Using Data Mining to Identify Actionable Information: Breaking New Ground in Data-Driven Decision Making

2005 ◽  
Vol 10 (3) ◽  
pp. 281-293 ◽  
Author(s):  
Philip A. Streifer ◽  
Jeffrey A. Schumann
Keyword(s):  
Data Mining ◽  
Decision Making ◽  
Data Driven ◽  
Data Driven Decision Making ◽  
Using 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.

scholarly journals The Rapid Adoption of Data-Driven Decision-Making

2016 ◽  
Vol 106 (5) ◽  
pp. 133-139 ◽  
Author(s):  
Erik Brynjolfsson ◽  
Kristina McElheran
Keyword(s):  
Decision Making ◽  
Companion Paper ◽  
Census Bureau ◽  
Data Driven ◽  
Data Driven Decision Making ◽  
Manufacturing Plants ◽  
Using Data ◽  
Large Representative ◽  
The U.S ◽  
Case Based

We provide a systematic empirical study of the diffusion and adoption patterns of data-driven decision making (DDD) in the U.S. Using data collected by the Census Bureau for a large representative sample of manufacturing plants, we find that DDD rates nearly tripled (11%-30%) between 2005 and 2010. This rapid diffusion, along with results from a companion paper, are consistent with case-based evidence that DDD tends to be productivity-enhancing. Yet certain plants are significantly more likely to adopt than others. Key correlates of adoption are size, presence of potential complements such as information technology and educated workers, and firm learning.

scholarly journals Data-driven decision-making in creating class rosters

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Rebecca Wolf ◽  
Joseph M. Reilly ◽  
Steven M. Ross
Keyword(s):  
Decision Making ◽  
Student Learning ◽  
Low Income ◽  
School Leaders ◽  
Data Driven ◽  
Data Driven Decision Making ◽  
Content Type ◽  
Use Of Data ◽  
Using Data ◽  
Rich Data

PurposeThis article informs school leaders and staffs about existing research findings on the use of data-driven decision-making in creating class rosters. Given that teachers are the most important school-based educational resource, decisions regarding the assignment of students to particular classes and teachers are highly impactful for student learning. Classroom compositions of peers can also influence student learning.Design/methodology/approachA literature review was conducted on the use of data-driven decision-making in the rostering process. The review addressed the merits of using various quantitative metrics in the rostering process.FindingsFindings revealed that, despite often being purposeful about rostering, school leaders and staffs have generally not engaged in data-driven decision-making in creating class rosters. Using data-driven rostering may have benefits, such as limiting the questionable practice of assigning the least effective teachers in the school to the youngest or lowest performing students. School leaders and staffs may also work to minimize negative peer effects due to concentrating low-achieving, low-income, or disruptive students in any one class. Any data-driven system used in rostering, however, would need to be adequately complex to account for multiple influences on student learning. Based on the research reviewed, quantitative data alone may not be sufficient for effective rostering decisions.Practical implicationsGiven the rich data available to school leaders and staffs, data-driven decision-making could inform rostering and contribute to more efficacious and equitable classroom assignments.Originality/valueThis article is the first to summarize relevant research across multiple bodies of literature on the opportunities for and challenges of using data-driven decision-making in creating class rosters.

Using Data-Driven Decision Making

2021 ◽  
pp. 83-99
Author(s):  
Mary Ruth Coleman ◽  
Jennifer Job
Keyword(s):  
Decision Making ◽  
Data Driven ◽  
Data Driven Decision Making ◽  
Using Data

scholarly journals Predicting Students' Attitudes Toward Collaboration: Evidence From Structural Equation Model Trees and Forests

2021 ◽  
Vol 12 ◽  
Author(s):  
Jialing Li ◽  
Minqiang Zhang ◽  
Yixing Li ◽  
Feifei Huang ◽  
Wei Shao
Keyword(s):  
Data Mining ◽  
Decision Making ◽  
Structural Equation Model ◽  
Structural Equation ◽  
Equation Model ◽  
Data Driven ◽  
Group Model ◽  
Data Driven Decision Making ◽  
Model Trees ◽  
Students Attitudes

Numerous studies have shed some light on the importance of associated factors of collaborative attitudes. However, most previous studies aimed to explore the influence of these factors in isolation. With the strategy of data-driven decision making, the current study applied two data mining methods to elucidate the most significant factors of students' attitudes toward collaboration and group students to draw a concise model, which is beneficial for educators to focus on key factors and make effective interventions at a lower cost. Structural equation model trees (SEM trees) and structural equation model forests (SEM forests) were applied to the Program for International Student Assessment 2015 dataset (a total of 9,769 15-year-old students from China). By establishing the most important predictors and the splitting rules, these methods constructed multigroup common factor models of collaborative attitudes. The SEM trees showed that home educational resources (split by “above-average or not”), home possessions (split by “disadvantaged or not”), mother's education (split by “below high school or not”), and gender (split by “male or female”) were the most important predictors among the demographic variables, drawing a 5-group model. Among all the predictors, achievement motivation (split by “above-average or not”) and sense of belonging at school (split by “above-average or not” and “disadvantaged or not”) were the most important, drawing a 6-group model. The SEM forest findings proved the relative importance of these variables. This paper discusses various interpretations of these results and their implications for educators to formulate corresponding interventions. Methodologically, this research provides a data mining approach to discover important information from large-scale educational data, which might be a complementary approach to enhance data-driven decision making in education.

Data-driven decision-making in the library

2016 ◽  
Vol 117 (1/2) ◽  
pp. 131-134 ◽  
Author(s):  
Bruce Massis
Keyword(s):  
Decision Making ◽  
Design Methodology ◽  
Data Driven ◽  
Data Driven Decision Making ◽  
Content Type ◽  
Aggregated Data ◽  
Strategic Direction ◽  
Reporting Decisions ◽  
Using Data ◽  
Support Decision Making

Purpose – The purpose of this paper is to describe the current environment for libraries to consider the value of using data to support decision-making. Design/methodology/approach – This paper contains literature review and commentary on this topic that has been addressed by professionals, researchers and practitioners. Findings – In developing a library’s strategic direction, it is essential that evidentiary data be referenced to supplement the organization’s rationale for decision-making. There is an expectation by stakeholders that libraries are able to generate reports and decisions based on aggregated data for in-demand reporting. Therefore, capturing, analyzing and reporting decisions based on data are indispensable in today’s libraries. Originality/value – The value in addressing this topic is to examine the option by libraries to use data to support data-driven decision-making.

Data-Driven Decision Making to Select Condition-Based Maintenance Technology

2019 ◽  
pp. 324-343
Author(s):  
María Carmen Carnero-Moya
Keyword(s):  
Decision Making ◽  
Vibration Analysis ◽  
Industry 4.0 ◽  
Condition Based Maintenance ◽  
Data Driven ◽  
Data Driven Decision Making ◽  
Technical Parameters ◽  
Multicriteria Model ◽  
Expected Benefits ◽  
Using Data

Condition-based maintenance (CBM) may be considered an essential part of the Industry 4.0 environment because it can improve production processes through the use of the latest digital technologies. The literature includes a large number of contributions on new techniques for diagnosis, signal treatment, analysis of technical parameters, and prognosis. However, to obtain the expected benefits of a vibration analysis program, it is necessary to choose the instruments and introduction process best suited to the organization, and so guarantee the best results using data-driven decision making in accordance with the needs of Industry 4.0. Despite the importance of these decisions, no relevant models are found in the literature. This contribution describes a fuzzy multicriteria model for choosing the most suitable technology in vibration analysis. The goal is to create a model that is easy for organizations to use, and which reflects the judgements of a number of experts in maintenance and vibration analysis. The model has been applied to a Spanish state-run healthcare organization.

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