scholarly journals A Preliminary Systematic Performanceon Critical Success Factors Categories for Big Data Analytics

2019 ◽  
Vol 9 (1) ◽  
pp. 2320-2324 ◽  
Keyword(s):  
Big Data ◽  
Data Analytics ◽  
Critical Success Factors ◽  
Success Factors ◽  
Research Question ◽  
Big Data Analytics ◽  
Data Driven ◽  
Successful Implementation ◽  
Critical Success ◽  
Data Driven Decisions

Big Data could be used in any industry to make effective data-driven decisions. The successful implementation of Big Data projects requires a combination of innovative technological, organizational, and processing approaches. Over the last decade, the research on Critical Success Factors (CSFs) within Big Data has developed rapidly but the number of available publications is still at a low level. Developing an understandingof the Critical Success Factors (CSFs) and their categoriesare essential to support management in making effective data-driven decisions which could increase their returns on investments.There islimited research conducted on the Critical Success Factors (CSFs) of Big DataAnalytics (BDA) development and implementation.This paper aims to provide more understanding about the availableCritical Success Factors (CSFs) categoriesfor Big Data Analytics implementation and answer the research question (RQ) “What are the existing categories of Critical Success Factors for Big Data Analytics”.Based on a preliminary Systematic Literature Review (SLR) for the available publications related to Big Data CSFs and their categories in the last twelve years (2007-2019),this paper identifiesfive categoriesfor Big Data AnalyticsCritical Success Factors(CSFs), namelyOrganization, People, Technology, Data Management, and Governance categories.

Critical Success Factors of Enterprise Data Analytics and Visualization Ecosystem

2019 ◽  
Vol 10 (3) ◽  
pp. 34-55
Author(s):  
Mohammad Daradkeh
Keyword(s):  
Decision Making ◽  
Data Analytics ◽  
Critical Success Factors ◽  
Success Factors ◽  
Successful Implementation ◽  
Structured Interviews ◽  
Integrative Framework ◽  
Critical Success ◽  
Increasing Demand ◽  
Effective Decision Making

With the huge proliferation of Big Data, combined with the increasing demand for analytics-driven decision-making, the data analytics and visualization (DAV) ecosystem is increasingly becoming a trending practice that many enterprises are adopting to gain actionable insights from corporate data for effective decision-making. Although DAV platforms have tremendous benefits, extant research has paid insufficient attention to the investigation of the critical success factors (CSFs) underpinning their successful implementation in enterprises. In order to bridge this knowledge gap, this study presents an integrative framework synthesizing a set of CSFs for implementing DAV platforms in enterprises. A qualitative research methodology, comprising semi-structured interviews with IT and business analysts, was conducted to collect and analyze the interview data. Analysis of results revealed that the CSFs of DAV implementation exist in various dimensions composed of organizational, technological, process, and people perspectives. This study provides several theoretical and practical implications.

scholarly journals Smart Cities and Big Data Analytics: A Data-Driven Decision-Making Use Case

Smart Cities ◽  
2021 ◽  
Vol 4 (1) ◽  
pp. 286-313
Author(s):  
Ahmed M. Shahat Osman ◽  
Ahmed Elragal
Keyword(s):  
Decision Making ◽  
Big Data ◽  
Data Analytics ◽  
Smart Cities ◽  
Research Question ◽  
Big Data Analytics ◽  
Data Driven ◽  
Use Case ◽  
Data Driven Decision Making ◽  
Domain Independent

Interest in smart cities (SCs) and big data analytics (BDA) has increased in recent years, revealing the bond between the two fields. An SC is characterized as a complex system of systems involving various stakeholders, from planners to citizens. Within the context of SCs, BDA offers potential as a data-driven decision-making enabler. Although there are abundant articles in the literature addressing BDA as a decision-making enabler in SCs, mainstream research addressing BDA and SCs focuses on either the technical aspects or smartening specific SC domains. A small fraction of these articles addresses the proposition of developing domain-independent BDA frameworks. This paper aims to answer the following research question: how can BDA be used as a data-driven decision-making enabler in SCs? Answering this requires us to also address the traits of domain-independent BDA frameworks in the SC context and the practical considerations in implementing a BDA framework for SCs' decision-making. This paper's main contribution is providing influential design considerations for BDA frameworks based on empirical foundations. These foundations are concluded through a use case of applying a BDA framework in an SC's healthcare setting. The results reveal the ability of the BDA framework to support data-driven decision making in an SC.

Survey of the Different Type of Data Analytics Algorithms

2017 ◽  
Vol 8 (1) ◽  
pp. 50-64 ◽  
Author(s):  
Michael Kevin Hernandez
Keyword(s):  
Big Data ◽  
Data Analytics ◽  
Predictive Analytics ◽  
Big Data Analytics ◽  
High Volume ◽  
Data Driven ◽  
Advantages And Disadvantages ◽  
Definition Of ◽  
Data Driven Decisions ◽  
Quantitative Definition

From as early as 1854 to today, society has been gathering, processing, transforming, modeling and visualizing data to help drive data-driven decisions. The qualitative definition of big data can be defined more conclusively as data that has high volume, velocity, and variety. Whereas, the quantitative definition of big data does vary with respect to time due to the dependence of the time's technology and processing capabilities. However, making use of that big data to facilitate data-driven decisions, one should employ either descriptive, predictive, or prescriptive analytics. This article has discussed and summarized the advantages and disadvantages of the algorithms that fell under descriptive and predictive analytics. Given the sheer number of the different types of algorithms and the amount of versatile data mining software available sometimes, the best big data analytics results can come from mixing two to three of the mentioned algorithms.

scholarly journals The Role of Big Data in Enhancing Customer Experience in UAE Retail

2018 ◽  
Vol 9 (6) ◽  
pp. 76
Author(s):  
Shital Vakhariya ◽  
Kirti Khanzode
Keyword(s):  
Big Data ◽  
Data Analytics ◽  
Success Factors ◽  
Big Data Analytics ◽  
Customer Experience ◽  
Structured Interview ◽  
Critical Success ◽  
Retail Business ◽  
Relevant Factors

Purpose: The objective of this research was to identify critical success factors for the adoption of Big Data in UAE retail. The use of Big Data, in this case, focused on improving its system of recommendations for a better understanding of consumer behavior and its impact on consumer experience.Design/Methodology/Approach: The research was done through interviews & observation of shopping patterns. A semi - structured interview script was used for the interviews.Findings: Based on the results, we outline some propositions related to the opportunities and obstacles for the implementation of Big Data in UAE retail.Originality/Value: The main contribution of the research was the identification of relevant factors to the adoption of Big Data that were not considered as critical for the adoption of previous technologies.Research Rationale: Few years ago, retailers had no idea who was buying what and from where they are buying and where not so bother about customer experience. Now Big Data helps retailers understand individuals' needs, allowing them to create segments to target. Big Data will help in understanding the buying trend. Not may study are conducted in UAE retail. This study will help to understand the adoption and role of Big data in UAE retail customer experience.This paper show how can big data analytics help to improve the retail business and can be applied in the sector and help in decision making.

Quality 4.0 conceptualisation and theoretical understanding: a global exploratory qualitative study

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Jiju Antony ◽  
Olivia McDermott ◽  
Michael Sony
Keyword(s):  
Critical Success Factors ◽  
Success Factors ◽  
Full Range ◽  
Senior Management ◽  
Successful Implementation ◽  
Motivating Factors ◽  
Theoretical Understanding ◽  
Content Type ◽  
Critical Success ◽  
Management Professionals

PurposeQuality 4.0 has a unique potential to create a competitive advantage for organisations by improving customer experience and enhancing profitability. The purpose of this study is to examine Quality 4.0, the9; benefits, motivating factors, critical success factors and the skills required by quality professionals in the successful implementation of Quality 4.0. The study also investigates the organisational readiness factors9 and challenges that need to be addressed before Quality 4.0 adoption and assess their importance.Design/methodology/approachA qualitative interview approach was utilised by interviewing a panel of senior management, engineering and continuous improvement (CI); professionals working in leading companies in Asia, Europe and America who are currently deploying Quality 4.0.FindingsThis study provides a theoretical base for the Quality 4.0 body of knowledge in terms of an organisation’s adoption and overcoming implementation challenges and providing examples of Quality 4.0 application. Organisations can use this study to understand what Quality 4.0 means to industry, the benefits and motivating factors for implementing, the Critical Success Factors, challenges, the organisational readiness factors and the role of leadership in a Quality 4.0 deployment. In addition, the study looks at the skills required by future Quality 4.0 professionals in terms of hard skills, soft skills and a curriculum for educating future quality management professionals. The respondents cited that predictive analytics, sensors and tracking, and electronic feedback loops are the most critical technologies for driving Quality 4.0.Research limitations/implicationsOne of the limitations of this research was that as this area is a nascent area the researchers were limited in their literature review. The second limitation was that the study was based on 12 interviews. A more comprehensive longitudinal study would yield more data so that better and robust conclusions can be derived from the study.Originality/valueThis is the first empirical study on Quality 4.0, which captures the viewpoints of senior management professionals on a full range of topics related to Quality 4.0 motivation for deployment, implementation and readiness for its adoption.

scholarly journals Critical Success Factors for Building Information Modelling Implementation

2018 ◽  
Vol 18 (3) ◽  
pp. 55-73 ◽  
Author(s):  
Ganiyu Amuda-Yusuf
Keyword(s):  
Critical Success Factors ◽  
Success Factors ◽  
Successful Implementation ◽  
Development Education ◽  
Building Information Modelling ◽  
Information Modelling ◽  
Critical Success ◽  
Building Information ◽  
Global Construction ◽  
Product And Process

Adoption of Building Information Modelling (BIM) in the global construction industry is fast growing. This paper expounds the Critical Success Factors (CSFs) for BIM implementation and explore their ranking and underlying relationships. A total of 28 CSFs was identified from the review of previous studies on success factors. Survey questionnaire containing these 28 factors was used to collect data from industry practitioners in Nigeria. Benchmark metrics was developed to rank the success factors. The topmost five success factors for BIM implementation in order of importance are: standard platforms for integration and communication; cost of development; education and training; standardization (product and process); and clear definition and understanding of users’ requirement. Analysis of variance shows that significant differences exist in the pattern of rating for the topmost CSFs based on turnover. Factor analysis was further adopted to group the 28 CSFs into five components, using rotated component matrix method. The five components extracted are: (i) industry stakeholders’ commitment and knowledge of BIM, (ii) capacity building for technology adoption, (iii) organisational support, (iv) collaborative synergy among industry professional and (v) cultural orientation. The rankings of the CSFs provide basis for prioritising the most significant factors that industry stakeholders should focus attention for successful implementation of BIM. In addition, the underlying relationships among the success factors identified in this study, will assist industry stakeholders to determine best strategy to adopt in implementing BIM at industry level.

A review on the identification of total productive maintenance critical success factors for effective implementation in the manufacturing sector

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Sudhir Chaurey ◽  
Shyamkumar D. Kalpande ◽  
R.C. Gupta ◽  
Lalit K. Toke
Keyword(s):  
Literature Review ◽  
Critical Success Factors ◽  
Success Factors ◽  
Manufacturing Sector ◽  
Successful Implementation ◽  
Critical Factors ◽  
Total Productive Maintenance ◽  
Content Type ◽  
Effective Implementation ◽  
Critical Success

PurposeThe purpose of this paper is to carry out the literature search on manufacturing organizations and total productive maintenance (TPM). This research aims at studying TPM attributes and barriers in line with the TPM framework for effective implementation of TPM. This study identifies the barriers in TPM implementation and the critical success factors (CSFs) for effective TPM implementation.Design/methodology/approachIn this manuscript, the study of TPM in the manufacturing sector has been considered a broad area of the research and emphasis on the TPM literature review, which primarily relates to the contribution of manufacturing sector and employment availability. Next sections covers TPM history, importance, justification, pillars, obstacles and TPM implementation procedure and models. Thereafter author identified the gaps in existing literature.FindingsThe existing literature shows that very few TPM implementation models are available for the manufacturing sector. The study also found that there is no systematically conducted large-scale empirical research which deals with TPM implementation. In order to bridge this gap, an investigation into the successful implementation of TPM in is truly needed. The finding of the literature shows that there is a need of TPM model specially developed for the manufacturing sector. The identified critical factors derived from the extensive literature review help to overcome the barriers for effective TPM implementation.Research limitations/implicationsThis review study is limited to Indian manufacturing industries. The identified TPM CSFs are based on the TPM pillars and their sub-factors. This cross-sectional study was based on the existing TPM model.Practical implicationsThis paper can increase the significance of TPM strategy, which could help managers of organizations to have a better understanding of the benefits of implementing TPM and therefore enable patient satisfaction within their organizations.Originality/valueThe literature review covers methodical identification of TPM barriers and critical factors for maintenance performance improvements. It allows the practitioners to apply these identified CSFs for TPM implementation to achieve an improvement in industrial performance and competitiveness.

Critical Success Factors of IS Implementation

2015 ◽  
Vol 3 (8) ◽  
pp. 192-212
Author(s):  
Iqbal Saad Al Saleh
Keyword(s):  
Information Technology ◽  
Critical Success Factors ◽  
Success Factors ◽  
Vital Role ◽  
Successful Implementation ◽  
Technological Factors ◽  
Is Implementation ◽  
Critical Success ◽  
Quality Of Products

Recently, the use of information technology has become important and critical, for organizations, as it enhance the quality of products and integrity of organizational services. However, the implementation of such systems is still problematic. Due to globalization and the variation in the monetary, social and technological environments, Information Systems (IS) have turned out to be an imperative feature and high priority for different kind of organizations. In this regard, critical success factors play a vital role in successful implementation of IS. This paper evaluates and analyzes the critical success factors (CSFs) of IS implementation from the Saudi managers’ perspective in different industries in Saudi Arabia. This study will also present the illustration or identification of 19 critical success factors, which are based on a review of literatures and the first phase of a study in Saudi industries. The study will also rank those 19 factors in a logical way and will characterize them into three categories of factors, including organisational, human and technological factors.

Sign in / Sign up

Export Citation Format

Share Document