.S have adopted the use of big data to predict their clients'
1
Student paper
For instance, Wal-Mart can predict what products to sell, Telecom companies can predict their customer churn, and car insurance companies understand perfectly how well their clients drive their vehicles. Big data I used in not only the business environment but also other platforms. For example, in government elections where it is widely believed that Obama's Presidential victory in 2012 was primarily due to his campaign team's superior ability in the use of big data. Big data is not only for organizations use alone but for us to use as well, and I am an example of myself as one of the beneficiaries of big data.
Original source
For instance, Wal-Mart can predict what products to sell, Telecom companies can predict their customer churn, and car insurance companies understand perfectly how well their clients drive their vehicles Big data I used in not only the business environment but also other platforms For example, in government elections where it is widely believed that Obama's Presidential victory in 2012 was primarily due to his campaign team's superior ability in the use of big data Big data is not only for organizations use alone but for us to use as well
1
Student paper
Through the help of smartwatches, I can collect data activity levels, calorie consumption, as well as sleep patterns, but the actual value is in the analysis of the collected data. Through the study of the gathered data, I can create entirely new ideas and develop a healthy lifestyle.
Original source
Through the help of smartwatches, I can collect data activity levels, calorie consumption, as well as sleep patterns Through the study of the gathered data, I can create entirely new ideas and develop a healthy lifestyle
1
Student paper
According to Sreenandana, Nair, and Aneesh (2020), the global green IT services market is projected to reach more than 7 billion by 2025, to reflect an annual growth rate of nearly 7 percent. The growth trend is primarily attributed to green data center initiatives that are not only aimed at reducing environmental pollution but also in managing the ever-increasing energy costs. Several factors play a significant role in loss and carbon footprint reduction, and the major one includes alternative green energy technologies. Assert that there are several ways in which organizations can build and implement green data center initiatives to maximize efficiency and profits (Airehrour, Cherrington, Madanian & Singh, 2019).
Original source
According to Sreenandana, Nair & Aneesh (2020), the global green IT services market is projected to reach more than 7 billion by 2025, to reflect an annual growth rate of nearly 7 percent The growth trend is primarily attributed to green data center initiatives that are not only aimed at reducing environmental pollution but also in managing the ever-increasing energy costs Several factors play a significant role in loss and carbon footprint reduction, and the major one includes alternative green energy technologies Airehrour, Cherrington, Madanian & Singh (2019) assert that there are several ways in which organizations can build and implement green data center initiatives to maximize efficiency and profits
1
Student paper
The first step to achieving this include, conducting a baseline energy audit to provide a real-time assessment of usage and efficiency, and it will also be used as a benchmark for evaluation to guide long term planning. This is significant since data centers are typically comprised of a variety of diverse systems. After the full audit is accomplished, the next step would be to select green friendly and environmental materials such as renewable sources. The third way would be prioritizing the reduction of data center power usage as this is critical in lowering the amount of energy needed to power the IT equipment.
Original source
The first step to achieving this include, conducting a baseline energy audit to provide a real-time assessment of usage and efficiency, as well as a benchmark for evaluation to guide long term planning This is significant since data centers are typically comprised of a variety of diverse systems After the full audit is accomplished, the next step would be to select green friendly and environmental materials such as renewable sources The third way would be prioritizing the reduction of data center power usage as this is critical in lowering the amount of energy needed to power the IT equipment
4/18/2020 Originality Report
https://ucumberlands.blackboard.com/webapps/mdb-sa-BB5a31b16bb2c48/originalityReport/ultra?attemptId=40488198-db07-4fb3-8984-c92a4e917c… 6/6
Student paper 96%
Student paper 100%
Student paper 94%
Student paper 100%
Student paper 100%
Student paper 100%
Student paper 100%
Student paper 100%
Student paper 100%
Student paper 100%
Student paper 100%
Student paper 100%
1
Student paper
The last step would be to build the green data center infrastructure, and this would include eliminating all the inefficiencies. Microsoft Corporation is an example of an organization that has already implemented IT green computing successfully. The company has tested the undersea data center through its new research initiative, known as Project Natick. The project has supposedly reduced costs, enhanced environmental sustainability, as well as accelerated deployment.
Original source
The last step would be to build the green data center infrastructure, and this would include eliminating all the inefficiencies Microsoft Corporation is an example of an organization that has already implemented IT green computing successfully The company has tested the undersea data center through its new research initiative, known as Project Natick (www.informationweek.com/data- centers) The project has supposedly reduced costs, enhanced environmental sustainability, as well as accelerated deployment
1
Student paper
The data center is environment friendly because it does not consume ocean water and runs on energy produced by the water’s movement.
Original source
The data center is environment friendly because it does not consume ocean water, and runs on energy produced by the water’s movement
1
Student paper
Data Warehouse, Big data, and Green computing, once defined, show how they relate with each other. While Big data is a collection of information, the data warehouse is where all the collected data are stored to help in decision making as well as support the organization’s needs. Business trends are ever-changing, and environmental needs must enhance the system that supports them. Data is vital for organizations, and that is why managers keep this critical resource effectively in the data warehouse to make better decisions and gain competitive advantage.
Original source
Data Warehouse, Big data, and Green computing, once defined show how they relate with each other While Big data is a collection of information, the data warehouse is where all the collected data are stored to help in decision making as well as support the organization’s needs Business trends are ever changing, and the system that supports them must be enhanced in accordance with environmental needs Data is vital for organizations and that is why managers keep this important resource effectively in the data warehouse to make better decisions and gain competitive advantage
1
Student paper
Managers also have to keep track that their business operations are environmental-friendly to improve and optimize business processes.
Original source
Managers also have to keep track that their business operations are environmental-friendly to improve and optimize business processes
1
Student paper
Airehrour, D., Cherrington, M., Madanian, S., & Singh, J.
Original source
Airehrour, D., Cherrington, M., Madanian, S., & Singh, J
1
Student paper
Reducing ICT carbon footprints through adoption of green computing. In 10.12948/ie2019.
Original source
Reducing ICT carbon footprints through adoption of green computing In 10.12948/ie2019
1
Student paper
Academy of Economic Studies in Bucharest. Department of Economic Informatics and Cybernetics.
Original source
Academy of Economic Studies in Bucharest Department of Economic Informatics and Cybernetics
1
Student paper
Data warehouse with big data technology for higher education. Procedia Computer Science, 124, 93-99.
Original source
Data warehouse with big data technology for higher education Procedia Computer Science, 124, 93-99
1
Student paper
Y., Martinho, B., & Costa, C.
Original source
Y., Martinho, B., & Costa, C
1
Student paper
Modelling and implementing big data warehouses for decision support. Journal of Management Analytics, 4(2), 111-129.
Original source
Modelling and implementing big data warehouses for decision support Journal of Management Analytics, 4(2), 111-129
1
Student paper
V., Nair, G. B., & Aneesh, A.
Original source
V., Nair, G B., & Aneesh, A
1
Student paper
TECHNOLOGY AS GREEN ENABLERS. SUSTAINABILITY, TRANSFORMATION, DEVELOPMENT IN BUSINESS AND MANAGEMENT, 206.
Original source
TECHNOLOGY AS GREEN ENABLERS SUSTAINABILITY, TRANSFORMATION, DEVELOPMENT IN BUSINESS AND MANAGEMENT, 206
