scholarly journals Airline Data Analysis

2019 ◽  
pp. 22-29
Author(s):  
Navuluri Madhavilatha ◽  
Bheema Shireesha ◽  
Chunduru Anilkumar
Keyword(s):  
Decision Making ◽  
Data Analysis ◽  
Data Analytics ◽  
Decision Making Process ◽  
Flight Delays ◽  
Commercial Aviation ◽  
World Data ◽  
Contemporary World ◽  
Business System

In the contemporary world, Data analysis is a challenge in the era of varied inters disciplines through there is a specialization in the respective disciplines. In other words, effective data analytics helps in analyzing the data of any business system. Flight delays hurt airlines, airports, and passengers. Their prediction is crucial during the decision-making process for all players of commercial aviation. The goal of our project is to get monthly wise statistics of airline data and taking particular airport as target we are further analyzing the data to get the hourly statistics. And also we are finding out the most popular source-destination pairs and calculating the average delays at every airport. The data for this project comes from the stat-computing.org website. In particular, in the year 2008 data 70,09,728 titles recorded there which includes information on the Origin, Destination, Month, Year, DayofWeek, DayofMonth, DepDelay, ArvDelay, DepTime, ArvTime and a few other less interesting variables. Conveniently, you can export the data directly as a csv file.

Big Data Myth

2019 ◽  
pp. 223-237
Author(s):  
Agata Mardosz-Grabowska
Keyword(s):  
Decision Making ◽  
Big Data ◽  
Data Analysis ◽  
Qualitative Data ◽  
New Technologies ◽  
Marketing Management ◽  
Individual Experience ◽  
Decision Making Process

Organizations are expected to act rationally; however, mythical thinking is often present among their members. It refers also to myths related to technology. New inventions and technologies are often mythologized in organizations. People do not understand how new technologies work and usually overestimate their possibilities. Also, myths are useful in dealing with ambivalent feelings, such as fears and hopes. The text focuses on the so-called “big data myth” and its impact on the decision-making process in modern marketing management. Mythical thinking related to big data in organizations has been observed both by scholars and practitioners. The aim of the chapter is to discuss the foundation of the myth, its components, and its impact on the decision-making process. Among others, a presence of a “big data myth” may be manifested by over-reliance on data, neglecting biases in the process of data analysis, and undermining the role of other factors, including intuition and individual experience of marketing professionals or qualitative data.

scholarly journals Evaluation of application of new decision-making methods in selected companies: the use of business intelligence in practice

ACC Journal ◽  
2020 ◽  
Vol 26 (2) ◽  
pp. 29-40
Author(s):  
Petra Kašparová
Keyword(s):  
Decision Making ◽  
Data Analysis ◽  
Business Intelligence ◽  
Questionnaire Survey ◽  
Human Life ◽  
Utilization Rate ◽  
Decision Making Process ◽  
Short Introduction ◽  
Business Decision

Growing pressure on increasing decision-making speed in all spheres of human life is one of the basic phenomena of today. Immediately after the first wave of the coronavirus pandemic, we can consider the ability of making good decisions quickly as one of the most important aspects of our being. The main objective of this article is to find out the utilization rate of several basic decision-making approaches in selected companies with an emphasis on newly used methods such as data analysis and business intelligence tools. The first part of the article presents a short introduction of the decision-making process and an overview of hitherto known and used tools facilitating the whole procedure. The submitted study of available literature leads to the presentation of own classification of the most widely used decisionmaking methods. Based on a questionnaire survey, in the second section, the pilot research examines the involvement of five different groups of methods in business decision-making, such as intuition and previous experiences, consultation with colleagues, data analysis (historical), MCDM methods and consultation with experts. Afterwards, the most common obstacles that employees must face in introducing new tools have been identified. In general, the results show that time and the associated pressure on decision-making speed play a crucial role in the decision-making process.

scholarly journals A functional framework based on big data analytics for smart farming

2021 ◽  
Vol 24 (3) ◽  
pp. 1772
Author(s):  
Loubna Rabhi ◽  
Noureddine Falih ◽  
Lekbir Afraites ◽  
Belaid Bouikhalene
Keyword(s):  
Decision Making ◽  
Big Data ◽  
Internet Of Things ◽  
Data Analytics ◽  
New Products ◽  
Big Data Analytics ◽  
Decision Making Process ◽  
Time Production ◽  
Smart Farming ◽  
The Future

Big <span>data in agriculture is defined as massive volumes of data with a wide variety of sources and types which can be captured using internet of things sensors (soil and crops sensors, drones, and meteorological stations), analyzed and used for decision-making. In the era of internet of things (IoT) tools, connected agriculture has appeared. Big data outputs can be exploited by the future connected agriculture in order to reduce cost and time production, improve yield, develop new products, offer optimization and smart decision-making. In this article, we propose a functional framework to model the decision-making process in digital and connected agriculture</span>.

Utilising ontology for “heteregeneous data analysis in organizational goals”

2019 ◽  
Vol 15 (3) ◽  
pp. 310-323
Author(s):  
Tengku Adil Tengku Izhar ◽  
Bernady O. Apduhan ◽  
Torab Torabi
Keyword(s):  
Decision Making ◽  
Data Analysis ◽  
Design Methodology ◽  
Decision Making Process ◽  
Time Data ◽  
Content Type ◽  
Organizational Goals ◽  
Dependency Relationship ◽  
Operational Level ◽  
The Relationship

Purpose The purpose of this paper is to assess the level of the organizational goal accomplishment by assessing the reliance relationship between organizational data and organizational goals. Design/methodology/approach The evaluation of the organizational goals is based on design and operational level, which can serve in ranking of the organizational goals achievement and hence assist the decision-making process in achieving the organizational goals. To achieve this aim, the authors propose an ontology to develop the relationship between organizational data and organizational goals. Findings Data goals dependency shows the dependency relationship between organizational data and organizational goals. At the same time, data goals dependency assists the process of identifying data attributes, where the authors suggest that these data attributes are relevant in relation to the organizational goals. Originality/value The contribution of this paper will serve as the first step to evaluate the relevance of organizational data to assist decision-making in relation to the organizational goals.

Frequent Item-set Mining For Electronics Data

2020 ◽  
Author(s):  
Nikhil Ranjan Nayak
Keyword(s):  
Decision Making ◽  
Data Analytics ◽  
Pattern Analysis ◽  
Decision Making Process ◽  
Memory Consumption ◽  
Frequent Item ◽  
Cost Cutting ◽  
Time Period ◽  
Hidden Patterns ◽  
Frequent Item Sets

Data Analytics plays an important role in the decision-making process. Insights from such pattern analysis offer vast benefits, including increased revenue, cost cutting, and improved competitive advantage. However, the hidden patterns of the frequent item-sets become more time consuming to be mined when the amount of data increases over the time. Moreover, significant memory consumption is needed in mining the hidden patterns of the frequent item-sets due to a heavy computation by the algorithm. Therefore, an efficient algorithm is required to mine the hidden patterns of the frequent item-sets within a shorter run time and with less memory consumption while the volume of data increases over the time period.

Strengthening the Capabilities in Data Analytics

2020 ◽  
pp. 525-549
Author(s):  
Milenka Linneth Argote Cusi ◽  
Leon Dario Parra Bernal
Keyword(s):  
Decision Making ◽  
Data Analytics ◽  
Industrial Revolution ◽  
Digital Economy ◽  
Decision Making Process ◽  
Economic Sectors ◽  
Fourth Industrial Revolution ◽  
Internal Capabilities ◽  
Technological Tools

In the framework of digital economy and the fourth industrial revolution, it is very important that companies have internal capabilities for the analysis of data and of the information they produce, as well as to generate value in the decision-making process. In 2017 the EAN University implemented the Program for Strengthening Capabilities in DA (PSCDA) with 15 companies from different economic sectors in Bogotá, Colombia. The main purpose of the program was to diagnose, qualify, and accompany the participating companies, in the process of strengthening their DA capabilities. Among the most important results we highlighted that 90% of the companies from the program have applied technological tools for the analysis of their data, while an 80% were able to design and implement a plan of improvement for their processes in data analytics and its use in decision making.

A model for examining the effects of communication on innovation performance: emphasis on the intermediary role of strategic decision-making speed

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Fariborz Rahimnia ◽  
Homa Molavi
Keyword(s):  
Decision Making ◽  
Data Analysis ◽  
Innovation Performance ◽  
Strategic Decision ◽  
Equation Modeling ◽  
Strategic Decision Making ◽  
Decision Making Process ◽  
Content Type ◽  
Intermediary Role

PurposeIn recent years, rapid changes in the economic situation and high levels of competition have increased the need for innovation in order to gain success. In such circumstances, organizational strategists are considered as critical in determining the success or failure of organizations. Using innovation in various aspects of organizational operations is the most important factor to achieve sustainable competitive advantages in industry. As a result, analyzing the effective factors involved in promoting the efficiency of innovative activities in the organization and ways of achieving it are of utmost importance. Thus, this paper examines the relationship between communication and innovation performance with respect to the intermediary role of strategic decision-making process speed.Design/methodology/approachThe present study has used quantitative methodology and questionnaire to collect data from 450 managers and members who are involved in the decision-making process in 150 companies operating in the food-industry sector. Data analysis was done by using structural equation modeling and AMOS software.FindingsThe results of the data analysis suggest that communication and strategic decision-making speed possess a significant positive impact on innovation performance. Also, strategic decision-making speed has sufficiently played the intermediary role between communication and innovation performance.Originality/valueThis survey specifies the effects of communication on the success of making fast strategic decision and innovation performance which aid Iranian food companies to tackle one of the managerial challenges: postponing strategic decisions due to lack of efficient communication to get information. In addition, to the best of the authors' knowledge, this essay is a first in Iran.

scholarly journals Big Data Analysis Proposal for Manufacturing Firm

2021 ◽  
Vol 5 (1) ◽  
pp. 68-75
Author(s):  
Alicia Valdez ◽  
Griselda Cortes ◽  
Laura Vazquez ◽  
Adriana Martinez ◽  
Gerardo Haces
Keyword(s):  
Decision Making ◽  
Data Analysis ◽  
Business Processes ◽  
Decision Making Process ◽  
Manufacturing Companies ◽  
Manufacturing Firm ◽  
Production Factors ◽  
Ac Motors ◽  
Selection Of

The analysis of large volumes of data is an important activity in manufacturing companies, since they allow improving the decision-making process. The data analysis has generated that the services and products are personalized, and how the consumption of the products has evolved, obtaining results that add value to the companies in real time. In this case study, developed in a large manufacturing company of electronic components as robots and AC motors; a strategy has been proposed to analyze large volumes of data and be able to analyze them to support the decision-making process; among the proposed activities of the strategy are: Analysis of the technological architecture, selection of the business processes to be analyzed, installation and configuration of Hadoop software, ETL activities, and data analysis and visualization of the results. With the proposed strategy, the data of nine production factors of the motor PCI boards were analyzed, which had a greater incidence in the rejection of the components; a solution was made based on the analysis, which has allowed a decrease of 28.2% in the percentage of rejection.

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