The transformative impact of big data applications in sport marketing: current and future directions

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Yoseph Mamo ◽  
Yiran Su ◽  
Damon P.S. Andrew
Keyword(s):  
Big Data ◽  
Management Practices ◽  
Sport Management ◽  
Analytical Techniques ◽  
Data Sources ◽  
Yield Data ◽  
Content Type ◽  
Domain Specific ◽  
Big Data Applications ◽  
Data Driven Decisions

PurposeAs big data (BD) has increasingly become an important tool for managers and researchers to transform sport management practices, the purpose of this research is to highlight diverse data sources and modern analytical techniques that will leverage BD as a means to advance scholarship in sport management.Design/methodology/approachA comprehensive review of existing BD literature in sport management outlines new perspectives on BD research method and the application of BD in sport management.Findings First, through a thorough review of the literature, a domain-specific conceptualization that incorporates the field's mission and priorities was developed. Second, potential data sources and different types of analytical opportunities was identified, highlighting strategies for developing methodological approaches that leads to novel research questions. BD analytics can allow for more flexibility in improving methodological capability to analyze data and, thus, provide more granular and predictive insights. Finally, this paper concludes with a discussion of BD's impact on three domains of sport management, whereby the organizations yield data-driven decisions.Originality/valueBD has the potential to transform the sport management operations and bridges the research-practice gap. BD research in sport management is instrumental for accumulating new knowledge and/or testing existing theories, either in a deductive fashion or by taking an inductive approach, as the field embarks to advance scholarship.

Proposal of Analytical Model for Business Problems Solving in Big Data Environment

Web Services ◽  
2019 ◽  
pp. 618-638
Author(s):  
Goran Klepac ◽  
Kristi L. Berg
Keyword(s):  
Data Mining ◽  
Big Data ◽  
Predictive Models ◽  
Analytical Approach ◽  
Fraud Detection ◽  
Analytical Techniques ◽  
Data Sources ◽  
Business Decisions ◽  
Mining Projects ◽  
Structured Approach

This chapter proposes a new analytical approach that consolidates the traditional analytical approach for solving problems such as churn detection, fraud detection, building predictive models, segmentation modeling with data sources, and analytical techniques from the big data area. Presented are solutions offering a structured approach for the integration of different concepts into one, which helps analysts as well as managers to use potentials from different areas in a systematic way. By using this concept, companies have the opportunity to introduce big data potential in everyday data mining projects. As is visible from the chapter, neglecting big data potentials results often with incomplete analytical results, which imply incomplete information for business decisions and can imply bad business decisions. The chapter also provides suggestions on how to recognize useful data sources from the big data area and how to analyze them along with traditional data sources for achieving more qualitative information for business decisions.

Data Literacy

2017 ◽  
Vol 8 (2) ◽  
pp. 14-38 ◽  
Author(s):  
Dimitar Christozov ◽  
Katia Rasheva-Yordanova
Keyword(s):  
Big Data ◽  
Domain Knowledge ◽  
Real Life ◽  
Big Data Analytics ◽  
Point Of View ◽  
Life Problems ◽  
Big Data Applications ◽  
Master Data ◽  
Big Data Application ◽  
Data Driven Decisions

The article shares the authors' experiences in training bachelor-level students to explore Big Data applications in solving nowadays problems. The article discusses curriculum issues and pedagogical techniques connected to developing Big Data competencies. The following objectives are targeted: The importance and impact of making rational, data driven decisions in the Big Data era; Complexity of developing and exploring a Big Data Application in solving real life problems; Learning skills to adopt and explore emerging technologies; and Knowledge and skills to interpret and communicate results of data analysis via combining domain knowledge with system expertise. The curriculum covers: The two general uses of Big Data Analytics Applications, which are well distinguished from the point of view of end-user's objectives (presenting and visualizing data via aggregation and summarization [data warehousing: data cubes, dash boards, etc.] and learning from Data [data mining techniques]); Organization of Data Sources: distinction of Master Data from Operational Data, in particular; Extract-Transform-Load (ETL) process; and Informing vs. Misinforming, including the issue of over-trust vs. under-trust of obtained analytical results.

Analytics based decision making

2014 ◽  
Vol 6 (4) ◽  
pp. 332-340 ◽  
Author(s):  
Deepak Agrawal
Keyword(s):  
Decision Making ◽  
Big Data ◽  
Data Analytics ◽  
Design Methodology ◽  
Historical Perspective ◽  
Big Data Analytics ◽  
Analytical Techniques ◽  
Content Type ◽  
Informed Decisions ◽  
Different Types

Purpose – This paper aims to trace the history, application areas and users of Classical Analytics and Big Data Analytics. Design/methodology/approach – The paper discusses different types of Classical and Big Data Analytical techniques and application areas from the early days to present day. Findings – Businesses can benefit from a deeper understanding of Classical and Big Data Analytics to make better and more informed decisions. Originality/value – This is a historical perspective from the early days of analytics to present day use of analytics.

scholarly journals Data Analytics and Mining in Healthcare with Emphasis on Causal Relationship Mining

2019 ◽  
Vol 8 (4) ◽  
pp. 195-204
Keyword(s):  
Data Mining ◽  
Big Data ◽  
Causal Relationship ◽  
Data Analytics ◽  
Statistical Tests ◽  
Analytical Techniques ◽  
Data Sources ◽  
Healthcare Personnel ◽  
Related Data ◽  
Healthcare Data

High volumes and varieties of data is piling every day from healthcare and related fields. This big data sources if managed and analysed properly will provide vital knowledge. Data mining and data analytics have been playing an important role in extracting useful information from healthcare and related data sources. The knowledge extracted from these data sources guiding patients and healthcare personnel towards improved health conditions. Analytical techniques from statistics, functionalities from data mining and machine learning already proved their capability with significant contributions to healthcare industry. The dominant functionality of data mining is classification which has been in use in mining healthcare data. Though classification is a good learning technique it may not provide a causation model which will be a reliable model for better decision making particularly in the medical field. The present models for causality have limitations in terms of scalability and reliability. The present study is targeted to study causal models for causal relationship mining. This study tried to conclude with some proposals for causal relationship discovery which are efficient, reliable and scalable. The proposed model is going to make use of some qualities of decision trees along with statistical tests and analytics. It is proposed to build the learning models on healthcare big data sources.

scholarly journals PerTract: Model Extraction and Specification of Big Data Systems for Performance Prediction by the Example of Apache Spark and Hadoop

2019 ◽  
Vol 3 (3) ◽  
pp. 47
Author(s):  
Johannes Kroß ◽  
Helmut Krcmar
Keyword(s):  
Big Data ◽  
Response Times ◽  
Apache Spark ◽  
Data Systems ◽  
Domain Specific ◽  
Model And Simulation ◽  
Big Data Applications ◽  
Simulation Based ◽  
Measurement Results ◽  
Big Data Systems

Evaluating and predicting the performance of big data applications are required to efficiently size capacities and manage operations. Gaining profound insights into the system architecture, dependencies of components, resource demands, and configurations cause difficulties to engineers. To address these challenges, this paper presents an approach to automatically extract and transform system specifications to predict the performance of applications. It consists of three components. First, a system-and tool-agnostic domain-specific language (DSL) allows the modeling of performance-relevant factors of big data applications, computing resources, and data workload. Second, DSL instances are automatically extracted from monitored measurements of Apache Spark and Apache Hadoop (i.e., YARN and HDFS) systems. Third, these instances are transformed to model- and simulation-based performance evaluation tools to allow predictions. By adapting DSL instances, our approach enables engineers to predict the performance of applications for different scenarios such as changing data input and resources. We evaluate our approach by predicting the performance of linear regression and random forest applications of the HiBench benchmark suite. Simulation results of adjusted DSL instances compared to measurement results show accurate predictions errors below 15% based upon averages for response times and resource utilization.

scholarly journals GrandBase: generating actionable knowledge from Big Data

2017 ◽  
Vol 1 (2) ◽  
pp. 105-126 ◽  
Author(s):  
Xiu Susie Fang ◽  
Quan Z. Sheng ◽  
Xianzhi Wang ◽  
Anne H.H. Ngu ◽  
Yihong Zhang
Keyword(s):  
Big Data ◽  
Large Scale ◽  
Empirical Studies ◽  
Knowledge Extraction ◽  
Data Sources ◽  
Future Research ◽  
Content Type ◽  
Actionable Knowledge ◽  
True Value ◽  
Truth Discovery

Purpose This paper aims to propose a system for generating actionable knowledge from Big Data and use this system to construct a comprehensive knowledge base (KB), called GrandBase. Design/methodology/approach In particular, this study extracts new predicates from four types of data sources, namely, Web texts, Document Object Model (DOM) trees, existing KBs and query stream to augment the ontology of the existing KB (i.e. Freebase). In addition, a graph-based approach to conduct better truth discovery for multi-valued predicates is also proposed. Findings Empirical studies demonstrate the effectiveness of the approaches presented in this study and the potential of GrandBase. The future research directions regarding GrandBase construction and extension has also been discussed. Originality/value To revolutionize our modern society by using the wisdom of Big Data, considerable KBs have been constructed to feed the massive knowledge-driven applications with Resource Description Framework triples. The important challenges for KB construction include extracting information from large-scale, possibly conflicting and different-structured data sources (i.e. the knowledge extraction problem) and reconciling the conflicts that reside in the sources (i.e. the truth discovery problem). Tremendous research efforts have been contributed on both problems. However, the existing KBs are far from being comprehensive and accurate: first, existing knowledge extraction systems retrieve data from limited types of Web sources; second, existing truth discovery approaches commonly assume each predicate has only one true value. In this paper, the focus is on the problem of generating actionable knowledge from Big Data. A system is proposed, which consists of two phases, namely, knowledge extraction and truth discovery, to construct a broader KB, called GrandBase.

Interactive visualization of big data in the field of accounting

2019 ◽  
Vol 20 (4) ◽  
pp. 497-525 ◽  
Author(s):  
Lisa Maria Perkhofer ◽  
Peter Hofer ◽  
Conny Walchshofer ◽  
Thomas Plank ◽  
Hans-Christian Jetter
Keyword(s):  
Big Data ◽  
Visual Analytics ◽  
Interactive Visualization ◽  
Ease Of Use ◽  
Data Sources ◽  
Sample Population ◽  
Perceived Ease Of Use ◽  
Content Type ◽  
Technological Support ◽  
The Impact

Purpose Big Data introduces high amounts and new forms of structured, unstructured and semi-structured data into the field of accounting and this requires alternative data management and reporting methods. Generating insights from these new data sources highlight the need for different and interactive forms of visualization in the field of visual analytics. Nonetheless, a considerable gap between the recommendations in research and the current usage in practice is evident. In order to understand and overcome this gap, a detailed analysis of the status quo as well as the identification of potential barriers for adoption is vital. The paper aims to discuss this issue. Design/methodology/approach A survey with 145 business accountants from Austrian companies from a wide array of business sectors and all hierarchy levels has been conducted. The survey is targeted toward the purpose of this study: identifying barriers, clustered as human-related and technological-related, as well as investigating current practice with respect to interactive visualization use for Big Data. Findings The lack of knowledge and experience regarding new visualization types and interaction techniques and the sole focus on Microsoft Excel as a visualization tool can be identified as the main barriers, while the use of multiple data sources and the gradual implementation of further software tools determine the first drivers of adoption. Research limitations/implications Due to the data collection with a standardized survey, there was no possibility of dealing with participants individually, which could lead to a misinterpretation of the given answers. Further, the sample population is Austrian, which might cause issues in terms of generalizing results to other geographical or cultural heritages. Practical implications The study shows that those knowledgeable and familiar with interactive Big Data visualizations indicate high perceived ease of use. It is, therefore, necessary to offer sufficient training as well as user-centered visualizations and technological support to further increase usage within the accounting profession. Originality/value A lot of research has been dedicated to the introduction of novel forms of interactive visualizations. However, little focus has been laid on the impact of these new tools for Big Data from a practitioner’s perspective and their needs.

Big Data: A Strategic Perspective

2014 ◽  
Vol 08 (03) ◽  
pp. 319-333
Author(s):  
David Alfred Ostrowski
Keyword(s):  
Big Data ◽  
Software Development ◽  
Data Sources ◽  
Apache Hadoop ◽  
Big Data Applications ◽  
Programming Techniques ◽  
Future Direction ◽  
Strategic Perspective ◽  
New Applications

Big Data has become ubiquitous across all areas of research allowing for new applications that were not possible earlier. Unlike software development relying on traditional data sources, Big Data applications present their own unique challenges to appropriately harness the utility of the Apache Hadoop architecture. In this paper, we introduce fundamental concepts of Hadoop and explore its usage as well as future direction. We also present our strategy for exploring the Hadoop architecture including addressing issues of scalability, customization of code and utilization of programming techniques.

Proposal of Analytical Model for Business Problems Solving in Big Data Environment

2015 ◽  
pp. 209-228 ◽  
Author(s):  
Goran Klepac ◽  
Kristi L. Berg
Keyword(s):  
Data Mining ◽  
Big Data ◽  
Predictive Models ◽  
Analytical Approach ◽  
Fraud Detection ◽  
Analytical Techniques ◽  
Data Sources ◽  
Business Decisions ◽  
Mining Projects ◽  
Structured Approach

This chapter proposes a new analytical approach that consolidates the traditional analytical approach for solving problems such as churn detection, fraud detection, building predictive models, segmentation modeling with data sources, and analytical techniques from the big data area. Presented are solutions offering a structured approach for the integration of different concepts into one, which helps analysts as well as managers to use potentials from different areas in a systematic way. By using this concept, companies have the opportunity to introduce big data potential in everyday data mining projects. As is visible from the chapter, neglecting big data potentials results often with incomplete analytical results, which imply incomplete information for business decisions and can imply bad business decisions. The chapter also provides suggestions on how to recognize useful data sources from the big data area and how to analyze them along with traditional data sources for achieving more qualitative information for business decisions.

The use of big data in corporate communication

2017 ◽  
Vol 22 (3) ◽  
pp. 258-272 ◽  
Author(s):  
Christian Wiencierz ◽  
Ulrike Röttger
Keyword(s):  
Big Data ◽  
Literature Review ◽  
Public Relations ◽  
Impact Factor ◽  
Internal Communication ◽  
Corporate Communication ◽  
Future Research ◽  
Content Type ◽  
Big Data Applications ◽  
State Of Research

Purpose The purpose of this paper is to illustrate the current state of research on the significance of big data in and for corporate communication and to introduce a framework which provides specific connecting points for future research. This is achieved by summarizing and reviewing the insights provided by relevant articles in the most significant scholarly journals. The paper also investigates trends in the literature. Design/methodology/approach On the basis of a systematic literature review, 53 key articles from 2010 to 2015 were further analyzed. Findings The literature review illustrates the potentialities of big data for corporate communication, especially with regard to the field of marketing communication. It also reveals a dramatic lack of research in the fields of public relations and internal communication with respect to big data applications. Research limitations/implications The online databases used in this paper comprised of refereed scientific journals with the highest impact factor in the respective disciplines. Journals with a lower impact factor and books were not included in the search process for this thematic analysis. Practical implications This paper provides a conceptual framework that describes four phases of strategic big data usage in corporate communication. The results show how big data is able to highlight stakeholders’ insights so that more effective communication strategies can be created. Originality/value This paper brings together previously disparate streams of work in the fields of communication science, marketing, and information systems with respect to big data applications in corporate communication. It represents the first attempt to undertake a systematic and comprehensive interdisciplinary overview of this kind.

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