scholarly journals Company Managers Knowledge and Skills in the Use of Data Analytics in Decision-Making Process: Basis for Developing Program

2021 ◽  
Vol 11 (8) ◽  
pp. 90-107
Author(s):  
Maricar V. Maniquis
Keyword(s):  
Decision Making ◽  
Data Analytics ◽  
Decision Making Process ◽  
Knowledge And Skills ◽  
Use Of Data

scholarly journals Analysis of Decision Making Process in Moneyball: The Art of Winning an Unfair Game

The Winners ◽  
2015 ◽  
Vol 16 (1) ◽  
pp. 57
Author(s):  
Mochamad Sandy Triady ◽  
Ami Fitri Utami
Keyword(s):  
Decision Making ◽  
Excellent Result ◽  
Data Driven ◽  
Decision Making Process ◽  
Data Driven Decision Making ◽  
General Manager ◽  
Use Of Data ◽  
Bottom Position ◽  
Using Data ◽  
Baseball Team

Billy Beanes’s success in using data-driven decision making in baseball industry is wonderfully written by Michael Lewis in Moneyball. As a general manager in baseball team that were in the bottom position of the league from the financial side to acquire the players, Beane, along with his partner, explored the use of data in choosing the team’s player. They figured out how to determine the worth of every player.The process was not smooth, due to the condition of baseball industry that was not common with using advanced statistic in acquiring   players. Many teams still use the old paradigm that rely on experts’ judgments, intuition, or experience in decision making process. Moneyball approached that using data-driven decision making gave excellent result for Beane’s team. The team won 20 gamessequently in the 2002 season and also spent the lowest cost per win than other teams.This paper attempts to review the principles of Moneyball – The Art of Winning an Unfair Game as a process of decision making and gives what we can learn from the story in order to win the games, the unfair games.

Improving the use of analytics and big data by changing the decision-making culture

2017 ◽  
Vol 55 (10) ◽  
pp. 2074-2088 ◽  
Author(s):  
Jane Elisabeth Frisk ◽  
Frank Bannister
Keyword(s):  
Decision Making ◽  
Big Data ◽  
Data Analytics ◽  
Design Approach ◽  
Action Research Project ◽  
Content Type ◽  
Use Of Data ◽  
Support Organizations ◽  
Novel Approach ◽  
Using Data

Purpose Evolving digital technologies continue to enable new ways to collect and analyze data and this has led some researchers to claim that skillful use of data analytics and big data can radically improve a company’s performance, but that in order to achieve such improvements managers need to change their decision-making culture and to increase the degree of collaboration in the decision-making process. The purpose of this paper is to create an increased understanding of how a decision-making culture can be changed by using a design approach. Design/methodology/approach The paper presents an action research project in which the authors use a design approach. Findings By adopting a design approach organizations can change their decision-making culture, increase the degree of collaboration and also reduce the influence of power and politics on their decision-making. Research limitations/implications This paper proposes a new approach to changing a decision-making culture. Practical implications Using data analytics and big data, a design approach can support organizations change their decision-making culture resulting in better and more effective decisions. Originality/value This paper bridges design and decision-making theory in a novel approach to an old problem.

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>.

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.

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.

Cross-Cultural Career Counselling

1996 ◽  
Vol 5 (1) ◽  
pp. 19-22 ◽  
Author(s):  
Linda Rainey
Keyword(s):  
Decision Making ◽  
Ethnic Minorities ◽  
Career Decision ◽  
Cross Cultural ◽  
Career Counselling ◽  
Decision Making Process ◽  
Career Decision Making ◽  
Knowledge And Skills ◽  
Australian Context

When conducting a search of the literature concerning the career decision-making process undertaken by Vietnamese Australian tertiary students, various papers concerning career counselling with ethnic minorities and cross-cultural career counselling have been consulted. This paper presents the main points in the literature because of their relevance to the Australian context. The knowledge and skills required of career counsellors who work with such clients, as described in these texts, highlight the challenges facing these professionals.

Analytics Taming the Data Monster

2018 ◽  
Author(s):  
Mahesh K. Joshi ◽  
J.R. Klein
Keyword(s):  
Decision Making ◽  
Mobile Devices ◽  
Data Analytics ◽  
Decision Making Process ◽  
Interaction Point ◽  
Its Analysis ◽  
Digital World ◽  
The Us ◽  
Point Data ◽  
Data Location

In this digital world in two days we create as much information as we did from the beginning of time until 2003. The volume of data being captured and stored is mind boggling. It seems that there is quantum disruption coming in the decision-making process in the way massive amounts of data and its analysis is being used to make decisions. Data about personal choices are collected at every interaction point, data location flows through the daily use of mobile devices, and these are being used by companies for making business choices. It may seem that those who are in control of data may know more about the person than the person himself; however, if you look at Brexit and the US elections, data analytics pretty much failed to deliver significant insight.

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