scholarly journals Data Visualization in Support of Executive Decision Making

10.28945/3687 ◽  
2017 ◽  
Vol 12 ◽  
pp. 125-138 ◽  
Author(s):  
Jeanne Moore
Keyword(s):  
Decision Making ◽  
Big Data ◽  
Technology Adoption ◽  
Data Visualization ◽  
Psychological Factors ◽  
Health Sector ◽  
Future Research ◽  
Journal Paper ◽  
The Right ◽  
Data Visualizations

Aim/Purpose: This journal paper seeks to understand historical aspects of data management, leading to the current data issues faced by organizational executives in relation to big data and how best to present the information to circumvent big data challenges for executive strategic decision making. Background: This journal paper seeks to understand what executives value in data visualization, based on the literature published from prior data studies. Methodology: The qualitative methodology was used to understand the sentiments of executives and data analysts using semi-structured interview techniques. Contribution: The preliminary findings can provide practical knowledge for data visualization designers, but can also provide academics with knowledge to reflect on and use, specifically in relation to information systems (IS) that integrate human experience with technology in more valuable and productive ways. Findings: Preliminary results from interviews with executives and data analysts point to the relevance of understanding and effectively presenting the data source and the data journey, using the right data visualization technology to fit the nature of the data, creating an intuitive platform which enables collaboration and newness, the data presenter’s ability to convey the data message and the alignment of the visualization to core the objectives as key criteria to be applied for successful data visualizations Recommendations for Practitioners: Practitioners, specifically data analysts, should consider the results highlighted in the findings and adopt such recommendations when presenting data visualizations. These include data and premise understanding, ensuring alignment to the executive’s objective, possessing the ability to convey messages succinctly and clearly to the audience, having knowledge of the domain to answer questions effectively, and using the right technology to convey the message. Recommendation for Researchers: The importance of human cognitive and sensory processes and its impact in IS development is paramount. More focus can be placed on the psychological factors of technology acceptance. The current TAM model, used to describe use, identifies perceived usefulness and perceived ease-of-use as the primary considerations in technology adoption. However, factors that have been identified that impact on use do not express the importance of cognitive processes in technology adoption. Future Research: Future research requires further focus on intangible and psychological factors that could affect technology adoption and use, as well as understanding data visualization effectiveness in corporate environments, not only predominantly within the Health sector. Lessons from Health sector studies in data visualization should be used as a platform.

Big Data Analytics in Cloud Computing

2019 ◽  
pp. 325-341
Author(s):  
Rajganesh Nagarajan ◽  
Ramkumar Thirunavukarasu
Keyword(s):  
Cloud Computing ◽  
Big Data ◽  
Data Processing ◽  
Data Visualization ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Future Research ◽  
Big Data Processing ◽  
Investment Cost

In this chapter, the authors consider different categories of data, which are processed by the big data analytics tools. The challenges with respect to the big data processing are identified and a solution with the help of cloud computing is highlighted. Since the emergence of cloud computing is highly advocated because of its pay-per-use concept, the data processing tools can be effectively deployed within cloud computing and certainly reduce the investment cost. In addition, this chapter talks about the big data platforms, tools, and applications with data visualization concept. Finally, the applications of data analytics are discussed for future research.

Semantic Technologies and Big Data Analytics for Cyber Defence

Web Services ◽  
2019 ◽  
pp. 1430-1443
Author(s):  
Louise Leenen ◽  
Thomas Meyer
Keyword(s):  
Decision Making ◽  
Big Data ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Future Research ◽  
Data Sets ◽  
Semantic Technologies ◽  
Data Types ◽  
Intelligent Decision Making ◽  
Big Data Technologies

The Governments, military forces and other organisations responsible for cybersecurity deal with vast amounts of data that has to be understood in order to lead to intelligent decision making. Due to the vast amounts of information pertinent to cybersecurity, automation is required for processing and decision making, specifically to present advance warning of possible threats. The ability to detect patterns in vast data sets, and being able to understanding the significance of detected patterns are essential in the cyber defence domain. Big data technologies supported by semantic technologies can improve cybersecurity, and thus cyber defence by providing support for the processing and understanding of the huge amounts of information in the cyber environment. The term big data analytics refers to advanced analytic techniques such as machine learning, predictive analysis, and other intelligent processing techniques applied to large data sets that contain different data types. The purpose is to detect patterns, correlations, trends and other useful information. Semantic technologies is a knowledge representation paradigm where the meaning of data is encoded separately from the data itself. The use of semantic technologies such as logic-based systems to support decision making is becoming increasingly popular. However, most automated systems are currently based on syntactic rules. These rules are generally not sophisticated enough to deal with the complexity of decisions required to be made. The incorporation of semantic information allows for increased understanding and sophistication in cyber defence systems. This paper argues that both big data analytics and semantic technologies are necessary to provide counter measures against cyber threats. An overview of the use of semantic technologies and big data technologies in cyber defence is provided, and important areas for future research in the combined domains are discussed.

Unlocking the Potential: Understanding the Psychological Factors That Influence Technology Adoption in the Upstream Oil and Gas Industry

SPE Journal ◽  
2019 ◽  
Vol 25 (01) ◽  
pp. 515-528
Author(s):  
Ruby Roberts ◽  
Rhona Flin
Keyword(s):  
Technology Adoption ◽  
Psychological Factors ◽  
Oil And Gas ◽  
New Technologies ◽  
New Technology ◽  
Oil And Gas Industry ◽  
Research Area ◽  
Organizational Level ◽  
Innovative Technology ◽  
Future Research

Summary To maximize the opportunities for the adoption of newly developed products, there is a need to better understand how psychological factors have an impact on the acceptance and deployment of innovative technology in industry. While there is extensive general literature on the psychological factors that influence consumer behavior and the use of new technologies, there seemed to be very limited understanding of this topic, specifically relating to the upstream energy sector. A literature review was conducted to (1) identify what, if any, research has been conducted in relation to the psychological factors influencing technology adoption and deployment in the oil and gas (O&G) industry and (2) identify what interventions have been developed to support technology adoption in O&G. A literature search was undertaken, and given the limited research anticipated, minimum selection criteria were applied on the basis of Cochrane quality control (Higgins and Green 2011). In the 17 articles that met the search criteria, there was limited discussion of the psychological factors that have an impact on O&G technology adoption. The articles were subject to Braun and Clarke (2006) thematic analysis, producing a list of psychological factors that influence technology adoption in O&G. Only five psychological factors were identified: personality (e.g., exploration traits and risk aversion), attitude (e.g., trust and not-invented-here syndrome), social (e.g., social norms), cognition (e.g., risk perception), and psychological factors at an organizational level (leadership and organizational culture). In addition, our review identified a small number of interventions that were developed and deployed to support technology adoption in O&G. Given the early stages of this research area, combined with the relevance for technology innovation in upstream O&G, our review adds to the literature by identifying an initial framework of the key psychological factors. This essential set of factors can be used to direct future research, as well as to support effective interventions aimed at supporting the introduction of new technology.

scholarly journals Aesthetics and Clarity in Information Visualization: The Designer’s Perspective

Arts ◽  
2018 ◽  
Vol 7 (4) ◽  
pp. 72
Author(s):  
Annemarie Quispel ◽  
Alfons Maes ◽  
Joost Schilperoord
Keyword(s):  
Mass Media ◽  
Data Visualization ◽  
Information Visualization ◽  
Information Design ◽  
Future Research ◽  
Good Design ◽  
The Public ◽  
Objective Information ◽  
Data Visualizations ◽  
Important Design

Designers are increasingly involved in creating ‘popular’ data visualizations in mass media. Scientists in the field of information visualization propose collaborations between designers and scientists in popular data visualization. They assume that designers put more emphasis on aesthetics than on clarity in their representation of data, and that they aim to convey subjective, rather than objective, information. We investigated designers’ criteria for good design for a broad audience by interviewing professional designers and by reviewing information design handbooks. Additionally, we investigated what might make a visualization aesthetically pleasing (attractive) in the view of the designers. Results show that, according to the information designers, clarity and aesthetics are the main criteria, with clarity being the most important. They aim to objectively inform the public, rather than conveying personal opinions. Furthermore, although aesthetics is considered important, design literature hardly addresses the characteristics of aesthetics, and designers find it hard to define what makes a visualization attractive. The few statements found point at interesting directions for future research.

scholarly journals How to use Big Data technologies to optimize operations in Upstream Petroleum Industry

2013 ◽  
Vol 1 (1) ◽  
pp. 19-25 ◽  
Author(s):  
Abdelkader Baaziz ◽  
Luc Quoniam
Keyword(s):  
Decision Making ◽  
Big Data ◽  
Petroleum Industry ◽  
Data Sets ◽  
Big Data Technologies ◽  
World Economic ◽  
Disparate Data ◽  
Complex Forms ◽  
The Right ◽  
4D Seismic

“Big Data is the oil of the new economy” is the most famous citation during the three last years. It has even been adopted by the World Economic Forum in 2011. In fact, Big Data is like crude! It’s valuable, but if unrefined it cannot be used. It must be broken down, analyzed for it to have value. But what about Big Data generated by the Petroleum Industry and particularly its upstream segment? Upstream is no stranger to Big Data. Understanding and leveraging data in the upstream segment enables firms to remain competitive throughout planning, exploration, delineation, and field development.Oil Gas Companies conduct advanced geophysics modeling and simulation to support operations where 2D, 3D 4D Seismic generate significant data during exploration phases. They closely monitor the performance of their operational assets. To do this, they use tens of thousands of data-collecting sensors in subsurface wells and surface facilities to provide continuous and real-time monitoring of assets and environmental conditions. Unfortunately, this information comes in various and increasingly complex forms, making it a challenge to collect, interpret, and leverage the disparate data. As an example, Chevron’s internal IT traffic alone exceeds 1.5 terabytes a day.Big Data technologies integrate common and disparate data sets to deliver the right information at the appropriate time to the correct decision-maker. These capabilities help firms act on large volumes of data, transforming decision-making from reactive to proactive and optimizing all phases of exploration, development and production. Furthermore, Big Data offers multiple opportunities to ensure safer, more responsible operations. Another invaluable effect of that would be shared learning.The aim of this paper is to explain how to use Big Data technologies to optimize operations. How can Big Data help experts to decision-making leading the desired outcomes?Keywords:Big Data; Analytics; Upstream Petroleum Industry; Knowledge Management; KM; Business Intelligence; BI; Innovation; Decision-making under Uncertainty

scholarly journals Graph structures for data visualizations

2021 ◽  
Vol 6 (2) ◽  
pp. 24-31
Author(s):  
Stefana Janićijević ◽  
Vojkan Nikolić
Keyword(s):  
Big Data ◽  
Data Visualization ◽  
Data Types ◽  
Current State ◽  
Graph Structures ◽  
Big Data Visualization ◽  
Visualization Tools ◽  
Graph System ◽  
Data Visualizations

Networks are all around us. Graph structures are established in the core of every network system therefore it is assumed to be understood as graphs as data visualization objects. Those objects grow from abstract mathematical paradigms up to information insights and connection channels. Essential metrics in graphs were calculated such as degree centrality, closeness centrality, betweenness centrality and page rank centrality and in all of them describe communication inside the graph system. The main goal of this research is to look at the methods of visualization over the existing Big data and to present new approaches and solutions for the current state of Big data visualization. This paper provides a classification of existing data types, analytical methods, techniques and visualization tools, with special emphasis on researching the evolution of visualization methodology in recent years. Based on the obtained results, the shortcomings of the existing visualization methods can be noticed.

scholarly journals An Interactive Data Visualization Framework for Exploring Geospatial Environmental Datasets and Model Predictions

Water ◽  
2020 ◽  
Vol 12 (10) ◽  
pp. 2928
Author(s):  
Jeffrey D. Walker ◽  
Benjamin H. Letcher ◽  
Kirk D. Rodgers ◽  
Clint C. Muhlfeld ◽  
Vincent S. D’Angelo
Keyword(s):  
Decision Making ◽  
Case Studies ◽  
Data Visualization ◽  
Large Scale ◽  
Research Management ◽  
Web Based ◽  
Environmental Models ◽  
Potential Benefits ◽  
Interactive Data ◽  
Data Visualizations

With the rise of large-scale environmental models comes new challenges for how we best utilize this information in research, management and decision making. Interactive data visualizations can make large and complex datasets easier to access and explore, which can lead to knowledge discovery, hypothesis formation and improved understanding. Here, we present a web-based interactive data visualization framework, the Interactive Catchment Explorer (ICE), for exploring environmental datasets and model outputs. Using a client-based architecture, the ICE framework provides a highly interactive user experience for discovering spatial patterns, evaluating relationships between variables and identifying specific locations using multivariate criteria. Through a series of case studies, we demonstrate the application of the ICE framework to datasets and models associated with three separate research projects covering different regions in North America. From these case studies, we provide specific examples of the broader impacts that tools like these can have, including fostering discussion and collaboration among stakeholders and playing a central role in the iterative process of data collection, analysis and decision making. Overall, the ICE framework demonstrates the potential benefits and impacts of using web-based interactive data visualization tools to place environmental datasets and model outputs directly into the hands of stakeholders, managers, decision makers and other researchers.

The big data regime shift in real estate

2020 ◽  
Vol 38 (4) ◽  
pp. 363-395 ◽  
Author(s):  
James R. DeLisle ◽  
Brent Never ◽  
Terry V. Grissom
Keyword(s):  
Decision Making ◽  
Big Data ◽  
Real Estate ◽  
Urban Areas ◽  
Regime Shift ◽  
Design Thinking ◽  
Future Research ◽  
Content Type ◽  
Data Movement ◽  
Support Decision Making

PurposeThe paper explores the emergence of the “big data regime” and the disruption that it is causing for the real estate industry. The paper defines big data and illustrates how an inductive, big data approach can help improve decision-making.Design/methodology/approachThe paper demonstrates how big data can support inductive reasoning that can lead to enhanced real estate decisions. To help readers understand the dynamics and drivers of the big data regime shift, an extensive list of hyperlinks is included.FindingsThe paper concludes that it is possible to blend traditional and non-traditional data into a unified data environment to support enhanced decision-making. Through the application of design thinking, the paper illustrates how socially responsible development can be targeted to under-served urban areas and helps serve residents and the communities in which they live.Research limitations/implicationsThe paper demonstrates how big data can be harnessed to support decision-making using a hypothetical project. The paper does not present advanced analytics but focuses aggregating disparate longitudinal data that could support such analysis in future research.Practical implicationsThe paper focuses on the US market, but the methodology can be extended to other markets where big data is increasingly available.Social implicationsThe paper illustrates how big data analytics can be used to help serve the needs of marginalized residents and tenants, as well as blighted areas.Originality/valueThis paper documents the big data movement and demonstrates how non-traditional data can support decision-making.

Telemedical Second Opinions in Germany – a Customer Survey of an Online Portal

2021 ◽  
Author(s):  
Nadja Könsgen ◽  
Barbara Prediger ◽  
Anna Schlimbach ◽  
Ana-Mihaela Bora ◽  
Victoria Weißflog ◽  
...  
Keyword(s):  
Decision Making ◽  
Patient Satisfaction ◽  
Patient Population ◽  
Response Rate ◽  
Health Insurer ◽  
Treatment Recommendation ◽  
Future Research ◽  
Online Portal ◽  
The Right ◽  
Customer Survey

Abstract BackgroundSecond medical opinions (SOs) can strengthen patients’ certainty in decision-making. Since 2019, statutory health insured German residents have the right to obtain an SO for selected indications according to the SO Directive. Additionally, several health insurers offer SO programs often in cooperation with telemedical SO providers. Telemedical SO programs are mostly based on documents only. Our aim was to analyze the characteristics of people who obtained telemedical SOs, their experiences made during the SO process, and their assessment of SOs in general and of the different routes of SO delivery (personally/by phone/documents only).MethodsGerman residents who obtained an SO via an online portal between January 2016 and February 2019 (n=1247) were contacted by post between August and November 2019 up to three times. The results were analyzed descriptively.ResultsThe 368 participants (response rate 30%) were 54% male, 95% statutory health insured and 61 years old (median; interquartile range 51-72). Most participants reported having an orthopedic condition (87%) or had been recommended surgery (78%). The most common reason for seeking an SO in general was the need for more information on further treatment recommendations (64%). A telemedical SO was mainly chosen because it was offered by the health insurer (82%). Disagreements between first opinions and SOs occurred in 55% of the participants. Approximately 60% of the participants with disagreements followed the treatment recommendation of the SO. For 67% of the participants, the SO (rather) enhanced the certainty in decision-making. Approximately 75% were (rather) satisfied with obtaining the SO via the online portal, and 95% would seek another SO (irrespective of the online portal). The most preferred route of SO delivery was a personally delivered SO, which 80% would (rather) consider, followed by 70% (rather) considering SOs based on documents only and 48% (rather) considering SOs by phone.ConclusionSOs were generally appreciated. Although our results show that SOs (based on documents only) support patients and that patient satisfaction was high, personally delivered SOs were still preferred. Future research on the use of SOs based on documents only (in which patient population and in what situations) is needed.

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