scholarly journals Data competence maturity: developing data-driven decision making

2018 ◽  
Vol 11 (2) ◽  
pp. 139-158 ◽  
Author(s):  
Thomas G. Cech ◽  
Trent J. Spaulding ◽  
Joseph A. Cazier
Keyword(s):  
Decision Making ◽  
Secondary Education ◽  
Data Science ◽  
Data Driven ◽  
Decision Making Process ◽  
Maturity Model ◽  
Data Driven Decision Making ◽  
Content Type ◽  
Development Data ◽  
Education Performance

Purpose The purpose of this paper is to lay out the data competence maturity model (DCMM) and discuss how the application of the model can serve as a foundation for a measured and deliberate use of data in secondary education. Design/methodology/approach Although the model is new, its implications, and its application are derived from key findings and best practices from the software development, data analytics and secondary education performance literature. These principles can guide educators to better manage student and operational outcomes. This work builds and applies the DCMM model to secondary education. Findings The conceptual model reveals significant opportunities to improve data-driven decision making in schools and local education agencies (LEAs). Moving past the first and second stages of the data competency maturity model should allow educators to better incorporate data into the regular decision-making process. Practical implications Moving up the DCMM to better integrate data into their decision-making process has the potential to produce profound improvements for schools and LEAs. Data science is about making better decisions. Understanding the path laid out in the DCMM to helping an organization move to a more mature data-driven decision-making process will help improve both student and operational outcomes. Originality/value This paper brings a new concept, the DCMM, to the educational literature and discusses how these principles can be applied to improve decision making by integrating them into their decision-making process and trying to help the organization mature within this framework.

Data science in the business environment: customer analytics case studies in SMEs

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Jing Lu ◽  
Lisa Cairns ◽  
Lucy Smith
Keyword(s):  
Decision Making ◽  
Case Studies ◽  
Process Model ◽  
Data Science ◽  
Business Environment ◽  
Data Driven ◽  
Complex Data ◽  
Data Driven Decision Making ◽  
Content Type ◽  
Customer Analytics

Purpose A vast amount of complex data is being generated in the business environment, which enables support for decision-making through information processing and insight generation. The purpose of this study is to propose a process model for data-driven decision-making which provides an overarching methodology covering key stages of the business analytics life cycle. The model is then applied in two small enterprises using real customer/donor data to assist the strategic management of sales and fundraising. Design/methodology/approach Data science is a multi-disciplinary subject that aims to discover knowledge and insight from data while providing a bridge to data-driven decision-making across businesses. This paper starts with a review of established frameworks for data science and analytics before linking with process modelling and data-driven decision-making. A consolidated methodology is then described covering the key stages of exploring data, discovering insights and making decisions. Findings Representative case studies from a small manufacturing organisation and an independent hospice charity have been used to illustrate the application of the process model. Visual analytics have informed customer sales strategy and donor fundraising strategy through recommendations to the respective senior management teams. Research limitations/implications The scope of this research has focused on customer analytics in small to medium-sized enterprise through two case studies. While the aims of these organisations are rather specific, they share a commonality of purpose for their strategic development, which is addressed by this paper. Originality/value Data science is shown to be applicable in the business environment through the proposed process model, synthesising micro- and macro-solution methodologies and allowing organisations to follow a structured procedure. Two real-world case studies have been used to highlight the value of the data-driven model in management decision-making.

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 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.

Empowering human resource functions with data-driven decision-making in start-ups: a narrative inquiry approach

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Deepkumar Varma ◽  
Pankaj Dutta
Keyword(s):  
Decision Making ◽  
Narrative Inquiry ◽  
Technology Implementation ◽  
Data Driven ◽  
Structured Interviews ◽  
New Paradigm ◽  
Data Driven Decision Making ◽  
Content Type ◽  
Start Up ◽  
Start Ups

Purpose Across industries, firms want to adopt data-driven decision-making (DDDM) in various organizational functions. Although DDDM is not a new paradigm, little is known about how to effectively implement DDDM and which problem areas to focus on in these functions. This study aims to enable start-ups to use DDDM in human resources (HR) by studying five HR domains using a narrative inquiry technique and aims to guide managers and HR practitioners in start-ups to enable data-driven decisions in HR. Design/methodology/approach This study adopts the narrative inquiry technique by conducting semi-structured interviews with HR practitioners and senior members handling HR functions in start-ups. Interview memos are thematically analyzed to identify repeated ideas, concepts or elements that become apparent. Findings The study findings indicate that start-ups need to have canned operational reports with right attributes in each of these HR domains, which members should use when performing HR tasks. Few metrics, like cost-to-hire in recruitment, distinctly surfaced relatively higher in importance that each start-up, should compute and use in decision-making. Practical implications Managers, HR practitioners and information technology implementation teams will be able to consume the findings to effectively design or evaluate HR processes or systems that empower decision-making in a start-up. Originality/value Start-ups have a fast-paced culture where creativity, relationships and nimbleness are valued. Prevalent decision models of larger organizations are not suitable in start-ups’ environments. This study, being cognizant of these nuances, takes a fresh approach to guide start-ups adopt DDDM in HR and identify key problem areas where decision-making should be enabled through data.

The business of the library is service

2017 ◽  
Vol 118 (7/8) ◽  
pp. 447-450
Author(s):  
Bruce E. Massis
Keyword(s):  
Decision Making ◽  
Literature Review ◽  
Design Methodology ◽  
Business Practice ◽  
Data Driven ◽  
Business Practices ◽  
Data Driven Decision Making ◽  
Content Type

Purpose The purpose of this paper is to suggest that today’s libraries function using business practices in its management of the library to ensure that its service-based mission is respected. Design/methodology/approach Literature review and commentary on this topic that has been addressed by professionals, researchers and practitioners. Findings Libraries have learned from business the importance of using not only the business practice of using a vigorous level of data-driven decision-making, but data-driven reporting as well to a public that expects a higher level of scrutiny, clarity and precision. Paired with evidence from those who have benefitted from the library’s programs and services most visibly, this combination of data and human-driven anecdotes can serve as the optimum marriage of business and service-based confirmation of library success. Originality/value The value in exploring this topic is to make the distinction between libraries whose supporters expect it to be formally managed like a business as opposed to those who suggest that business practices be used in library operations to ensure its mission as a service-based entity is maintained.

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.

Sign in / Sign up

Export Citation Format

Share Document