Abstract
At the heart of all policy design and implementation, there is a need to understand how well decisions are made. It is evidently known that the quality of decision making depends significantly on the quality of the analyses and advice provided to the associated actors. Over decades, organizations were highly diligent in gathering and processing vast amounts of data, but they have given less emphasis on how these data can be used in policy argument. With the arrival of big data, attention has been focused on whether it could be used to inform policy-making. This chapter aims to bridge this gap, to understand variations in how big data could yield usable evidence, and how policymakers can make better use of those evidence in policy choices. An integrated and holistic look at how solving complex problems could be conducted on the basis of semantic technologies and big data is presented in this chapter.
TopIntroduction
To ensure global prosperity there is a need to bridge the gap between big data and policy-making, to “overcome mistrust and misunderstanding, [to] resolve conflicts of goals, and [to] learn to speak the same language” (Jacoby, 2013: 3). This ongoing divergence is raising a serious question of social responsibility and is calling for a drastic change by investigating the scientific and moral foundations of contemporary beliefs to help decision-makers in dealing with complex global issues. It is thus imperative to place big data and policy-making in the time horizon of doing the informed right thing rather than merely doing. In other words, there is an urgent need to be endowed with an ability to exercise wise judgment, to adopt a balanced perception of doing based on the assumption that political, scientific and ethical aspects are closely interrelated and mutually reinforced. In doing so, serious attention should be paid to the relationship between big data and policy-making on one side and ethics on the other side with a perspective to go beyond the use of big data to inform policy, towards seeking novel meta-evidences inferred from available big data to underpin wise judgment leading to sustainable policies. According to Marcus (2013) “solving problems will often require a fair amount of ... specific information often gathered painstakingly by experts. So-called machine learning can sometimes help, but ... Big Data is a powerful tool for inferring correlations”.
Believing that the most pressing problems organizations face today are characterized by unprecedented levels of complexity and interdependence leads to the breakdown of the conventional problem-solving paradigm focusing on explicit knowledge and incremental improvements, and go about leveraging from the currently experienced change and complexity. USCCF (2014: 7) has stated that regardless of what form it takes, big data has the potential to identify new connections and opportunities, and enable improved understanding of the past to shape a better future. Bean (2017) has argued that big data is already being used to improve operational efficiency, and the ability to make informed decisions based on the very latest up-to-the-moment information is rapidly becoming the mainstream norm. Yet, the most critical dilemma facing industries isn’t the big size and the related enormous complexity of big data, but having the right data (Wessel, 2016). This challenge does not lie in a lack of data processing tools, but more in a holistic, integrated and unified framework governing the steady flow of data. Despite the increased adoption of data analytic tools, the current state of the art shows that using big data to solve complex problems still remains problematic.
By its very construct, big data analytic tools were designed to enable in-depth understanding of complex issues, anticipating possible scenarios and making better decisions. Although these issues can be similar across different disciplines, too many data will look too hard to form any coherent judgment. Maybe it is difficult to deal with this elusive challenge because policy-making is based on a context-dependent process and the idea of using big data, which is still a very slippery concept, requires making best use of global knowledge sources. However, a holistic picture can be drawn from the exploration of the distinguishing characteristics of the social and semantic faces of the web of data. More specifically, dealing with this gap and finding meaningful correlation relies fundamentally on data indexing and information integration based on domain knowledge representation. Following today’s transition towards a new era of wise and smart forms of managing and organizing, this chapter makes a conceptual contribution by investigating the question How to make best use of big data to inform policy-making and complex problem-solving? and provides a generic, holistic and integrated framework for big data governance based on semantic web principles.
Key Terms in this Chapter
Practical Wisdom: The capacity to make informed, rational judgments without recourse to a formal decision procedure.
Data Dynamics: The dynamics that emerge from the processes of iterative transition from creation; through usage, transformation, and movement; to the diffusion of valuable data.
Complex Problem-Solving: Refers to the co-production of knowledge arising from the collaboration of multiple knowledge providers seeking global public goods by embracing a balanced perception between social, economic and environmental challenges and goals.
Case-Based Reasoning: Aims to (re)use previous experiences defined as codified rules and strong domain models (cases) to deal with challenged situation using similarity retrieval techniques.
Big Data: Enormous amount of unstructured, heterogeneous and distributed data that is constantly changing over time and depending on the surrounding context. Big data can be seen as both a resource and a process, both of which are linked to the different interactions occurring on the web.
Meta-Ontology: A whole ontology containing just the global key concepts.
Knowledge: Meaningful, subjective, and semantically constructed information by integration of data from selected sources and giving it meaning using ontological representation of the knowledge domain (contextualization).
Complexity Science: Aims to understand how things are connected with each other, and how these interactions work together. It is concerned with the study of emergent order in what otherwise may be considered as very disorderly systems.
Evidence: Based on understandable and context-rich insights elicited from knowledge, evidence is an abstract and structured pattern disembodied from its context of creation (de-contextualized/generalized model).
Wisdom Governance: To develop a cross-domain and moral-driven system of governance that emerges out of the transaction between practicing the acquired wisdom while focusing on the ethical dimension, establishing a connection with the strategic roadmap of the larger system (organization or market), as well as assessing the progress and evaluating the impact.
Structuration Theory: Aims to conceptualize the interplay in social systems as an inseparable and intricate “duality” explaining the production and reproduction of dynamic social structures as they coevolved with human interactions, over space and time.
Policy-Making: The act of creating a deliberate system of principles to solve problems, guide decisions, improve the quality of life and achieve global prosperity.
GenID Framework to Big Data Governance: A generic approach that aims to leverage from explicit and formal representations of knowledge domains (ontologies) to structure and represent useful and relevant data in a uniform way, so it could be mined and leveraged to meet organizational requirements for linking information, detecting and structuring emergent process, and providing insight into and from spontaneous communities and emergent collaborative structures.
Wisdom: Using moral-based evidences to inform reasoning while espousing a moral lens towards making wise judgment.
Mother-Ontology: A whole large ontology containing all detailed specifications of modules.
Interoperability: To ensure cross-domain sharing of knowledge patterns by translating big data stores into knowledge bases using ontologies.
Wisdom Memory: Knowledge base of structured representations of past problem-solving experiences, considered as a practically-proved source of problem-solving expertise.
Sub-Ontology: A reusable module of a larger ontology which is self-contained and logically consistent, as well as tied to other sub-ontologies within the mother ontology.
Ontology Modularization: An approach allowing an ontology to be perceived simultaneously as a whole and as a set of parts (modules). A modular ontology can be designed either by composition (independently developing modules that can be integrated coherently and uniformly) or decomposition (extracting independent modules from an integrated ontology for supporting a particular use case).