Salaries, Dollars, and Sense: Making Data Driven Decisions

2014 ◽  
Vol 3 (1) ◽  
pp. 29-32
Author(s):  
Stacy Warner ◽  
Emily S. Sparvero
Keyword(s):  
Data Driven ◽  
Sense Making ◽  
Data Driven Decisions

scholarly journals COVID-19 Brings Data Equity Challenges to the Fore

2021 ◽  
Author(s):  
H.V. Jagadish ◽  
Julia Stoyanovich ◽  
Bill Howe
Keyword(s):  
Quality Control ◽  
Government Policy ◽  
Data Driven ◽  
Contact Tracing ◽  
Control Mechanisms ◽  
Sources Of Information ◽  
Decision Systems ◽  
Physical Resources ◽  
Multiple Levels ◽  
Data Driven Decisions

The COVID-19 pandemic is compelling us to make crucial data-driven decisions quickly, bringing together diverse and unreliable sources of information without the usual quality control mechanisms we may employ. These decisions are consequential at multiple levels: they can inform local, state and national government policy, be used to schedule access to physical resources such as elevators and workspaces within an organization, and inform contact tracing and quarantine actions for individuals. In all these cases, significant inequities are likely to arise, and to be propagated and reinforced by data-driven decision systems. In this article, we propose a framework, called FIDES, for surfacing and reasoning about data equity in these systems.

Against generalisation: Data-driven decisions need context to be human-compatible

2021 ◽  
pp. 026638212110619
Author(s):  
Sharon Richardson
Keyword(s):  
Artificial Intelligence ◽  
Real World ◽  
Data Science ◽  
Machine Learning Algorithms ◽  
Data Driven ◽  
Theoretical Frameworks ◽  
The Past ◽  
Real World Applications ◽  
Contextual Framework ◽  
Data Driven Decisions

During the past two decades, there have been a number of breakthroughs in the fields of data science and artificial intelligence, made possible by advanced machine learning algorithms trained through access to massive volumes of data. However, their adoption and use in real-world applications remains a challenge. This paper posits that a key limitation in making AI applicable has been a failure to modernise the theoretical frameworks needed to evaluate and adopt outcomes. Such a need was anticipated with the arrival of the digital computer in the 1950s but has remained unrealised. This paper reviews how the field of data science emerged and led to rapid breakthroughs in algorithms underpinning research into artificial intelligence. It then discusses the contextual framework now needed to advance the use of AI in real-world decisions that impact human lives and livelihoods.

scholarly journals Building strong health supply chain systems: the visibility and analytics network approach to improving the Nigeria immunisation supply chain

2018 ◽  
Vol 25 (4) ◽  
pp. 199-206 ◽  
Author(s):  
Chibuzo Ottih ◽  
Kevin Cussen ◽  
Mahmud Mustafa
Keyword(s):  
Decision Making ◽  
Supply Chain ◽  
Data Analytics ◽  
Data Driven ◽  
Evidence Based ◽  
Type Method ◽  
Effective Operation ◽  
Data Driven Decisions ◽  
Vaccine Availability ◽  
Evidence Based Decision Making

BackgroundHealth supply chain managers are unable to effectively monitor the performance of the immunisation supply chain in Nigeria. As a result, they are unable to make effective, data-driven decisions. This results in poor vaccine availability at some service delivery points. A lack of reliable data for evidence-based decision making is a significant contributor to this challenge.MethodThe visibility and analytics network (VAN) principles were introduced to enable end-to-end visibility in the immunisation supply chain and logistics (ISCL) system and make more accurate data available to health supply chain managers.ResultsThe application of the VAN principles has led to improved data collection, real-time stock visibility and enhanced data analytics framework. This enhanced visibility has promoted a culture of accountability and data-driven decision-making, previously unattainable. Health supply chain managers are now equipped with better skills and tools to promote effective operation of the immunisation supply chain.ConclusionThe introduction of VAN principles has been an effective approach to improving data visibility and creating incremental improvements in the ISCL in Nigeria.

scholarly journals SOMOSPIE: A Modular SOil MOisture SPatial Inference Engine Based on Data-Driven Decisions

2019 ◽  
Author(s):  
Danny Rorabaugh ◽  
Mario Guevara ◽  
Ricardo Llamas ◽  
Joy Kitson ◽  
Rodrigo Vargas ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Inference Engine ◽  
Data Driven ◽  
Data Driven Decisions

scholarly journals A Preliminary Systematic Performanceon Critical Success Factors Categories for Big Data Analytics

2019 ◽  
Vol 9 (1) ◽  
pp. 2320-2324 ◽  
Keyword(s):  
Big Data ◽  
Data Analytics ◽  
Critical Success Factors ◽  
Success Factors ◽  
Research Question ◽  
Big Data Analytics ◽  
Data Driven ◽  
Successful Implementation ◽  
Critical Success ◽  
Data Driven Decisions

Big Data could be used in any industry to make effective data-driven decisions. The successful implementation of Big Data projects requires a combination of innovative technological, organizational, and processing approaches. Over the last decade, the research on Critical Success Factors (CSFs) within Big Data has developed rapidly but the number of available publications is still at a low level. Developing an understandingof the Critical Success Factors (CSFs) and their categoriesare essential to support management in making effective data-driven decisions which could increase their returns on investments.There islimited research conducted on the Critical Success Factors (CSFs) of Big DataAnalytics (BDA) development and implementation.This paper aims to provide more understanding about the availableCritical Success Factors (CSFs) categoriesfor Big Data Analytics implementation and answer the research question (RQ) “What are the existing categories of Critical Success Factors for Big Data Analytics”.Based on a preliminary Systematic Literature Review (SLR) for the available publications related to Big Data CSFs and their categories in the last twelve years (2007-2019),this paper identifiesfive categoriesfor Big Data AnalyticsCritical Success Factors(CSFs), namelyOrganization, People, Technology, Data Management, and Governance categories.

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