Finding Pluto: An Analytics-Based Approach to Safety Data Ecosystems

Abstract

This review article addresses the role of safety professionals in the diffusion strategies for predictive analytics for safety performance. The article explores the models, definitions, roles, and relationships of safety professionals in knowledge application, access, management, and leadership in safety analytics. The article addresses challenges safety professionals face when integrating safety analytics in organizational settings in four operations areas: application, technology, management, and strategy. A review of existing conventional safety data sources (safety data, internal data, external data, and context data) is briefly summarized as a baseline. For each of these data sources, the article points out how emerging analytic data sources (such as Industry 4.0 and the Internet of Things) broaden and challenge the scope of work and operational roles throughout an organization. In doing so, the article defines four perspectives on the integration of predictive analytics into organizational safety practice: the programmatic perspective, the technological perspective, the sociocultural perspective, and knowledge-organization perspective. The article posits a four-level, organizational knowledge-skills-abilities matrix for analytics integration, indicating key organizational capacities needed for each area. The work shows the benefits of organizational alignment, clear stakeholder categorization, and the ability to predict future safety performance.

The role of evidence in occupational safety and health

“…Some stakeholders from the primary interviews also offered definitions. • Big data and safety analytics [32,40] [INT04]…”

“…• Biomarker data [41] • Cultural assessments [40] • Guidelines produced by the government [34,42,43]…”

DaLiF: a data lifecycle framework for data-driven governments

“…Such big data sources include people, the Internet, smart mobile handset, online social networks (Twitter, Facebook, LinkedIn, Instagram), the Internet of Things (IoT), autonomous vehicle, Global Positioning Systems, smart cities, etc. [28][29][30][31][32][33][34][35]. Globally, there are over 3.5 billion social network users and about 26.66 billion IoT linked devices and sensors [8,32].…”

“…The analysis phase enables an organization to handle ample information that can affect the business [61]. This phase is responsible for developing all data analysis and data analytics to extract knowledge and discover new insights [34,42,165]. This phase is like a human brain, i.e., processes the information for the next appropriate required actions by human beings [143,155].…”

“…• Discover business processes that can be enhanced through big data technology, perform analysis of existing issues in each business process, and perform re-engineering of each business process using big data technology [62]. • Define the required types of big data analysis that include descriptive, predictive, prescriptive, and diagnostic [34,62,165]. • In the analysis phase, prepare and publish outputs in machine-readable formats [53,55].…”