Analytics in Public Policy Related to Service Sector

Web Services ◽  
2019 ◽  
pp. 185-203
Author(s):  
Maryam Ebrahimi
Keyword(s):  
Public Policy ◽  
Decision Making ◽  
Big Data ◽  
Competitive Advantage ◽  
Rapid Growth ◽  
Financial Services ◽  
Policy Making ◽  
Service Sector ◽  
Fraud Detection ◽  
Tools And Techniques

Big Data is transforming industries such as healthcare, financial services and banking, insurance, pharmacy, and telecommunication. Big Data concerns datasets that are not only big, but also high in variety and velocity, which makes them difficult to manage applying traditional tools and techniques. Big Data causes multitude benefits and advantages for industries such as marketing and selling, fraud detection, competitive advantage, risk reduction, and finally decision making and policy making. Due to the rapid growth of such data, methodologies and conceptual architectures need to be studied and provided in order to handle and extract value and knowledge from these data. The purpose of this chapter is studying Big Data benefits, characteristics, methodologies, and conceptual architectures in five different industries. Finally, according to the studies, a comprehensive methodology and architecture are proposed which might be applicable in service sector and one of the useful outcomes can be public policies.

Analytics in Public Policy Related to Service Sector

2017 ◽  
pp. 34-53
Author(s):  
Maryam Ebrahimi
Keyword(s):  
Public Policy ◽  
Decision Making ◽  
Big Data ◽  
Competitive Advantage ◽  
Rapid Growth ◽  
Financial Services ◽  
Policy Making ◽  
Service Sector ◽  
Fraud Detection ◽  
Tools And Techniques

Big Data is transforming industries such as healthcare, financial services and banking, insurance, pharmacy, and telecommunication. Big Data concerns datasets that are not only big, but also high in variety and velocity, which makes them difficult to manage applying traditional tools and techniques. Big Data causes multitude benefits and advantages for industries such as marketing and selling, fraud detection, competitive advantage, risk reduction, and finally decision making and policy making. Due to the rapid growth of such data, methodologies and conceptual architectures need to be studied and provided in order to handle and extract value and knowledge from these data. The purpose of this chapter is studying Big Data benefits, characteristics, methodologies, and conceptual architectures in five different industries. Finally, according to the studies, a comprehensive methodology and architecture are proposed which might be applicable in service sector and one of the useful outcomes can be public policies.

Examining the potential of textual big data analytics for public policy decision-making: A case study with driverless cars in Denmark

2020 ◽  
Vol 98 ◽  
pp. 68-78 ◽  
Author(s):  
Aseem Kinra ◽  
Samaneh Beheshti-Kashi ◽  
Rasmus Buch ◽  
Thomas Alexander Sick Nielsen ◽  
Francisco Pereira
Keyword(s):  
Public Policy ◽  
Decision Making ◽  
Big Data ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Policy Decision ◽  
Driverless Cars ◽  
Policy Decision Making

Integrating Ecosystem Management and Environmental Media for Public Policy on Public Health and Safety

2018 ◽  
pp. 321-338
Author(s):  
Augustine Nduka Eneanya
Keyword(s):  
Public Health ◽  
Public Policy ◽  
Decision Making ◽  
Environmental Policy ◽  
Policy Making ◽  
Health And Safety ◽  
Scientific Data ◽  
Scientific Uncertainty ◽  
Public Health And Safety

Over the past three decades, the relationship between ecology and public policy has changed because of the increasing role of scientific uncertainty in environmental policy making. While earlier policy questions might have been solved simply by looking at the scientific technicalities of the issues, the increased role of scientific uncertainty in environmental policy making requires that we re-examine the methods used in decision-making. Previously, policymakers use scientific data to support their decision-making disciplinary boundaries are less useful because uncertain environmental policy problems span the natural sciences, engineering, economics, politics, and ethics. The chapter serves as a bridge integrating environmental ecosystem, media, and justice into policy for public health and safety. The chapter attempts to demonstrate the linkage between the environmental policy from a holistic perspective with the interaction of air, water, land, and human on public health and safety.

Big Data, Knowledge, and Business Intelligence

2018 ◽  
pp. 943-950
Author(s):  
G. Scott Erickson ◽  
Helen N. Rothberg
Keyword(s):  
Big Data ◽  
Knowledge Management ◽  
Competitive Advantage ◽  
Intellectual Capital ◽  
Business Intelligence ◽  
Competitive Intelligence ◽  
Business Analytics ◽  
The Past ◽  
The People ◽  
Tools And Techniques

Knowledge management (KM), intellectual capital (IC), and competitive intelligence are distinct yet related fields that have endured and grown over the past two decades. KM and IC have always differentiated between the terms and concepts of data, information, knowledge, and wisdom/intelligence, suggesting value only comes from the more developed end of the range (knowledge and intelligence). But the advent of big data/business analytics has created new interest in the potential of data and information, by themselves, to create competitive advantage. This new attention provides opportunities for some exchange with more established theory. Big data gives direction for reinvigorating the more mature fields, providing new sources of inputs and new potential for analysis and use. Alternatively, big data/business analytics applications will undoubtedly run into common questions from KM/IC on appropriate tools and techniques for different environments, the best methods for handling the people issues of system adoption and use, and data/intelligence security.

Big Data, Semantics, and Policy-Making

2019 ◽  
pp. 154-178
Author(s):  
Lamyaa El Bassiti
Keyword(s):  
Decision Making ◽  
Big Data ◽  
Policy Making ◽  
Policy Design ◽  
Semantic Technologies ◽  
Design And Implementation ◽  
Policy Choices ◽  
Data Semantics ◽  
Policy Argument

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.

A Conceptual Model for Describing the Integration of Decision Aspect into Big Data

2022 ◽  
pp. 294-318
Author(s):  
Fatma Chiheb ◽  
Fatima Boumahdi ◽  
Hafida Bouarfa
Keyword(s):  
Decision Making ◽  
Big Data ◽  
Competitive Advantage ◽  
Conceptual Model ◽  
Big Data Analytics ◽  
Decision Makers ◽  
Modern Business ◽  
Definition Of ◽  
A Company ◽  
Support Decision Making

Big Data is an important topic for discussion and research. It has gained this importance due to the meaningful value that could be extracted from these data. The application of Big Data in the modern business allows enterprises to take faster and smarter decisions, achieving a real competitive advantage. However, a lot of Big Data projects provide disappointing results that don't address the decision-makers' needs due to many reasons. The main reason for this failure can be summarized in neglecting the study of the decision-making aspect of these projects. In light of this challenge, this study proposes the integration of decision aspect into Big Data as a solution. Therefore, this article presents three main contributions: 1) Clarify the definition of Big Data; 2) Presents BD-Da model, a conceptual model describes the levels that should be considered to develop a Big Data project aiming to solve a problem that calls a decision; 3) Describes a particular, logical, requirements-like approach that explains how a company develops a Big Data analytics project to support decision-making.

Integrating Ecosystem Management and Environmental Media for Public Policy on Public Health and Safety

2021 ◽  
pp. 612-629
Author(s):  
Augustine Nduka Eneanya
Keyword(s):  
Public Health ◽  
Public Policy ◽  
Decision Making ◽  
Environmental Policy ◽  
Policy Making ◽  
Health And Safety ◽  
Scientific Data ◽  
Scientific Uncertainty ◽  
Public Health And Safety

Over the past three decades, the relationship between ecology and public policy has changed because of the increasing role of scientific uncertainty in environmental policy making. While earlier policy questions might have been solved simply by looking at the scientific technicalities of the issues, the increased role of scientific uncertainty in environmental policy making requires that we re-examine the methods used in decision-making. Previously, policymakers use scientific data to support their decision-making disciplinary boundaries are less useful because uncertain environmental policy problems span the natural sciences, engineering, economics, politics, and ethics. The chapter serves as a bridge integrating environmental ecosystem, media, and justice into policy for public health and safety. The chapter attempts to demonstrate the linkage between the environmental policy from a holistic perspective with the interaction of air, water, land, and human on public health and safety.

Application of Statistical Analysis Tools and Concepts to Big Data and Predictive Analytics to New Product Development

2020 ◽  
Vol 3 (1) ◽  
pp. 17-35
Author(s):  
Brian J. Galli
Keyword(s):  
Decision Making ◽  
Big Data ◽  
Data Analysis ◽  
Product Development ◽  
Competitive Advantage ◽  
New Product Development ◽  
New Product ◽  
Big Data Analysis ◽  
Life Cycles ◽  
The Impact

In today's fiercely competitive environment, most companies face the pressure of shorter product life cycles. Therefore, if companies want to maintain a competitive advantage in the market, they need to keep innovating and developing new products. If not, then they will face difficulties in developing and expanding markets and may go out of business. New product development is the key content of enterprise research and development, and it is also one of the strategic cores for enterprise survival and development. The success of new product development plays a decisive role both in the development of the company and in maintaining a competitive advantage in the industry. Since the beginning of the 21st century, with the continuous innovation and development of Internet technology, the era of big data has arrived. In the era of big data, enterprises' decision-making for new product development no longer solely relies on the experience of decision-makers; it is based on the results of big data analysis for more accurate and effective decisions. In this thesis, the case analysis is mainly carried out with Company A as an example. Also, it mainly introduces the decision made by Company A in the actual operation of new product development, which is based on the results of big data analysis from decision-making to decision-making innovation. The choice of decision-making is described in detail. Through the introduction of the case, the impact of big data on the decision-making process for new product development was explored. In the era of big data, it provides a new theoretical approach to new product development decision-making.

Evidence-Based Policy Making in the Social Sciences

2016 ◽  
Keyword(s):  
Social Sciences ◽  
Public Policy ◽  
Big Data ◽  
Fuzzy Set ◽  
Policy Making ◽  
Design Thinking ◽  
Simulation Modelling ◽  
Social Scientists ◽  
Policy Makers ◽  
The Social

The purpose of this edited book is to make the case for why the social sciences are more relevant than ever before in helping governments solve the wicked problems of public policy. It does this through a critical showcase of new forms of discovery for policy-making drawing on the insights of some of the world’s leading authorities in public policy analysis. The authors have brought together an expert group of social scientists who can showcase their chosen method or approach to policy makers and practitioners. These methods include making more use of Systematic Reviews, Random Controlled Trials, the analysis of Big Data, deliberative tools for decision-making, design thinking, qualitative techniques for comparison using Boolean and fuzzy set logic, citizen science, narrative from policy makers and citizens, policy visualisation, spatial mapping, simulation modelling and various forms of statistical analysis that draw from beyond the established tools. Of course some of the methods the book refers to have been on the shelves for a number of decades but the authors would argue that it is only over the last decade or so that increased efforts have been made to apply these methods across a range of policy arenas. Other methods such as the use of analysis of Big Data or new fuzzy set comparative tools are relatively more novel within social science but again they have been selected for attention as there are growing examples of their application in the context of policy making.

scholarly journals E-Customer Profiling Technology in Service Industry

2020 ◽  
Vol 9 (4) ◽  
pp. 1390-1397
Keyword(s):  
Competitive Advantage ◽  
Financial Services ◽  
Service Sector ◽  
Service Industry ◽  
Research Question ◽  
Demographic Data ◽  
Customer Data ◽  
Service Companies ◽  
Customer Profiling ◽  
Customer Identification

Service companies are very customer oriented in serving the needs of the customer. Higher services require competitive advantage in companies, including customer identification or e-customer profiling. The aim of this qualitative research is to determine the trend of the technology used for e-customer profiling or customer identification, so that service companies obtain customer information deeper and more accurately with customer identification. The research question in this study is "what is the technology used for e-customer profiling in the service sector?". By using literature research, there are 39 technologies that support e-customer profiling in service companies. The most service companies in this study are banking and financial services, hospitality and other services. 11 technologies used include data mining, big data analysis, customer data platforms, data management, demographic data, digital identity, machine learning, biometric recognition, CRM, social network analysis and transaction data. Apart from the 11 technologies used, there are several other technologies that are currently starting to develop, such as blockchain technology, social CRM, semantic web, etc. Service companies may also consider using the latest technology according to the needs of service companies in e-customer profiling such as competitive advantage.

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