Big Data provides an opportunity to educational Institutions to use their Information Technology resources strategically to improve educational quality and guide students to higher rates of completion, and to improve student persistence and outcomes. This paper explores the attributes of big data that are relevant to educational institutions, investigates the factors influencing adoption of big data and analytics in learning institutions and seeks to establish the limiting factors hindering use of big data in Institutions of higher learning. The study has been conducted through a desk search and reviewed sources of literature including scientific research journals and reports. The paper is based on desk research. The sources of literature that were reviewed included scientific research articles and journals, conference reports and theses. Online journals found on the internet were also examined with the search being broadened by Google Scholar where the following keywords were used “big data”, “developing countries”, “education systems” and “clustering”. The paper concludes that Big Data is important since it offers Universities opportunities to their Information Technology resources strategically to improve educational quality and guide students, colleges and universities see value in analytics; and therefore recommends that these institutions carry out investments in analytics programs and in people to have relevant data science. This is because Big Data can afford educational institutions opportunities to shape a modern and dynamic education system, in which every individual student can have the maximum benefit from, and can greatly contribute towards improving the quality of education
THE CRITICAL SUCCESS FACTORS FOR BIG DATA ADOPTION IN GOVERNMENTIAEME Publication
Over the past decade, governments around the world have been trying to take
advantage of Big Data technology to improve public services with citizens. The
adoption of Big Data has increased in most countries, but at the same time, the rate of
successful adoption and management varies from one country to another. A systematic
review of the literature (SLR) was carried out to identify the critical success factors
(CSF) for the adoption of big data in the government. It includes the critical success
factor of the adoption of Big Data in the government in the last 10 years. It presents
the general trends that examine 183 journals and numerous literary works related to
government operations, the provision of public services, citizen participation, decision
making and policies, and governance reform. We selected 90 journals and found 11
classification factors that refer to the successions of a Big Data adoption in the
government
Diffusion of Big Data and Analytics in Developing Countriestheijes
This document provides an overview of big data and analytics in developing countries. It discusses the increasing volume, velocity, and variety of digital data being produced globally each year, including in developing nations. The document then examines the potential for big data innovation diffusion in developing countries using Rogers' diffusion of innovations theory framework. Some key challenges to big data adoption in developing countries include lack of infrastructure, skills, and resources. However, the theory suggests concepts like communication channels and the innovation-decision process can help increase adoption rates over time within social systems. The study aims to inform policymakers on maximizing big data potential in developing nations.
Hadoop and Big Data Readiness in Africa: A Case of Tanzaniaijsrd.com
Big data has been referred to as a forefront pillar of any modern analytics application. Together with Hadoop which is open source software, they have emerged to be a solution to the processing of massive generated both structured and unstructured data. With different strategies and initiatives taken by governments and private institutions in the world towards deployment and support of big data analytics and hadoop, Africa cannot be left isolated. In this paper, we assessed the readiness of Africa with a case study of Tanzania in harnessing the power of big data analytics and hadoop as a tool for drawing insights that might help them make crucial decisions. We used a survey in collecting the data using questionnaires. Results reveal that majority of the companies are either not aware of the technologies or still in their infancy stages in using big data analytics and hadoop. We identified that most companies are in either awakening or advancing stages of the big data continuum. This is attributed by challenges such as lack of IT skills to manage big data projects, cost of technology infrastructure, making decision on which data are relevant, lack of skills to analyze the data, lack of business support and deciding on what technology is best compared to others. It has also been found out that most of the companies' IT officers are not aware with the concepts and techniques of big data analytics and hadoop.
The document discusses developing an integrated framework to utilize big data for higher education institutions in Saudi Arabia. It aims to develop a framework to support decision making and improve performance in education sectors using big data. The study collected data through surveys and interviews to analyze factors affecting adoption and implementation of big data. The framework addresses issues related to adoption of big data in education.
Big Data Analytics and E Learning in Higher Education. Tulasi.B & Suchithra.Reraser Juan José Calderón
Big Data Analytics and E Learning in Higher Education. Tulasi.B & Suchithra.R. Department of Computer Science, Christ University, Bangalore, India Department of Computer Science , Jain University, Bangalore, India
Identifying and analyzing the transient and permanent barriers for big datasarfraznawaz
The document discusses identifying and analyzing the transient and permanent barriers for adopting big data. It begins by providing background on big data and its opportunities. It then identifies five transient barriers: data storage and transfer, scalability, data quality, data complexity, and timeliness. The barriers are analyzed in depth. Four permanent barriers are also identified: security, privacy, trust, data ownership, and transparency. The barriers are discussed and the challenges of overcoming the permanent barriers through technology alone are noted.
The effect of technology-organization-environment on adoption decision of bi...IJECEIAES
Big data technology (BDT) is being actively adopted by world-leading organizations due to its expected benefits. However, most of the organizations in Thailand are still in the decision or planning stage to adopt BDT. Many challenges exist in encouraging the BDT diffusion in businesses. Thus, this study develops a research model that investigates the determinants of BDT adoption in the Thai context based on the technology-organizationenvironment (TOE) framework and diffusion of innovation (DOI) theory. Data were collected through an online questionnaire. Three hundred IT employees in different organizations in Thailand were used as a sample group. Structural equation modeling (SEM) was conducted to test the hypotheses. The result indicated that the research model was fitted with the empirical data with the statistics: Normed Chi-Square=1.651, GFI=0.895, AFGI=0.863, NFI=0.930, TLI=0.964, CFI=0.971, SRMR=0.0392, and RMSEA=0.046. The research model could, at 52%, explain decision to adopt BDT. Relative advantage, top management support, competitive pressure, and trading partner pressure show significant positive relation with BDT adoption, while security negatively influences BDT adoption.
Data mining involves extracting patterns from large data sets. It is used to uncover hidden information and relationships within data repositories like databases, text files, social networks, and computer simulations. The patterns discovered can be used by organizations to make better business decisions. Some common applications of data mining include credit card fraud detection, customer segmentation for marketing, and scientific research. The process involves data preparation, algorithm selection, model building, and interpretation. While useful, data mining also raises privacy, security, and ethical concerns if misused.
THE CRITICAL SUCCESS FACTORS FOR BIG DATA ADOPTION IN GOVERNMENTIAEME Publication
Over the past decade, governments around the world have been trying to take
advantage of Big Data technology to improve public services with citizens. The
adoption of Big Data has increased in most countries, but at the same time, the rate of
successful adoption and management varies from one country to another. A systematic
review of the literature (SLR) was carried out to identify the critical success factors
(CSF) for the adoption of big data in the government. It includes the critical success
factor of the adoption of Big Data in the government in the last 10 years. It presents
the general trends that examine 183 journals and numerous literary works related to
government operations, the provision of public services, citizen participation, decision
making and policies, and governance reform. We selected 90 journals and found 11
classification factors that refer to the successions of a Big Data adoption in the
government
Diffusion of Big Data and Analytics in Developing Countriestheijes
This document provides an overview of big data and analytics in developing countries. It discusses the increasing volume, velocity, and variety of digital data being produced globally each year, including in developing nations. The document then examines the potential for big data innovation diffusion in developing countries using Rogers' diffusion of innovations theory framework. Some key challenges to big data adoption in developing countries include lack of infrastructure, skills, and resources. However, the theory suggests concepts like communication channels and the innovation-decision process can help increase adoption rates over time within social systems. The study aims to inform policymakers on maximizing big data potential in developing nations.
Hadoop and Big Data Readiness in Africa: A Case of Tanzaniaijsrd.com
Big data has been referred to as a forefront pillar of any modern analytics application. Together with Hadoop which is open source software, they have emerged to be a solution to the processing of massive generated both structured and unstructured data. With different strategies and initiatives taken by governments and private institutions in the world towards deployment and support of big data analytics and hadoop, Africa cannot be left isolated. In this paper, we assessed the readiness of Africa with a case study of Tanzania in harnessing the power of big data analytics and hadoop as a tool for drawing insights that might help them make crucial decisions. We used a survey in collecting the data using questionnaires. Results reveal that majority of the companies are either not aware of the technologies or still in their infancy stages in using big data analytics and hadoop. We identified that most companies are in either awakening or advancing stages of the big data continuum. This is attributed by challenges such as lack of IT skills to manage big data projects, cost of technology infrastructure, making decision on which data are relevant, lack of skills to analyze the data, lack of business support and deciding on what technology is best compared to others. It has also been found out that most of the companies' IT officers are not aware with the concepts and techniques of big data analytics and hadoop.
The document discusses developing an integrated framework to utilize big data for higher education institutions in Saudi Arabia. It aims to develop a framework to support decision making and improve performance in education sectors using big data. The study collected data through surveys and interviews to analyze factors affecting adoption and implementation of big data. The framework addresses issues related to adoption of big data in education.
Big Data Analytics and E Learning in Higher Education. Tulasi.B & Suchithra.Reraser Juan José Calderón
Big Data Analytics and E Learning in Higher Education. Tulasi.B & Suchithra.R. Department of Computer Science, Christ University, Bangalore, India Department of Computer Science , Jain University, Bangalore, India
Identifying and analyzing the transient and permanent barriers for big datasarfraznawaz
The document discusses identifying and analyzing the transient and permanent barriers for adopting big data. It begins by providing background on big data and its opportunities. It then identifies five transient barriers: data storage and transfer, scalability, data quality, data complexity, and timeliness. The barriers are analyzed in depth. Four permanent barriers are also identified: security, privacy, trust, data ownership, and transparency. The barriers are discussed and the challenges of overcoming the permanent barriers through technology alone are noted.
The effect of technology-organization-environment on adoption decision of bi...IJECEIAES
Big data technology (BDT) is being actively adopted by world-leading organizations due to its expected benefits. However, most of the organizations in Thailand are still in the decision or planning stage to adopt BDT. Many challenges exist in encouraging the BDT diffusion in businesses. Thus, this study develops a research model that investigates the determinants of BDT adoption in the Thai context based on the technology-organizationenvironment (TOE) framework and diffusion of innovation (DOI) theory. Data were collected through an online questionnaire. Three hundred IT employees in different organizations in Thailand were used as a sample group. Structural equation modeling (SEM) was conducted to test the hypotheses. The result indicated that the research model was fitted with the empirical data with the statistics: Normed Chi-Square=1.651, GFI=0.895, AFGI=0.863, NFI=0.930, TLI=0.964, CFI=0.971, SRMR=0.0392, and RMSEA=0.046. The research model could, at 52%, explain decision to adopt BDT. Relative advantage, top management support, competitive pressure, and trading partner pressure show significant positive relation with BDT adoption, while security negatively influences BDT adoption.
Data mining involves extracting patterns from large data sets. It is used to uncover hidden information and relationships within data repositories like databases, text files, social networks, and computer simulations. The patterns discovered can be used by organizations to make better business decisions. Some common applications of data mining include credit card fraud detection, customer segmentation for marketing, and scientific research. The process involves data preparation, algorithm selection, model building, and interpretation. While useful, data mining also raises privacy, security, and ethical concerns if misused.
A Model Design of Big Data Processing using HACE TheoremAnthonyOtuonye
This document presents a model for big data processing using the HACE theorem. It proposes a three-tier data mining structure to provide accurate, real-time social feedback for understanding society. The model adopts Hadoop's MapReduce for big data mining and uses k-means and Naive Bayes algorithms for clustering and classification. The goal is to address challenges of big data and assist governments and businesses in using big data technology.
This document summarizes a case study on a large logistics firm's use of big data analytics (BDA) and the Internet of Things (IoT) to improve operations. Specifically, the company utilizes truck telematics to monitor driver behavior data and inform training. Sensor data is also used to send proactive alerts to drivers. Camera technologies capture driving events to improve safety. BDA helps optimize routing, fuel purchasing, and predictive maintenance scheduling. The case study provides a real-world example of how logistics companies can leverage emerging technologies to enhance performance.
Australia bureau of statistics some initiatives on big data - 23 july 2014noviari sugianto
This document discusses the opportunities and challenges of using Big Data in official statistics. It outlines several potential applications of Big Data, including sample frame creation, full or partial data substitution, imputation, and generating new insights. However, the decision to use a Big Data source should be based on a strong business case and cost-benefit analysis. The document provides an example cost-benefit analysis for using satellite imagery to replace agricultural survey data. It also emphasizes that Big Data sources must meet validity criteria for statistical inferences.
The document reports on the Massachusetts big data ecosystem and opportunities for growth. It finds that Massachusetts has close to 500 big data companies, 10 leading research centers, and graduates around 5,600 students annually from 14 data science programs. The state has seen $2.5 billion in investment funding for big data companies since 2000. However, it also faces challenges retaining data science talent and developing skills in the growing field. The report identifies priorities like strengthening data science education and expanding collaboration to help the ecosystem continue thriving.
This document discusses challenges and outlooks related to big data. It begins with an introduction describing how big data is being collected and analyzed in various fields such as science, education, healthcare, urban planning, and more. It then outlines the key phases in big data analysis: data acquisition and recording, information extraction and cleaning, data integration and representation, query processing and analysis, and result interpretation. For each phase, it discusses challenges and how existing techniques can be applied or extended to address big data issues. Some of the major challenges discussed are data scale, heterogeneity, lack of structure, privacy, timeliness, provenance, and visualization across the entire big data analysis pipeline.
Big Data, Data Science, Machine learning creating tremendous value in the education sector. Combination of open source with IBM value adds create compelling value. Artificial intelligence will revolutionize the sector with making education more relevant with Cognitive capabilities of students.
Influence of Big Data Analytics in Supply Chain Management- A case study in B...Monwar Anik
Big Data Analytics (BDA) has the potential to improve demand forecasting, communications and better manage supply chain resources. Despite such recognized benefits and the increase of BDA research, little is known about the general approaches used to investigate BDA in the context of supply chain management (SCM). In the light of the Resource-based View, the main goal of this study was, by means of a systematic literature review, to comprehend how BDA has been investigated on SCM studies, which resources are managed by BDA as well as which SCM processes are involved. Our study found out that the predictive and prescriptive approaches are more frequently used and organizational, technological and human resources are often managed by BDA. It was observed a focus on Demand Management and Order Fulfilment processes and a lack of studies on Returns Management, which indicates an open research area that should be exploited by future studies
Influence of Big Data Analytics in Supply Chain Management- A case study in B...Monwar Anik
Big Data Analytics (BDA) has the potential to improve demand forecasting, communications and better manage supply chain resources. Despite such recognized benefits and the increase of BDA research, little is known about the general approaches used to investigate BDA in the context of supply chain management (SCM). In the light of the Resource-based View, the main goal of this study was, by means of a systematic literature review, to comprehend how BDA has been investigated on SCM studies, which resources are managed by BDA as well as which SCM processes are involved. Our study found out that the predictive and prescriptive approaches are more frequently used and organizational, technological and human resources are often managed by BDA. It was observed a focus on Demand Management and Order Fulfilment processes and a lack of studies on Returns Management, which indicates an open research area that should be exploited by future studies
Big Data must be processed with advanced collection and analysis tools, based on predetermined algorithms, in order to obtain relevant information. Algorithms must also take into account invisible aspects for direct perceptions. Big Data issues is multi-layered. A distributed parallel architecture distributes data on multiple servers (parallel execution environments) thus dramatically improving data processing speeds. Big Data provides an infrastructure that allows for highlighting uncertainties, performance, and availability of components.
DOI: 10.13140/RG.2.2.12784.00004
Data science applications and use cases were discussed. Examples included using data science in business for tasks like car design and insurance, in healthcare for reducing readmissions and improving care, and in urban planning to address challenges in growing cities. Cancer research was highlighted as an area using big data analytics and machine learning to identify patterns linked to cancer. Healthcare examples included using genetic data at Stanford Medicine for precision health. Data science was applied to political elections through Obama's targeted social media campaigns. Finally, the growing field of internet of things was noted as an area that will produce huge volumes of data for analysis.
Moving Toward Big Data: Challenges, Trends and PerspectivesIJRESJOURNAL
Abstract: Big data refers to the organizational data asset that exceeds the volume, velocity, and variety of data typically stored using traditional structured database technologies. This type of data has become the important resource from which organizations can get valuable insightand make business decision by applying predictive analysis. This paper provides a comprehensive view of current status of big data development,starting from the definition and the description of Hadoop and MapReduce – the framework that standardizes the use of cluster of commodity machines to analyze big data. For the organizations that are ready to embrace big data technology, significant adjustments on infrastructure andthe roles played byIT professionals and BI practitioners must be anticipated which is discussed in the challenges of big data section. The landscape of big data development change rapidly which is directly related to the trend of big data. Clearly, a major part of the trend is the result ofthe attempt to deal with the challenges discussed earlier. Lastly the paper includes the most recent job prospective related to big data. The description of several job titles that comprise the workforce in the area of big data are also included.
A study on web analytics with reference to select sports websitesBhanu Prakash
This document is a project report submitted by Y. Bhanu Prakash to GITAM Institute of Management in partial fulfillment of the degree of Bachelor of Business Administration in Business Analytics. The report is on the topic of web analytics with reference to select sports websites. It includes declarations by the student and certification by the guide, as well as acknowledgements. The report will consist of 5 chapters - an introduction to analytics, a profile of Alexa.com, methodology, analysis and interpretation of data, and observations and conclusions.
This document discusses open data for digital development in Botswana. It outlines the importance of open data for transparency, participation, innovation and economic opportunities. It analyzes Botswana's open data readiness and compares it to international best practices from Korea. The document proposes establishing an open data portal and policy framework in Botswana to stimulate applications, startups and broadband usage to support digital development and economic diversification.
Big data analytics (BDA) provides capabilities for revealing additional value from big data. It examines large amounts of data from various sources to deliver insights that enable real-time decisions. BDA is different from data warehousing and business intelligence systems. The complexity of big data systems required developing specialized architectures like Hadoop, which processes large amounts of data in a timely and low cost manner. Big data challenges include capturing, storing, analyzing, sharing, transferring, visualizing, querying, updating, and ensuring privacy of large and diverse datasets.
LEVERAGING CLOUD BASED BIG DATA ANALYTICS IN KNOWLEDGE MANAGEMENT FOR ENHANCE...ijdpsjournal
In recent past, big data opportunities have gained much momentum to enhance knowledge management in
organizations. However, big data due to its various properties like high volume, variety, and velocity can
no longer be effectively stored and analyzed with traditional data management techniques to generate
values for knowledge development. Hence, new technologies and architectures are required to store and
analyze this big data through advanced data analytics and in turn generate vital real-time knowledge for
effective decision making by organizations. More specifically, it is necessary to have a single infrastructure
which provides common functionality of knowledge management, and flexible enough to handle different
types of big data and big data analysis tasks. Cloud computing infrastructures capable of storing and
processing large volume of data can be used for efficient big data processing because it minimizes the
initial cost for the large-scale computing infrastructure demanded by big data analytics. This paper aims to
explore the impact of big data analytics on knowledge management and proposes a cloud-based conceptual
framework that can analyze big data in real time to facilitate enhanced decision making intended for
competitive advantage. Thus, this framework will pave the way for organizations to explore the relationship
between big data analytics and knowledge management which are mostly deemed as two distinct entities.
LEVERAGING CLOUD BASED BIG DATA ANALYTICS IN KNOWLEDGE MANAGEMENT FOR ENHANCE...ijdpsjournal
In recent past, big data opportunities have gained much momentum to enhance knowledge management in
organizations. However, big data due to its various properties like high volume, variety, and velocity can
no longer be effectively stored and analyzed with traditional data management techniques to generate
values for knowledge development. Hence, new technologies and architectures are required to store and
analyze this big data through advanced data analytics and in turn generate vital real-time knowledge for
effective decision making by organizations. More specifically, it is necessary to have a single infrastructure
which provides common functionality of knowledge management, and flexible enough to handle different
types of big data and big data analysis tasks. Cloud computing infrastructures capable of storing and
processing large volume of data can be used for efficient big data processing because it minimizes the
initial cost for the large-scale computing infrastructure demanded by big data analytics. This paper aims to
explore the impact of big data analytics on knowledge management and proposes a cloud-based conceptual
framework that can analyze big data in real time to facilitate enhanced decision making intended for
competitive advantage. Thus, this framework will pave the way for organizations to explore the relationship
between big data analytics and knowledge management which are mostly deemed as two distinct entities.
A Model Design of Big Data Processing using HACE TheoremAnthonyOtuonye
This document presents a model for big data processing using the HACE theorem. It proposes a three-tier data mining structure to provide accurate, real-time social feedback for understanding society. The model adopts Hadoop's MapReduce for big data mining and uses k-means and Naive Bayes algorithms for clustering and classification. The goal is to address challenges of big data and assist governments and businesses in using big data technology.
This document summarizes a case study on a large logistics firm's use of big data analytics (BDA) and the Internet of Things (IoT) to improve operations. Specifically, the company utilizes truck telematics to monitor driver behavior data and inform training. Sensor data is also used to send proactive alerts to drivers. Camera technologies capture driving events to improve safety. BDA helps optimize routing, fuel purchasing, and predictive maintenance scheduling. The case study provides a real-world example of how logistics companies can leverage emerging technologies to enhance performance.
Australia bureau of statistics some initiatives on big data - 23 july 2014noviari sugianto
This document discusses the opportunities and challenges of using Big Data in official statistics. It outlines several potential applications of Big Data, including sample frame creation, full or partial data substitution, imputation, and generating new insights. However, the decision to use a Big Data source should be based on a strong business case and cost-benefit analysis. The document provides an example cost-benefit analysis for using satellite imagery to replace agricultural survey data. It also emphasizes that Big Data sources must meet validity criteria for statistical inferences.
The document reports on the Massachusetts big data ecosystem and opportunities for growth. It finds that Massachusetts has close to 500 big data companies, 10 leading research centers, and graduates around 5,600 students annually from 14 data science programs. The state has seen $2.5 billion in investment funding for big data companies since 2000. However, it also faces challenges retaining data science talent and developing skills in the growing field. The report identifies priorities like strengthening data science education and expanding collaboration to help the ecosystem continue thriving.
This document discusses challenges and outlooks related to big data. It begins with an introduction describing how big data is being collected and analyzed in various fields such as science, education, healthcare, urban planning, and more. It then outlines the key phases in big data analysis: data acquisition and recording, information extraction and cleaning, data integration and representation, query processing and analysis, and result interpretation. For each phase, it discusses challenges and how existing techniques can be applied or extended to address big data issues. Some of the major challenges discussed are data scale, heterogeneity, lack of structure, privacy, timeliness, provenance, and visualization across the entire big data analysis pipeline.
Big Data, Data Science, Machine learning creating tremendous value in the education sector. Combination of open source with IBM value adds create compelling value. Artificial intelligence will revolutionize the sector with making education more relevant with Cognitive capabilities of students.
Influence of Big Data Analytics in Supply Chain Management- A case study in B...Monwar Anik
Big Data Analytics (BDA) has the potential to improve demand forecasting, communications and better manage supply chain resources. Despite such recognized benefits and the increase of BDA research, little is known about the general approaches used to investigate BDA in the context of supply chain management (SCM). In the light of the Resource-based View, the main goal of this study was, by means of a systematic literature review, to comprehend how BDA has been investigated on SCM studies, which resources are managed by BDA as well as which SCM processes are involved. Our study found out that the predictive and prescriptive approaches are more frequently used and organizational, technological and human resources are often managed by BDA. It was observed a focus on Demand Management and Order Fulfilment processes and a lack of studies on Returns Management, which indicates an open research area that should be exploited by future studies
Influence of Big Data Analytics in Supply Chain Management- A case study in B...Monwar Anik
Big Data Analytics (BDA) has the potential to improve demand forecasting, communications and better manage supply chain resources. Despite such recognized benefits and the increase of BDA research, little is known about the general approaches used to investigate BDA in the context of supply chain management (SCM). In the light of the Resource-based View, the main goal of this study was, by means of a systematic literature review, to comprehend how BDA has been investigated on SCM studies, which resources are managed by BDA as well as which SCM processes are involved. Our study found out that the predictive and prescriptive approaches are more frequently used and organizational, technological and human resources are often managed by BDA. It was observed a focus on Demand Management and Order Fulfilment processes and a lack of studies on Returns Management, which indicates an open research area that should be exploited by future studies
Big Data must be processed with advanced collection and analysis tools, based on predetermined algorithms, in order to obtain relevant information. Algorithms must also take into account invisible aspects for direct perceptions. Big Data issues is multi-layered. A distributed parallel architecture distributes data on multiple servers (parallel execution environments) thus dramatically improving data processing speeds. Big Data provides an infrastructure that allows for highlighting uncertainties, performance, and availability of components.
DOI: 10.13140/RG.2.2.12784.00004
Data science applications and use cases were discussed. Examples included using data science in business for tasks like car design and insurance, in healthcare for reducing readmissions and improving care, and in urban planning to address challenges in growing cities. Cancer research was highlighted as an area using big data analytics and machine learning to identify patterns linked to cancer. Healthcare examples included using genetic data at Stanford Medicine for precision health. Data science was applied to political elections through Obama's targeted social media campaigns. Finally, the growing field of internet of things was noted as an area that will produce huge volumes of data for analysis.
Moving Toward Big Data: Challenges, Trends and PerspectivesIJRESJOURNAL
Abstract: Big data refers to the organizational data asset that exceeds the volume, velocity, and variety of data typically stored using traditional structured database technologies. This type of data has become the important resource from which organizations can get valuable insightand make business decision by applying predictive analysis. This paper provides a comprehensive view of current status of big data development,starting from the definition and the description of Hadoop and MapReduce – the framework that standardizes the use of cluster of commodity machines to analyze big data. For the organizations that are ready to embrace big data technology, significant adjustments on infrastructure andthe roles played byIT professionals and BI practitioners must be anticipated which is discussed in the challenges of big data section. The landscape of big data development change rapidly which is directly related to the trend of big data. Clearly, a major part of the trend is the result ofthe attempt to deal with the challenges discussed earlier. Lastly the paper includes the most recent job prospective related to big data. The description of several job titles that comprise the workforce in the area of big data are also included.
A study on web analytics with reference to select sports websitesBhanu Prakash
This document is a project report submitted by Y. Bhanu Prakash to GITAM Institute of Management in partial fulfillment of the degree of Bachelor of Business Administration in Business Analytics. The report is on the topic of web analytics with reference to select sports websites. It includes declarations by the student and certification by the guide, as well as acknowledgements. The report will consist of 5 chapters - an introduction to analytics, a profile of Alexa.com, methodology, analysis and interpretation of data, and observations and conclusions.
This document discusses open data for digital development in Botswana. It outlines the importance of open data for transparency, participation, innovation and economic opportunities. It analyzes Botswana's open data readiness and compares it to international best practices from Korea. The document proposes establishing an open data portal and policy framework in Botswana to stimulate applications, startups and broadband usage to support digital development and economic diversification.
Big data analytics (BDA) provides capabilities for revealing additional value from big data. It examines large amounts of data from various sources to deliver insights that enable real-time decisions. BDA is different from data warehousing and business intelligence systems. The complexity of big data systems required developing specialized architectures like Hadoop, which processes large amounts of data in a timely and low cost manner. Big data challenges include capturing, storing, analyzing, sharing, transferring, visualizing, querying, updating, and ensuring privacy of large and diverse datasets.
LEVERAGING CLOUD BASED BIG DATA ANALYTICS IN KNOWLEDGE MANAGEMENT FOR ENHANCE...ijdpsjournal
In recent past, big data opportunities have gained much momentum to enhance knowledge management in
organizations. However, big data due to its various properties like high volume, variety, and velocity can
no longer be effectively stored and analyzed with traditional data management techniques to generate
values for knowledge development. Hence, new technologies and architectures are required to store and
analyze this big data through advanced data analytics and in turn generate vital real-time knowledge for
effective decision making by organizations. More specifically, it is necessary to have a single infrastructure
which provides common functionality of knowledge management, and flexible enough to handle different
types of big data and big data analysis tasks. Cloud computing infrastructures capable of storing and
processing large volume of data can be used for efficient big data processing because it minimizes the
initial cost for the large-scale computing infrastructure demanded by big data analytics. This paper aims to
explore the impact of big data analytics on knowledge management and proposes a cloud-based conceptual
framework that can analyze big data in real time to facilitate enhanced decision making intended for
competitive advantage. Thus, this framework will pave the way for organizations to explore the relationship
between big data analytics and knowledge management which are mostly deemed as two distinct entities.
LEVERAGING CLOUD BASED BIG DATA ANALYTICS IN KNOWLEDGE MANAGEMENT FOR ENHANCE...ijdpsjournal
In recent past, big data opportunities have gained much momentum to enhance knowledge management in
organizations. However, big data due to its various properties like high volume, variety, and velocity can
no longer be effectively stored and analyzed with traditional data management techniques to generate
values for knowledge development. Hence, new technologies and architectures are required to store and
analyze this big data through advanced data analytics and in turn generate vital real-time knowledge for
effective decision making by organizations. More specifically, it is necessary to have a single infrastructure
which provides common functionality of knowledge management, and flexible enough to handle different
types of big data and big data analysis tasks. Cloud computing infrastructures capable of storing and
processing large volume of data can be used for efficient big data processing because it minimizes the
initial cost for the large-scale computing infrastructure demanded by big data analytics. This paper aims to
explore the impact of big data analytics on knowledge management and proposes a cloud-based conceptual
framework that can analyze big data in real time to facilitate enhanced decision making intended for
competitive advantage. Thus, this framework will pave the way for organizations to explore the relationship
between big data analytics and knowledge management which are mostly deemed as two distinct entities.
LEVERAGING CLOUD BASED BIG DATA ANALYTICS IN KNOWLEDGE MANAGEMENT FOR ENHANC...ijdpsjournal
In recent past, big data opportunities have gained much momentum to enhance knowledge management in
organizations. However, big data due to its various properties like high volume, variety, and velocity can
no longer be effectively stored and analyzed with traditional data management techniques to generate
values for knowledge development. Hence, new technologies and architectures are required to store and
analyze this big data through advanced data analytics and in turn generate vital real-time knowledge for
effective decision making by organizations. More specifically, it is necessary to have a single infrastructure
which provides common functionality of knowledge management, and flexible enough to handle different
types of big data and big data analysis tasks. Cloud computing infrastructures capable of storing and
processing large volume of data can be used for efficient big data processing because it minimizes the
initial cost for the large-scale computing infrastructure demanded by big data analytics. This paper aims to
explore the impact of big data analytics on knowledge management and proposes a cloud-based conceptual
framework that can analyze big data in real time to facilitate enhanced decision making intended for
competitive advantage. Thus, this framework will pave the way for organizations to explore the relationship
between big data analytics and knowledge management which are mostly deemed as two distinct entities.
Higher education institutions now a days are operating in an increasingly complex and
competitive environment. The application of innovation is a must for sustaining its competitive advantage.
Institution leaders are using data management and analytics to question the status quo and develop effective
solutions. Achieving these insights and information requires not a single report from a single system, but
rather the ability to access, share, and explore institution-wide data that can be transformed into meaningful
insights at every level of the institution. Consequently, institutions are facing problems in providing necessary
information technology support for fulfilling excellence in performance. More specifically, the best practices
of big data management and analytics need to be considered within higher education institutions. Therefore,
the study aimed at investigating big data and analytics, in terms of: (1) definition; (2) its most important
principles; (3) models; and (4) benefits of its use to fulfill performance excellence in higher education
institutions. This involves shedding light on big data and analytics models and the possibility of its use in
higher education institutions, and exploring the effect of using big data and analytics in achieving performance
excellence. To reach these objectives, the researcher employed a qualitative research methodology for
collecting and analyzing data. The study concluded the most important result, that there is a significant
relationship between big data and analytics and excellence of performance as big data management and
analytics mainly aims at achieving tasks quickly with the least effort and cost. These positive results support
the use of big data and analytics in institutions and improving knowledge in this field and providing a practical
guide adaptable to the institution structure. This paper also identifies the role of big data and analytics in
institutions of higher education worldwide and outlines the implementation challenges and opportunities in the
education industry.
A Review of Big Data Analytics in Sector of Higher EducationIJERA Editor
This paper is about the use of big data analytics in higher education. In this paper, we see what the big data is and where does it come from. We will also try to find why the big data analytics has become a buzzword in almost every sector today through our literature review on the big data analytics and its applications in higher education sector. Then we see what the big educational data is, how it is generated and analyzed. We found that the two most important types of analytics are- Learning and academic analytics which will be discussed. Several papers describe the benefits of implementation of analytics in the education sector and the opportunities provided which will be discussed in this paper. We also found that the basic characteristics such as size, speed, variety and some other factors are responsible for some issues and challenges to the use of analytics in this sector. We will discuss those issues and challenges and discuss some proposed solutions to address them.
Learning Resources Week 2
Frankfort-Nachmias, C., & Leon-Guerrero, A. (2020). Social statistics for a diverse society (9th ed.). Sage Publications.
· Chapter 2, “The Organization and Graphic Presentation Data” (pp. 27-74)
Wagner, III, W. E. (2020). Using IBM® SPSS® statistics for research methods and social science statistics (7th ed.). Thousand Oaks, CA: Sage Publications.
· Chapter 5, “Charts and Graphs”
· Chapter 11, “Editing Output”
Walden University Writing Center. (n.d.). General guidance on data displays. Retrieved from http://waldenwritingcenter.blogspot.com/2013/02/general-guidance-on-data-displays.html
Use this website to guide you as you provide appropriate APA formatting and citations for data displays.
Laureate Education (Producer). (2016j). Visual displays of data [Video file]. Baltimore, MD: Author.
Note: The approximate length of this media piece is 9 minutes.
In this media program, Dr. Matt Jones discusses frequency distributions. Focus on how his explanation might support your analysis in this week’s Assignment. (video attached separately)
sustainability
Case Report
Integrated Understanding of Big Data, Big Data
Analysis, and Business Intelligence: A Case Study
of Logistics
Dong-Hui Jin and Hyun-Jung Kim *
Seoul Business School, aSSIST, 46 Ewhayeodae 2-gil, Seodaemun-gu, Seoul 03767, Korea; [email protected]
* Correspondence: [email protected]; Tel.: +82-70-7012-2722
Received: 5 October 2018; Accepted: 17 October 2018; Published: 19 October 2018
����������
�������
Abstract: Efficient decision making based on business intelligence (BI) is essential to ensure
competitiveness for sustainable growth. The rapid development of information and communication
technology has made collection and analysis of big data essential, resulting in a considerable increase
in academic studies on big data and big data analysis (BDA). However, many of these studies are
not linked to BI, as companies do not understand and utilize the concepts in an integrated way.
Therefore, the purpose of this study is twofold. First, we review the literature on BI, big data,
and BDA to show that they are not separate methods but an integrated decision support system.
Second, we explore how businesses use big data and BDA practically in conjunction with BI through
a case study of sorting and logistics processing of a typical courier enterprise. We focus on the
company’s cost efficiency as regards to data collection, data analysis/simulation, and the results from
actual application. Our findings may enable companies to achieve management efficiency by utilizing
big data through efficient BI without investing in additional infrastructure. It could also give them
indirect experience, thereby reducing trial and error in order to maintain or increase competitiveness.
Keywords: business application; big data; big data analysis; business intelligence; logistics;
courier service
1. Introduction
A growing number of corporations depend on various and cont.
Learning Resources Week 2
Frankfort-Nachmias, C., & Leon-Guerrero, A. (2020). Social statistics for a diverse society (9th ed.). Sage Publications.
· Chapter 2, “The Organization and Graphic Presentation Data” (pp. 27-74)
Wagner, III, W. E. (2020). Using IBM® SPSS® statistics for research methods and social science statistics (7th ed.). Thousand Oaks, CA: Sage Publications.
· Chapter 5, “Charts and Graphs”
· Chapter 11, “Editing Output”
Walden University Writing Center. (n.d.). General guidance on data displays. Retrieved from http://waldenwritingcenter.blogspot.com/2013/02/general-guidance-on-data-displays.html
Use this website to guide you as you provide appropriate APA formatting and citations for data displays.
Laureate Education (Producer). (2016j). Visual displays of data [Video file]. Baltimore, MD: Author.
Note: The approximate length of this media piece is 9 minutes.
In this media program, Dr. Matt Jones discusses frequency distributions. Focus on how his explanation might support your analysis in this week’s Assignment. (video attached separately)
sustainability
Case Report
Integrated Understanding of Big Data, Big Data
Analysis, and Business Intelligence: A Case Study
of Logistics
Dong-Hui Jin and Hyun-Jung Kim *
Seoul Business School, aSSIST, 46 Ewhayeodae 2-gil, Seodaemun-gu, Seoul 03767, Korea; [email protected]
* Correspondence: [email protected]; Tel.: +82-70-7012-2722
Received: 5 October 2018; Accepted: 17 October 2018; Published: 19 October 2018
����������
�������
Abstract: Efficient decision making based on business intelligence (BI) is essential to ensure
competitiveness for sustainable growth. The rapid development of information and communication
technology has made collection and analysis of big data essential, resulting in a considerable increase
in academic studies on big data and big data analysis (BDA). However, many of these studies are
not linked to BI, as companies do not understand and utilize the concepts in an integrated way.
Therefore, the purpose of this study is twofold. First, we review the literature on BI, big data,
and BDA to show that they are not separate methods but an integrated decision support system.
Second, we explore how businesses use big data and BDA practically in conjunction with BI through
a case study of sorting and logistics processing of a typical courier enterprise. We focus on the
company’s cost efficiency as regards to data collection, data analysis/simulation, and the results from
actual application. Our findings may enable companies to achieve management efficiency by utilizing
big data through efficient BI without investing in additional infrastructure. It could also give them
indirect experience, thereby reducing trial and error in order to maintain or increase competitiveness.
Keywords: business application; big data; big data analysis; business intelligence; logistics;
courier service
1. Introduction
A growing number of corporations depend on various and cont.
sustainabilityCase ReportIntegrated Understanding of B.docxdeanmtaylor1545
sustainability
Case Report
Integrated Understanding of Big Data, Big Data
Analysis, and Business Intelligence: A Case Study
of Logistics
Dong-Hui Jin and Hyun-Jung Kim *
Seoul Business School, aSSIST, 46 Ewhayeodae 2-gil, Seodaemun-gu, Seoul 03767, Korea; [email protected]
* Correspondence: [email protected]; Tel.: +82-70-7012-2722
Received: 5 October 2018; Accepted: 17 October 2018; Published: 19 October 2018
!"#!$%&'(!
!"#$%&'
Abstract: Efficient decision making based on business intelligence (BI) is essential to ensure
competitiveness for sustainable growth. The rapid development of information and communication
technology has made collection and analysis of big data essential, resulting in a considerable increase
in academic studies on big data and big data analysis (BDA). However, many of these studies are
not linked to BI, as companies do not understand and utilize the concepts in an integrated way.
Therefore, the purpose of this study is twofold. First, we review the literature on BI, big data,
and BDA to show that they are not separate methods but an integrated decision support system.
Second, we explore how businesses use big data and BDA practically in conjunction with BI through
a case study of sorting and logistics processing of a typical courier enterprise. We focus on the
company’s cost efficiency as regards to data collection, data analysis/simulation, and the results from
actual application. Our findings may enable companies to achieve management efficiency by utilizing
big data through efficient BI without investing in additional infrastructure. It could also give them
indirect experience, thereby reducing trial and error in order to maintain or increase competitiveness.
Keywords: business application; big data; big data analysis; business intelligence; logistics;
courier service
1. Introduction
A growing number of corporations depend on various and continuously evolving methods of
extracting valuable information through big data and big data analysis (BDA) for business intelligence
(BI) to make better decisions. The term “big data” refers to large amounts of information or data at
a certain point in time and within a particular scope. However, big data have a short lifecycle with
rapidly decreasing effective value, which makes it difficult for academic research to keep up with their
fast pace. In addition, big data have no limits regarding their type, form, or scale, and their scope is
too vast to narrow them down to a specific area of study.
Big data can also simply refer to a huge amount of complex data, but their type, characteristics,
scale, quality, and depth vary depending on the capabilities and purpose of each company.
The same holds for the reliability and usability of the results gathered from analysis of the data.
Previous studies generally agree on three main properties that define big data, namely, volume,
velocity, and variety, or the “3Vs” [1–4], which have recently been expanded to “5Vs” with the ad.
sustainabilityCase ReportIntegrated Understanding of B.docxmabelf3
sustainability
Case Report
Integrated Understanding of Big Data, Big Data
Analysis, and Business Intelligence: A Case Study
of Logistics
Dong-Hui Jin and Hyun-Jung Kim *
Seoul Business School, aSSIST, 46 Ewhayeodae 2-gil, Seodaemun-gu, Seoul 03767, Korea; [email protected]
* Correspondence: [email protected]; Tel.: +82-70-7012-2722
Received: 5 October 2018; Accepted: 17 October 2018; Published: 19 October 2018
!"#!$%&'(!
!"#$%&'
Abstract: Efficient decision making based on business intelligence (BI) is essential to ensure
competitiveness for sustainable growth. The rapid development of information and communication
technology has made collection and analysis of big data essential, resulting in a considerable increase
in academic studies on big data and big data analysis (BDA). However, many of these studies are
not linked to BI, as companies do not understand and utilize the concepts in an integrated way.
Therefore, the purpose of this study is twofold. First, we review the literature on BI, big data,
and BDA to show that they are not separate methods but an integrated decision support system.
Second, we explore how businesses use big data and BDA practically in conjunction with BI through
a case study of sorting and logistics processing of a typical courier enterprise. We focus on the
company’s cost efficiency as regards to data collection, data analysis/simulation, and the results from
actual application. Our findings may enable companies to achieve management efficiency by utilizing
big data through efficient BI without investing in additional infrastructure. It could also give them
indirect experience, thereby reducing trial and error in order to maintain or increase competitiveness.
Keywords: business application; big data; big data analysis; business intelligence; logistics;
courier service
1. Introduction
A growing number of corporations depend on various and continuously evolving methods of
extracting valuable information through big data and big data analysis (BDA) for business intelligence
(BI) to make better decisions. The term “big data” refers to large amounts of information or data at
a certain point in time and within a particular scope. However, big data have a short lifecycle with
rapidly decreasing effective value, which makes it difficult for academic research to keep up with their
fast pace. In addition, big data have no limits regarding their type, form, or scale, and their scope is
too vast to narrow them down to a specific area of study.
Big data can also simply refer to a huge amount of complex data, but their type, characteristics,
scale, quality, and depth vary depending on the capabilities and purpose of each company.
The same holds for the reliability and usability of the results gathered from analysis of the data.
Previous studies generally agree on three main properties that define big data, namely, volume,
velocity, and variety, or the “3Vs” [1–4], which have recently been expanded to “5Vs” with the ad.
Full Paper: Analytics: Key to go from generating big data to deriving busines...Piyush Malik
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DEVELOPING AN INTEGRATED FRAMEWORK TO UTILIZE BIG DATA FOR HIGHER EDUCATION I...ijcsit
This document summarizes a study on developing an integrated framework to utilize big data in higher education institutions in Saudi Arabia. The study collected data through surveys and interviews at three universities in Riyadh. The results identified several key factors that influence the adoption of big data technology in universities. Environmental factors like lack of awareness and supportive policies were influential. Technological factors like perceived benefits, simplicity of use, security, and integration were also important. The study aims to help Saudi universities better utilize big data to support decision-making and improve performance.
This document summarizes a study on developing an integrated framework to utilize big data in higher education institutions in Saudi Arabia. The study collected data through surveys and interviews at three universities in Riyadh. The results identified several key factors that influence the adoption of big data technology in universities. Environmental factors like lack of awareness and supportive policies were influential. Technological factors like perceived benefits, simplicity of use, security, and integration were also important. The study aims to help Saudi universities better utilize big data to support decision-making and improve performance.
The Big data concept emerged to meet the growing demands in analysing large
volumes of fast moving, heterogeneous and complex data, which traditional data
analysis systems could not manage further. The application of big data technology
across various sectors of the economy has aided better utilization of multiple data
collated and hence decision making. Organizations no longer base operations on
assumptions or constructed models solely, but can make inferences from generated
data. Educational organizations are more efficient and the pedagogical processes
more effective, when multiple streams of data can be collated from the various
personnel and facilitators involved. This data when analysed, maximizes the
performance of administrators andrecipients alike. This paper looks at the
components and techniques in bigdata technology, and how it can be implemented in
the education system for effective administration and delivery
This document provides an overview of big data, including its definition, characteristics, examples, analysis methods, and challenges. It discusses how big data is characterized by its volume, variety, and velocity. Examples of big data are given from various industries like healthcare, retail, manufacturing, and web/social media. Analysis methods for big data like MapReduce, Hadoop, and HPCC are described and compared. The document also covers privacy and security issues that arise from big data analytics.
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This document discusses big data implementation in organizations and the potential opportunities and challenges. It begins by defining big data and explaining the key drivers of increasing data volumes, such as social media, the internet of things, and multimedia data. It then outlines some of the major benefits organizations can gain from big data, including improved decision making, customer experiences, and sales. However, successfully implementing big data also faces roadblocks such as selecting the right tools, techniques, and data sources. The document provides examples of technologies, methods, and skills needed to optimize big data initiatives.
A gigantic archive of terabytes of information is created every day from current data frameworks and computerized advances, for example, Internet of Things and distributed computing. Examination of these gigantic information requires a ton of endeavors at various levels to extricate information for dynamic. Hence, huge information examination is an ebb and flow region of innovative work. The essential goal of this paper is to investigate the likely effect of huge information challenges, and different instruments related with it. Accordingly, this article gives a stage to investigate enormous information at various stages. Moreover, it opens another skyline for analysts to build up the arrangement, in light of the difficulties and open exploration issues.
Data mining technique has a key role in knowledge
extraction from databases to promote efficient decision making.
This paper presents an approach for knowledge extraction from
a sample database of some school dropped students using
association rule generation and classification algorithms to
demonstrate how knowledge-based development policy making
decisions can be processed from the extracted knowledge. A
system architecture is proposed considering mobile computing
devices as user interface to the system connecting mass people
database with cloud computing environment resources. The
causes of education termination are investigated by analyzing the
sample database in terms of attribute value relationship in the
form of association rules to reason about the causes based on the
computed support and confidence. It is observed that if the
affected family had no service holders, the dropped student had
to stop his education because of financial problem. Classification
is applied to classify the dropped students in different groups
based on their level of education.
Big data refers to huge set of data which is very common these days due to the increase of internet utilities. Data generated from social media is a very common example for the same. This paper depicts the summary on big data and ways in which it has been utilized in all aspects. Data mining is radically a mode of deriving the indispensable knowledge from extensively vast fractions of data which is quite challenging to be interpreted by conventional methods. The paper mainly focuses on the issues related to the clustering techniques in big data. For the classification purpose of the big data, the existing classification algorithms are concisely acknowledged and after that, k-nearest neighbour algorithm is discreetly chosen among them and described along with an example.
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Big Data Analytics in Higher Education: A Review
1. The International Journal of Engineering and Science (IJES)
|| Volume || 6 || Issue || 6 || Pages || PP 14-21 || 2017 ||
ISSN (e): 2319 – 1813 ISSN (p): 2319 – 1805
DOI: 10.9790/1813-0606021421 www.theijes.com Page 14
Big Data Analytics in Higher Education: A Review
Julius Murumba1
, Elyjoy Micheni2
1,2
Department of Management Science and Technology, Technical University of Kenya
--------------------------------------------------------ABSTRACT-----------------------------------------------------------
Big Data provides an opportunity to educational Institutions to use their Information Technology resources
strategically to improve educational quality and guide students to higher rates of completion, and to improve
student persistence and outcomes. This paper explores the attributes of big data that are relevant to educational
institutions, investigates the factors influencing adoption of big data and analytics in learning institutions and
seeks to establish the limiting factors hindering use of big data in Institutions of higher learning. The study has
been conducted through a desk search and reviewed sources of literature including scientific research journals
and reports. The paper is based on desk research. The sources of literature that were reviewed included
scientific research articles and journals, conference reports and theses. Online journals found on the internet
were also examined with the search being broadened by Google Scholar where the following keywords were
used “big data”, “developing countries”, “education systems” and “clustering”. The paper concludes that Big
Data is important since it offers Universities opportunities to their Information Technology resources
strategically to improve educational quality and guide students, colleges and universities see value in analytics;
and therefore recommends that these institutions carry out investments in analytics programs and in people to
have relevant data science. This is because Big Data can afford educational institutions opportunities to shape a
modern and dynamic education system, in which every individual student can have the maximum benefit from,
and can greatly contribute towards improving the quality of education
Keywords: Big Data, Analytics, Higher Education
-------------------------------------------------------------------------------------------------------------------------
Date of Submission: 25 May 2017 Date of Accepted: 16 June 2017
--------------------------------------------------------------------------------------------------------------------------------------
I. INTRODUCTION
Information Technology is a key enabler of many of today’s technological innovations that have led to
improvements that benefit society in many areas including education, business, security and health care.
Technological advancements in digital technologies have led to an increase in the number of people accessing
and using digital devices worldwide. [1] States that digital connectivity in Africa is driven be greater ownership
of mobile devices, and is the driving force for the technological revolution on the African continent.
Technological innovations, increased affordability and use of digital devices have led to a situation where
extremely large amounts of data are being generated through use of these devices worldwide. Big Data Analytics
can therefore help institutions of higher learning make use of data that is generated in their ecosystems. A report
by [2], indicates that smart phone ownership rates in emerging and developing nations are rising at an
extraordinary rate, climbing from a median of 21% in 2013 to 37% in 2015; and that overwhelming majorities in
many countries surveyed reported owning some form of mobile device, even if they are not considered smart
phones. A good example is Kenya, where smart phones have become increasingly common, while food is
unfortunately scarce [3]. This trend is expected to continue, with data being rapidly generated and collected at a
tremendous rate and will therefore contribute to towards big data. This pool of data when harnessed has the
potential of helping to solve the statistical tragedy that has characterized many developing countries, and crises
that are related to unreliable data, particularly in Africa. Big Data refers to data sets that are large enough to pose
significant challenges when using commonly available or traditional tools and infrastructures to collect, manage,
process and analyze the data within a tolerable amount of time. [4] Points out that Big Data is concerned with
emergent suit of technologies that can process mass volumes of data of various types at faster speeds than ever
before. [5, 6] observe that the attributes of big data are best described as high-volume, high-velocity and high-
variety information assets which by their nature demand cost-effective, innovative forms of information
processing that can enable enhanced insight and decision making. Big Data therefore changes the way we
approach data analysis, inspiring entirely new families of information services and necessitating new processing
models and knowledge representations. Big Data consists of extensive datasets; primarily in the characteristics of
volume, variety, velocity, and/or variability that require a scalable architecture for efficient storage,
manipulation, and analysis [7]. [8] Defines big data as distribution of data systems across horizontally-coupled
2. The International Journal of Engineering and Science
DOI: 10.9790/1813-0606021421 www.theijes.com Page 15
independent resources to achieve the scalability needed for the efficient processing of extensive datasets. [9]
Describes Big Data as data that is too big in terms of volume, and moves too fast in terms of its acquisition and
analysis speed that normally exceeds the processing capacity of conventional database management systems. Big
Data is identified by a number of fundamental characteristics which include the following 1) Volume: which
refers to colossal amounts of data collected that present a challenge to conventional information technology
structures to store, process analyze and present. 2) Velocity: refers to the rapid pace at which information is
generated and flows within an organization. The velocity dimension is also concerned with the capability of an
organization understanding and responding to events as they occur [10]. 3) Variety: which refers to data in
diverse formats both structured and unstructured. This variety of data formats come from interaction between
people and the Information Technology systems such as sensors and web applications. 4) Veracity: which refers
to the quality and authenticity of the data, and looks at how data that is being stored is meaningfully mined to
address the problem being analyzed 5) Value: refers to the relevance of the data to Big Data Analytics and extent
to which the data is utilized to generate value of the insights, benefits and business processes within an
organization [4, 8, 11, 12, 13]. Big data comprises large and complex data sets which are difficult to process
using traditional data processing applications and common database management tools.
II. OBJECTIVES
The paper was guided by the following specific objectives
1. To highlight the attributes of big data that are relevant to educational institutions
2. Investigate the factors influencing adoption of big data and analytics in learning institutions
3. Establish the factors limiting adoption and use of big data by Institutions of higher learning.
III. METHODOLOGY
This paper is based on desk research. The sources of literature that were reviewed included scientific research
articles and journals, conference reports and theses. Online journals found on the internet were also examined
with the search being broadened by Google Scholar where the following keywords were used “big data”,
“developing countries” and “education systems”.
IV. NEED FOR BIG DATA IN ACADEMIA
Big data analytics is actively being used in many business organizations worldwide in business intelligence and
in areas such as marketing and financial forecasting. In recent years Big Data has started attracting the interest of
academia. Many academic institutions are moving to cloud architectures and with the increased use of digital
devices by users in these ecosystems is leading to a situation more data is being collected in these institutions
than ever before, creating considerable opportunities for using Big Data to analyze and correlate information that
enhance decision making. [14] Observed that it is absolutely important for universities to use Big Data Analytics
in order to deliver the very best of learning environments for the good of society. Possibilities for using Big Data
to link research to education exist, and are achievable when latest research practices and outcomes are better
used to inform teaching and in undertaking research activities as part of education. [4] notes that research into
big data in higher education, especially in developing countries is limited despite growing interests in exploring
and unlocking the value of the increasing data within higher education environment This paper seeks to
contribute to the theoretical understanding of Big Data and Analytics within educational institutions of
developing nations.
Big Data presents to Institutions a good framework for efficiently utilizing the vast array of data in shaping the
future of higher education [4]. [4] Citing [15] states that Big Data application in higher education is attributed to
technological innovations and developments; which have catalyzed the growth of analytics in higher education.
According to [16], Data Warehouses and Cloud Computing coupled with greater ownership of digital devices by
end users in the educational ecosystem are making it possible to collect, manage and maintain massive amounts
of data. These Information Technologies are important resources that when exploited by policy makers are
useful in driving institutional strategy and policy making for the future. Information Technology makes available
sophisticated platforms that provide computing power necessary for ploughing through massive amounts of data,
and turning these masses into meaningful information. Data mining technologies when applied use descriptive
and inferential statistics to derive patterns from the massive amounts of collected data for actionable information
[17]. An illustration of Big Data Environment is presented by figure 2 below. Big Data Analytics is relevant in
addressing a significant number of pressing issues for education systems [14] , key among them are 1) increasing
educator effectiveness; 2) harnessing insights from learning experiences; 3) delivering education for all that may
also be tailored for individual learners needs; and 4) equipping students with relevant skills for their future
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careers. Institutions of higher learning are faced with pressure to improve quality of learning outcomes while
cutting costs and to build new value relationships with students. This is achievable when these institutions have
the capability of leveraging the data collected during the enrollment stages, finance sourcing and instructional
process to support greater learning outcomes. Big Data technologies have been revolutionized by innovations in
database technologies, computer hardware, especially memory and storage capacities, and increased availability
of network bandwidth. According to [19], The Hadoop ecosystem which includes Pig, Hive, Mahout, and
RHadoop is a good example of the Big Data technologies that can be deployed in educational systems. Hadoop
and NoSQL databases are capable of enabling analysis of large heterogeneous datasets at unprecedented speeds.
[20] Explain that Hadoop is an open-source framework for distributed computing that enables processing of
large datasets through horizontal scalability. The Apache Hadoop project for instance develops open-source
software for reliable, scalable and distributed computing. In fact, organizations such as Yahoo, Google, and
Face book have invested in the Apache Hadoop Project with a view of addressing their Big Data needs. The
Apache Hadoop software library framework allows distributed processing of large data sets across clusters of
computers through simple programming models, and is designed to scale up from single machines to multiple
machines each offering local computation and storage [13, 19].The Education sector is nowadays becoming
more technology oriented. The digital revolution in developing countries is leading to development of new
technologies such as ubiquitous computing devices and the Massive Open Online Courses all of which are
radically transforming the mode and accessibility to teaching and learning [4]. These Massive open online
courses (MOOC) are generating huge amounts of data that are relevant for Big Data Analytics [21]. [22]
Underscores this by stating that the era of cloud and mobile computing is opening up many opportunities for
revolutionizing education. Massive open online courses (MOOC) are a new phenomenon in higher education in
which free online courses, open to an unlimited number of students are offered by Institutions of higher learning.
MOOC describes courses offered fully online where learners can obtain certificates upon completion of the
courses they enrolled in, and offers a paradigm shift in the delivery of learning. The virtual classroom is another
important tool on which distance learning programs are based upon, and on which instructors can share learning
material through the webinar. These tools, together with other learning management systems generate a lot of
data which can be stored and analyzed for use in forecasting [21]. In such cases Big Data Analytics is useful in
enabling institutions using such technologies to derive value from these sources of data. Improvements in
Internet accessibility mean that big data analytics can be applied to help reduce the costs of education and
improve student performances by examining and offering customized and self paced learning solutions for
learners [23]. [24] Argue that higher education institutions that focus on better instructional quality and the
acquisition and retention of students and faculty historically have used data warehouses and business intelligence
tools to report on and analyze data. Big Data analytics has the potential to positively impact all the major areas
that are of importance for an institution of learning; in areas such as student enrollment and retention, integrated
information management and reporting, operational cost management, regulatory compliance and research.
According to report by [18], through analytics learning institutions can perform thorough analyses of student and
learning data to make informed decisions on future course offerings in order to cater for the needs of potential
and existing students.
Figure 2: Analytics Driven Conceptual Framework: Adapted from [46]
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V. BIG DATA ANALYTICS
According to [7], Information and Communication Technologies have become indispensable tools for supporting
collaborative research work, and for this reason the critical role they play in supporting distributed teams can be
enhanced with improvements in networking technologies and the development of tools and applications capable
of supporting digital communications, access to and distribution of information. Information Technologies
provide opportunities to the younger researchers and those who were not in major universities to participate in
discussions with established researchers and those from prominent universities leading to an increased network
of researchers and wider participation [12]. [13] States that new Information Technologies enable new
communication structures that have radically reduced constraints of distance and time, and have also enabled
creation of new and unique environments capable of supporting research and teaching. These new and emerging
technologies afford university communities numerous opportunities to meet the challenge of the knowledge
economy, offering new and novel possibilities for creating learning environments and applying knowledge in
new ways in order to meet changing societal needs. The new Information Technology based infrastructures offer
new possibilities for sharing of facilities and enables new forms of distributed organization. According to [13],
the major components of the Information Technologies infrastructure in universities are: 1) Traditional packet
switching networks and the newer point-to-point optical networks. 2). Middleware that enables integration of
various technologies and makes it much easier to build university specific or inter-institutional virtual
organizations in efficient, secure, and trustful ways. 3). Data and knowledge management services supported by
vast networks of digital libraries, data sets, and archives providing content and sustainable knowledge
management services. 4) High performance computation services capable of simulating complex phenomena
such as galaxy formation or social-physical models of global warming. 5) User Interfaces and visualization
services to support interaction between humans and the Information Technology environments. 6) Computer
supported collaborative work to enable collaborative services to support and enable distributed teams to work
together in ways similar to those in physical proximity. Various computer resources such as computer desktops,
storage media, data and various input and output devices that are scattered across the university landscape can be
connected via internetworking links through grid computing. Collaborative Research systems therefore can be
presented as an amalgamation of grid computing technologies and research support systems in which Grid
Computing functionalities are incorporated into Research systems. This way the collaborative Research Grid can
be viewed as collection of computational resources on demand to match computational needs through generic
service matchmaking on the Web.
VI. FACTORS INFLUENCING BIG DATA ANALYTICS IN EDUCATION
The decreasing costs of big data storage, open source software such as Apache Hadoop, NoSQL databases,
network bandwidth and on-demand access to resources through cloud computing are bringing these complex
technologies close to nearly everyone [23]. [30] Underscore this by stating that while the cost of Big Data and
Analytic tools is coming down, they are becoming much easier to use. This is in turn opening up opportunities
for use of these tools by enterprises and educational institutions to achieve better outcomes and more efficient
use of resources. Expectations of good practices of accountability by stakeholders and increasing demands for
evidence based guidelines to support decision-making are among the factors that are contributing to the
emergence of Big Data in higher education. Therefore, finding appropriate technology platforms that will enable
big data analytics and the relevant technological skills e.g. data scientists is the first step in providing a means to
this end. This is because data can no longer be viewed as a back office accounts settling tool but rather a real
time decision making tool that can be used by data scientists to derive useful information that would otherwise
remain hidden in the terabytes of data [16]. Information Technology is reshaping education and expanding the
education ecosystem beyond traditional lecture halls and classrooms to accommodate learners' who need
flexibility in terms of time and distance through virtual and online learning systems [6]. The educational system
is increasingly coming under pressure to respond to economic, political and social changes such as the need to
increase the number of students in certain disciplines, and training graduates with skills and attributes required
by industry [4]. For these institutions to be responsive to these changes, it is necessary that they use Information
Technologies such as Big Data which has considerable opportunities in higher education. Educational data has in
the past been gathered through traditional assessments mainly but is now increasingly being gathered
throughonline educational systems, educational games and simulations leading to lots of data, which are in turn
creating opportunities for Big data analytics. [17] states that in the context of today’s technology enabled
learning, the student is at the center of a network of information rich connections in the education system, and
this is creating opportunities for Big Data analytics. [14] opines that institutions of higher education are
operating in increasingly complex environments, and therefore it is imperative that they use Big Data in order to
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deliver the best learning environments for the benefit of society. Possibilities exist for using Big Data to link
research to education, by making good use of latest research practices and outcomes to inform teaching and to
enable undertaking of research activities as part of education. In the course of learning processes, students
generate massive volumes of potentially valuable information in ares such as course selection and registration,
financial informration, class participation, online resource usage, and textbook purchases, all of which are raw
material for Big Data, and can be analyzed and redeployed to help administrators and students make better
decisions. Institutions of higher education are collecting more data than ever, as a consequence of the digital
revolution.
Many colleges and universities have demonstrated that analytics can help significantly advance an institution in
such strategic areas as resource allocation, student success, and finance. Leaders of Institutions of higher
education are looking into ways of initiating analytic programs in their institutions when they learn about
transformations occurring at other institutions through big data analytics [31]. The McKinsey Global Institute
Report on Big Data [33], states that the most developed regions such as Europe have had the biggest potential to
create value through the use of big data. This is an encouragement for adoption and widespread use of big data in
other regions especially developing nations. According to [32], the diffusion of big data provides an account of
how Big Data moves from invention to widespread use and is facilitated by measures taken by service providers
of key technologies needed to strengthen the resources and capabilities of academic institutions. [34] States that
developing nations can tap into the many opportunities for big data that are available to create value from the
massive amounts of data generated and in the long run help in their development. Big Data and analytics in
higher education can be transformative, altering the existing processes of administration, teaching and learning
and contribute to policy outcomes by helping address existing challenges facing educational systems [4, 35].
Successful Institutions have used big Data in the following ways 1) Creating a culture of completion and
outplacement. 2) Reducing non productive credits. 3) Redesigning delivery of instruction. 4) Redesigning core
support services such as human resources, academic services and finance to produce strategically useful data.
and 5) Optimizing non core services and operations [17].
VII.BIG DATA IMPLICATION FOR EDUCATION IN DEVELOPING COUNTRIES
Many in development cite Big data as key to successfully implementing the Sustainable Development Goals.
Digital learning is on the increase in developing nations, and is resulting in collection of massive data useful for
analytics which can contribute to teaching and learning. Many students participate in social networks, online and
mobile learning where new data are created. These new data are helping the students with the different
background to correlate among themselves and is helping them understand core course concepts, and to expand
their knowledge and skills [16]. These new computer supported interactive learning methods and tools often
include intelligent tutoring systems, simulations and games which are capable of being used to create
opportunities to collect and analyze student data from which patterns and trends can be discovered [36]. Big data
concepts and analytics can thus be applied to various administrative and instructional applications, including
recruitment and admissions processing, financial planning, donor tracking, and student performance monitoring
[37].
According to [38] there is anticipation that in the developing world, more learning content will be delivered
online through Virtual Learning Environments and supported by other digital systems including social media, in
turn increasing the velocity of new student data. Tight budgets will however continue to impact higher education
negatively in the developing nations leading to an increased focus on productivity and flexibility for institutions
and students. According to [39], there are likely to be attempts to implement outcome based funding with major
changes demanded in accountability, governance, core standards, completion rates and workforce development.
Big data analytics has the potential to help administrators and teachers in developing nations make better
judgements through data driven decision making supported by mining of large amounts of data [41]. [16] Noted
that the use of data to guide instruction leads to significantly positive academic effects which can also be adopted
in developing nations. Learning analytics is an area of research and application and is related to academic
analytics, action analytics, and predictive analytics which have implication for academic systems. Learning
analytics is related to educational data mining, and is an emerging research area with a series of computational
and psychological methods which researchers can exploit to understand how students learn. According to [36], a
key application of learning analytics is in identifying students at risk of failing a course or program of study, and
in monitoring and predicting students learning performance and spotting potential issues early in time for
interventions to be provided.
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VIII. BIG DATA CHALLENGES IN EDUCATIONAL INSTITUTIONS
Analytics is a growing interest in many educational Institutions with many believing in its importance for the
success of higher education. To improve the quality of learning outcomes, it is necessary that large volume of
data generated by educational systems should be analysed effectively to facilitate appropriate responses to new
challenges. Barriers to Big Data in Education generally include lack of executive vision, Inability to associate
important business problems with big data solutions , Users or executives rooted in a old technologies, Cost ,
Lack of data warehouses and analytical tools, Data quality issues and simply leaving most data to be uncollected
leading to no analyses. [31] Observed that widespread analytics use is limited mainly to the areas of enrollment
management, student progress, and resource optimization. This limited scope of analytics is mainly due to
barriers of affordability, data, culture, expertise, and communication. Utilizing the full potential in Big Data
Analytics in education presumes existence of a functioning Big Data ecosystem in which there exist policies to
support and facilitate the participating actors to collect, analyze, share, and distribute data and information. In
reality achieving a well structured and functional big data ecosystem with clear incentives for all parties involved
poses challenges in several areas. These are areas such as legislation, policy making, public administration and
management [5]. This calls for development of workable models for big data. Big Data analytics in higher
education also faces the challenge of finding the means to extract knowledge from the extensive data sets being
generated daily and to distill the extracted data into usable information for administrators, students, instructors,
and the public [40]. Pursuing big data is expensive, and as a consequence institutional leaders must be convinced
that it will yield valuable results before they agree to invest in the technology. Therefore Information
Technology leaders of these institutions should ideally demonstrate the importance of Big Data technologies in
their Information Technology strategic plans, in addition to showing how the proposals they present address vital
information issues [17]. This can help allay the fear that many big data projects don't have a tangible return on
investment (ROI) that can be determined upfront [6]. Big Data also presents several analytical challenges that
demand continual updating of tools and expertise, meaning that institutions need to have sufficient budgets to
address these concerns. There also exist legitimate concerns about privacy, especially for data from online
sources. This coupled with the digital divide in many nations presents obstacles to harnessing the power of Big
Data for the benefit of the users of the educational systems [42]. Many challenges associated with the handling
of Big Data are broadly due to its characterizing properties namely volume, velocity, veracity, variety and value.
The challenge is in integrating the heterogeneous data sources and data types in this big data era because the data
sources are diverse; for example there is need to integrate sensors data, cameras data, social media data, and so
on and all of which are different in format, byte, binary, string, number, and so forth. Data Analytics requires
communication with different types of devices and different systems including a means of extracting data from
web pages [26].
IX.CONCLUSION
This paper has explored Big Data Analytics and its relevance in Educational systems with a view of helping
educational institutions adopt Big Data Analytics. The paper has explored the attributes of big data that are
relevant to educational institutions, the factors influencing adoption of big data and analytics in educational
institutions and looked at the factors hindering use of big data in these Institutions. Big Data is far more than
simply collecting information and generating reports. It is a strategic resource that can be used to improve
educational quality. This paper recommends that educational institutions, particularly in developing countries be
encouraged to make investments in analytics programs and in developing expertise in order to get value of big
data. Big Data helps to cut costs and improve education by enabling administrators make decisions that are more
specific and affords teachers valuable tools to choose from for a variety of learning. The Big Data approach to
data management will help reduce difficulties associated with traditional data analysis; and this has the potential
of enriching the education system with new learning ways, and making decision making by policy makers more
efficient and targeted.
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Julius Murumba is a Doctor of Philosophy (Information Technology) student at
Kibabii University, Kenya. He holds a Bsc and MSc (Information Systems), and is
currently a Sessional Lecturer at the Technical University of Kenya. His research
interests include Technology enhanced Learning, Cloud computing and Grid computing
Dr. Elyjoy M. Micheni is a Senior Lecturer in Information Systems and the
Chairperson, Department of Management Science and Technology at The Technical
University of Kenya. She holds a PhD (Information Technology) from Masinde Muliro
University of Science and Technology, Master of Science (Computer Based Information
Systems) from Sunderland University, (UK); Bachelor of Education from Kenyatta
University; Post Graduate Diploma in Project Management from Kenya Institute of
Management. .She has taught Management Information System courses for many years
at University level. She has presented papers in scientific conferences and has many
publications in referred journals. She has also co-authored a book for Middle level
colleges entitled: “Computerized Document Processing” Her career objective is to tap computer based
knowledge as a tool to advance business activities, promote research in ICT and enhance quality service.