When Philanthropy Meets Data Science: A Framework for Governance to Achieve Data-Driven Decision-Making for Public Good

2021 ◽  
pp. 55-68
Author(s):  
Nuria Oliver
Keyword(s):  
Decision Making ◽  
Public Good ◽  
Data Science ◽  
Data Driven ◽  
Data Driven Decision Making

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.

scholarly journals Data Science and its Relationship to Big Data and Data-Driven Decision Making

Big Data ◽  
2013 ◽  
Vol 1 (1) ◽  
pp. 51-59 ◽  
Author(s):  
Foster Provost ◽  
Tom Fawcett
Keyword(s):  
Decision Making ◽  
Big Data ◽  
Data Science ◽  
Data Driven ◽  
Data Driven Decision Making

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 Improving Decision Quality for Business Users Based on Cloud-based Self-Service Business Intelligence Tools

2020 ◽  
pp. 1-11
Author(s):  
Christian Ploder ◽  
Reinhard Bernsteiner ◽  
Thomas Dilger
Keyword(s):  
Decision Making ◽  
Business Intelligence ◽  
Data Science ◽  
Data Driven ◽  
Decision Quality ◽  
Data Driven Decision Making ◽  
Significant Difference ◽  
Self Service ◽  
Business Informatics ◽  
User Friendly

The ever-growing volume of data promotes data-driven decision-making in more cases and more areas than before. The development of user-friendly self-service BI (SSBI) tools enable business users to autonomously execute tasks in the area of Business Intelligence (BI), statistical analysis, or data science. Cloud computing offers the opportunity to provide SSBI as services as well. This paper focusses on cloud-based SSBI tools and their support for data-driven decision-making by business users. This paper aims to identify the influence of a deeper understanding of business informatics on (a) the handling of the cloud-based SSBI tools and (b) the data-driven decision making performance. An experimental setting was used to collect empirical data. Two groups with equal knowledge in business administration, but different backgrounds in business informatics have been defined. Based on different backgrounds in business informatics, the results show no significant difference in handling the cloud-based SSBI tool but reveal significant differences in decision-making performance.

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