1.1 The Roots and Pillars of Analytics Work ......................................................4
1.2 Using Analytics in Management ..................................................................7
1.2.1 Managing Analytics Knowledge .......................................................7
1.2.2 Analytics Knowledge: Intelligence, Decisions, and Meaning ..........13
1.2.2.1 Intelligence .......................................................................14
1.2.2.2 Decision-Making Process .................................................15
1.2.2.3 Meaning ...........................................................................18
1.2.3 What Are the Types of Questions, Problems, and Tasks
in Analytics? ...................................................................................22
1.3 Thinking about Data and Analytics ...........................................................25
1.3.1 The Concepts of Small Data, Big Data, and Great Data .................27
1.3.2 Measurement Process ......................................................................32
1.4 Who Are Analytics Professionals? ............................................................. 34
1.5 First Steps in Analytics Process Design ......................................................43
References ...........................................................................................................49
This chapter is focused on presenting the basics of the analytics process. The core of the book concerns presenting the analytics process in management and how to develop a capacity for using analytics resources in order to improve corporate performance. With that purpose in mind, five sections are included to connect the analytics process with the setting in which it will be used. In our current setting of analytics processes, we relate these thoughts as part of the inductive and deductive work, the same as dealing with intuition, and quantitative and qualitative analysis of data. All of them are necessary in analytics-based solutions.
The chapter structure is based on starting with the main concepts that are coming from multiple disciplines and presenting the adaptation of these concepts to the management world. It is the case that we need to talk about analytics in general in the same way as knowledge and to put these concepts in the setting of organizations in which analytics are more business analytics and knowledge is more in the area of knowledge management.
In this chapter, we look for the way to connect the analytics process to knowledge creation, risk management, problem solving, and decision-making processes.
We consider analytics as a way to create knowledge and knowledge as a means to control risk. Analytics provides a better level of organizational intelligence, and analytics is converted into the neuroplasticity of the organizational brain connecting the dots of strategy design and strategy implementation. Analytics is the way to use intellectual capital—data, relationships, and processes—to compete and keep sustainable competitive advantages.
The aim of this chapter is to present concepts that describe the analytics process applied to a specific situation in an organization. For this, we have chosen the application of risk management (RM). We will first review how to design a methodology to improve the risk-modeling process, which, in this book, we treat as risk analytics or the use of the analytics process and knowledge management concepts and tools in an RM setting.
The chapter is organized through three concepts: the risk-modeling process in a transformation process of the organization, the concept of Big Data in RM, and the way that an enterprise risk knowledge management system (ERKMAS) can be built in order to respond to the needs of converting data, small and big, into knowledge and actions within organizations.
2.4 Conclusions
1. Analytics and KM are disciplines that are associated with ERM. The KMS needs to support ERM process development in common and different dimensions of the ERM processes. A better ERKMAS can provide support to the risk-modeling process as a common process in ERM. The ERKMAS includes the analytics process.
2. Assessment of risk organizational issues and the development of the analytics process are part of the creation of risk knowledge. The risk-modeling process is supported by the analytics process and follows similar steps for different problems to solve.
3. The management of the modeling process requires risk knowledge creation for effective and efficient development of solutions. There are tools to share knowledge and to produce results in the risk-modeling process that need identification, alignment and use of tools, methods, and technology from the analytics process.
4. The methodology summary for the development of risk-modeling knowledge is the following:
– Answering the questions related to the strategy and strategic planning in the organization
– Identifying the enablers to transfer risk knowledge from tacit to explicit knowledge and vice versa
– Understanding of flows of information to produce knowledge
– Understanding risk knowledge organization
– Searching for KM technologies and techniques
– Designing the ERKMAS to support risk modeling
– Connecting organizational performance metrics and the risk-modeling process
This chapter has presented a view of the analytics process based on KM processes using three risk management activities. The analysis and design of the ERKMAS is a task to perform in order to implement the analytics process. In the following chapter, we are going to review how these steps are related to management control systems. In the past, management control systems were seen as part of cost control.
In this book, we introduce the idea that management control systems are the systems to implement strategy at all dimensions of the expected value added from all stakeholders’ points of view.
Contents
3.1 Breaking Paradigms and Organizations as Systems ....................................86
3.2 Organizations and Management Control Systems ......................................88
3.3 Key Performance Indicators and Key Risk Indicators ................................ 90
References ...........................................................................................................94
This chapter is about the relationship between analytics, strategy, and management control systems. Analytics is an input for a strategic process, for creating and defining strategies. Once the strategy has been built, the next step is to find the way to achieve a good implementation. Many strategies may be good, but the implementation may not be. Bad strategy and bad implementation increase strategic risk.
Management control systems are the set of systematic activities used to implement the strategy to provide value to all stakeholders.
Analytics is part of the management control system. This is through the way in which it provides the insights that support any steps for implementation and in how using strategic intelligence can provide the structure for maintaining the competitive advantages of organizations.
This chapter has three sections: First, to present different kinds of organizations and encourage an open-minded and different way of looking at organizations; second, to connect organizations and management control systems; and third, to indicate how the measurement systems that analytics helps to create are based on key performance indicators and key risk indicators.
Contents
4.1 Introduction ...............................................................................................99
4.1.1 Purpose of the Study .......................................................................99
4.1.2 About the Background of This Work ..............................................99
4.1.3 What Is the Scope of This Work? ....................................................99
4.1.4 Definition of the Key Concepts ....................................................100
4.2 What Is the Work Performed in This Field? .............................................100
4.2.1 Knowledge-Based View of the Firm ..............................................100
4.2.2 Data, Information, Knowledge, and Wisdom or Intelligence
(DIKW) .......................................................................................101
4.2.3 KM and Intellectual Capital .........................................................101
4.2.4 Leveraging Knowledge ..................................................................102
4.2.5 Knowledge and Intelligence ..........................................................102
4.2.6 Big Data and Business Analytics ...................................................103
4.3 Description of the Problem ......................................................................104
4.3.1 Research Questions or Hypotheses ...............................................104
4.4 What Was the Methodology of the Problem Resolution? .........................104
4.4.1 What Was the Data Used For? ......................................................106
4.4.2 What Were the Models and Concepts Used in This Study? ..........106
4.4.3 About Validity and Reliability in This Work .................................107
4.5 What Were the Results and Their Meaning or Context? ..........................107
4.5.1 How Are These Results Meaningful for Organizations
and for Future Research? ..............................................................107
4.5.2 Where, How, and When to Use It? ...............................................109
4.6 Conclusions and Recommendations .........................................................109
4.6.1 Are the Objectives of the Research Achieved? ...............................109
4.6.2 Strategic ........................................................................................110
Acknowledgment ..............................................................................................110
References .........................................................................................................110
5.1 Introduction .............................................................................................114
5.1.1 Development of Big Data .............................................................114
5.2 Overview of Big Data Research ................................................................115
5.3 Analytics and Big Data in Higher Education ...........................................116
5.3.1 Conceptualizing Big Data in Higher Education ...........................117
5.3.2 Institutional Analytics ..................................................................120
5.3.3 Information Technology Analytics ...............................................120
5.3.4 Academic or Program Analytics ....................................................120
5.3.5 Learning Analytics .......................................................................120
5.4 Sources and Types of Big Data in Higher Education ................................121
5.4.1 Opportunities ...............................................................................121
5.5 Challenges of Implementation ..................................................................123
5.6 Summary and Future Directions ..............................................................124
Author’s Notes ...................................................................................................125
References .........................................................................................................125
Context
The problem is located in social sciences and humanities, that is, organization management, e-marketing, and business informatics.
Purpose
The aim of this chapter is to verify a hypothesis that Google Analytics (GA) is a prosumption tool for Internet data analysis that can be used by a company to effectively (in a simple way and on its own) manage its website, that is, manage the website content and traffic.
Design
To test the hypothesis, an experiment was designed and conducted on the website of Soluzioni IT, on which GA was installed and activated. The main goal was set, and then it was translated into particular goals. Next, measurable KPIs were assigned to the goals. During the experiment, the GA indicators were monitored, the impact of the changes on the GA values was assessed, and subsequent changes were designed and implemented. It involved active modification of website content and graphic design. The process was cyclically repeated in the four stages of the study.
Findings
Web analytics and GA allow owners to comprehensively monitor the parameters of any website. The software can track a user’s path on Internet platforms and visualize the results. Thanks to GA results, it is possible to precisely target advertisements and use various means to strengthen marketing initiatives and design websites, which can generate more conversions. GA provides information on how users find a site and what their interactions are. It enables comparison of changes in users’ behavior due to, for example, improvements in site content and design. It is a good analytics tool that ensures comprehensive analysis of the whole site from multiple perspectives, depending on users’ needs.
Research Limitations/Implications
GA is an analytics tool used to analyze Internet data from various perspectives. This chapter focuses on the analysis of data to optimize website content and traffic. In addition, GA analyses are able to answer other questions that are of critical importance to running a business, for example, at what stage and why do users abandon purchases in a store? Moreover, GA itself is not able to arrive at any conclusions. Using this tool requires the ability to interpret data from individual reports and identify relationships between particular data as well as knowledge about products, services, and customers’ needs. A lack of such skills and knowledge can lead to wrong decisions, which will negatively affect website goals. It implies the necessity for continuous further research.
Practical Implications
GA is a supporting tool that supplies detailed information about, for example, the behavior of visitors to a particular website, the usability of the content provided, etc. The users themselves can set website goals, assign KPI to the goals, monitor and compare the data they need, analyze goal conversion, etc. This knowledge helps to improve the decision-making process and optimize the management of marketing activities.
Originality/Value
The results of web analyses help improve the quality of services, save resources, or make some business processes more efficient. Based on the data, business can be improved. But it is also necessary to assess the path between the data and improvement activities, that is, those increasing company efficiency. There is one principle: Analytics should lead to continuous optimization. When a report is ready, the data should be analyzed, and recommendations for changes made and then implemented (this is a key moment of the entire process and the most important stage as it brings a company real profits).
6.1 Introduction .............................................................................................129
6.1.1 Purpose of the Study .....................................................................129
6.1.2 About the Background of This Work ............................................130
6.1.3 What Is the Scope of This Work? ..................................................130
6.1.4 Definition of the Key Concepts ....................................................130
6.2 What Is the Work Performed in This Field? .............................................131
6.2.1 General Assumptions about Web Analytics ..................................131
6.2.2 Measurement ................................................................................132
6.2.3 Analysis ........................................................................................133
6.2.4 Reporting .....................................................................................133
6.2.5 Conclusion Development ..............................................................133
6.2.6 The Accomplishment of Case Study Goals ....................................134
6.3 Description of the Problem and Method to Solve It .................................135
6.3.1 Definition of the Problem That Is Analyzed..................................135
6.3.2 Research Questions or Hypotheses ...............................................136
6.3.3 What Was the Methodology of the Problem Resolution? ..............136
6.3.4 How Was the Research Designed? ................................................136
6.3.5 What Data Was Used? ..................................................................137
6.4 What Were the Results and Their Meaning/Context? ..............................138
6.4.1 Why Is This Approach to the Solution Valuable? ..........................138
6.4.2 What Are the Results and Their Interpretation? ............................138
6.4.3 How Are These Results Meaningful for Organizations
and for Future Research? ..............................................................141
6.4.4 Where, How, and When to Use It? ...............................................142
6.5 Conclusions and Recommendations .........................................................142
6.5.1 Are the Objectives of the Research Achieved? ...............................142
6.5.2 Operational and Tactical ..............................................................142
6.5.3 Strategic ........................................................................................142
References .........................................................................................................142
Contents
7.1 Introduction .............................................................................................146
7.1.1 Purpose of the Study .....................................................................146
7.1.2 About the Background of This Work ............................................147
7.1.3 What Is the Scope of This Work? ..................................................147
7.1.4 Definition of the Key Concepts ....................................................147
7.2 What Data Was Used? .............................................................................148
7.3 What Were the Models and Concepts Used in This Study? ......................149
7.3.1 Knowledge Representation ...........................................................149
7.3.2 Knowledge Reasoning ..................................................................149
7.3.3 DSS Model Output ......................................................................151
References .........................................................................................................153
Context
Computer-based analysis to support decision making in organizations is a crucial competitive factor. Cause–effect analysis is an important component of these analyses as it identifies cause–effect relationships among data, which can be applied in decision-making situations to improve the decision-making quality.
Purpose
This chapter envisions a concept for the support of cause–effect analyses, which is based on an integrated knowledge base with cause–effect relationships and a knowledge reasoning process, according to the human approach to solving problems.
Design
The knowledge base integrates both structured and unstructured knowledge from a variety of organizational sources. The knowledge reasoning is divided into three phases during which the decision situation is (1) isolated and matched into the knowledge base, (2) explored for potential causes (including their validation), and finally, (3) verified
and, if necessary, adjusted by the user. As a proof of concept, this concept is applied manually to the slightly extended example data set from Microsoft for the SQL Server 2012. For the creation of the knowledge base, knowledge about the cause–effect relationships is extracted manually from the database schemas and integrated with additional expert knowledge about further cause–effect relationships.
Findings
The result is an ontology with cause–effect relationships for this specific data set. Based on a fictitious decision scenario, the phases of the knowledge reasoning are played through. The exploration of the ontology will typically identify cause–effect chains with various potential explanations alongside the levels of the chain. These potential cause–
effect chains are implemented in a DSS model with multiple layers.
The resulting DSS model enables the evaluation of the impact of the identified cause–effect chains for the specific decision scenario.
8.1 Introduction .............................................................................................158
8.1.1 Purpose of the Study .....................................................................158
8.1.2 About the Background of This Work ............................................158
8.1.3 What Is the Scope of This Work? ..................................................158
8.1.4 Definition of the Key Concepts ....................................................159
8.2 What Is the Work Performed in This Field? .............................................159
8.2.1 Theories and Models Used for Approaching the Problem .............159
8.3 Description of the Problem ......................................................................160
8.3.1 Definition of the Problem That Is Analyzed..................................160
8.3.2 Research Questions or Hypotheses ...............................................161
8.4 What Was the Methodology of the Problem Resolution? .........................161
8.4.1 How Was the Research Designed? ................................................162
8.4.2 What Were the Data, Models, and Tests Used? ............................162
8.4.2.1 What Were the Models and Concepts Used in This Study? .............................................................................162
8.4.2.2 What Was the Way to Test/Answer the Hypotheses/
Research Questions? .......................................................162
8.5 What Were the Results and Their Meaning or Context? ..........................162
8.5.1 Why Is This Approach to the Solution Valuable? ..........................162
8.5.2 What Are the Results and Their Interpretation? ............................163
8.5.3 How Are These Results Meaningful for Organizations
and for Future Research? ..............................................................165
8.5.4 Where, How, and When to Use It? ...............................................166
8.6 Conclusions and Recommendations .........................................................166
8.6.1 Are the Objectives of the Research Achieved? ...............................166
References .........................................................................................................166
Context
Within the context of a smart and inclusive society, cyber security is an important issue, which must be analyzed and discussed in the field of science and in practice. Collective intelligence, which emerges in the activities of online communities, is a new quality of civic engagement that grants more effective decisions and compliance with societal
needs. Various social technologies have created possibilities for society members to communicate despite the limitations of the physical world, but they have brought high prospects for sophisticated crimes and other violations of rights and obligations of users, administrators, and states as regulatory bodies.
Purpose
The purpose of this chapter is to connect several independent fields of research: analytics, social technologies, civil engagement, collective intelligence, and cyber security in order to reveal the main threats of using social technologies during the process of engaging society in socially responsible activities. The sources of data are growing, and data
mining could be used in variety of ways. Analytics allow the combination of different observations in order to see new patterns.
Design
Research solutions were approached by fulfilling the analyses of regulatory framework for online communities, presenting Internet user analytics in Lithuania as well as identifying the cyber security perspective in online communication. As a result, the main trends were identified in the context of online community projects in Lithuania based on a quantitative public opinion survey conducted in 2013.
Findings
The quantitative research results helped to create a profile of frequent Internet users in Lithuania, where 58 out of every 100 people are using the Internet daily or a few times per week. Frequent Internet users are younger than 39 years old, well educated, and living in the biggest cities in the country. Mostly they use the Internet for communication or looking for professional or general information. Frequent Internet users use social networks and online communities in order to realize some personal interests, connected mostly with hobbies or other areas of personal interest. They perform passive activities, such as getting actual information or broadening one’s view, and mostly avoid active behaviors, such as commenting or sharing information or knowledge. Despite the high accessibility of the web in Lithuania, people are not inclined to join socially oriented activities. This fact creates an obvious finding that accessibility is the condition but not a catalyst for increasing the social involvement of society. Even those respondents who are used to visiting websites oriented toward social problem solving, most are not active and mainly susceptible to observing the ongoing processes rather than taking part in them.
People using the Internet every day are more often involved in socially oriented activities, and it could be concluded that digital competencies, in general, have a positive influence on online civic engagement.
From the cyber security perspective, respondents do not rank the legal risks as critically important, but they are aware of cyber security issues and strongly support most offered ideas about safe and secure operations online. This shows that people in Lithuania still lack experience in online civic activities and cannot identify independently what problems they might face in virtual space. A united effort is necessary—from the government and law enforcement to the general public—to meet the evolving challenges in securing cyberspace.
Research Limitations/Implications
This research mostly focuses on the context of online community projects in Lithuania (based on a quantitative public opinion survey conducted in 2013). The results of research in the future may be validated by further qualitative research as well as extended worldwide.
Practical Implications
This chapter stresses the importance of personal data protection in online networks and identifies the main legal problems that arise in networked society. These implications may be used in future investigations as well as in designing necessary legal regulations in this field.
Originality/Value
The topic of civic engagement through social networks is considered to be a novelty as is taking an analytic approach toward it. This research adds value to the stimulation of socially oriented activities on the Internet as well as identifying the main threats that the people involved may face.
Contents
9.1 Introduction .............................................................................................172
9.1.1 Purpose of the Study .....................................................................172
9.1.2 About the Background of This Work ............................................172
9.1.3 What Is the Scope of This Work? ..................................................173
9.1.4 Contextual Taxonomies ................................................................173
9.2 What Is the Work Performed in This Field? .............................................175
9.2.1 Theories and Models Used for Approaching the Problem .............175
9.3 Description of the Problem ......................................................................176
9.3.1 Definition of the Problem That Is Analyzed..................................176
9.3.2 Research Questions or Hypotheses ...............................................176
9.3.3 What Was the Methodology of the Problem Resolution? ..............177
9.3.4 How Was the Research Designed? ................................................177
9.3.5 What Data Was Used? ..................................................................177
9.3.6 What Were the Models and Concepts Used in This Study? ..........177
9.3.6.1 Visualizing Data Governance in Health Care Practices ....177
9.4 How Were the Hypotheses/Research Questions Tested/Answered? ..........181
9.4.1 Scenario Analysis: The Need for Data Governance .......................181
9.4.1.1 Scenario 1: Scrutinizing the Business Case
of an Operating Theater in the State of Victoria .............181
9.4.1.2 Scenario 2: Information Focus in a Public Primary
Care Provider ..................................................................182
9.4.1.3 Scenario 3: Influence of Genomic Data ..........................182
9.4.1.4 Scenario 4: Optimizing Income ......................................183
9.4.1.5 Scenario 5: Campus, Network, and Regional .................183
9.4.1.6 Scenario 6: Cancer Research Information Exchange Framework ......................................................................184
9.4.2 About the Validity and Reliability in This Work ...........................185
9.5 What Were the Results and Their Meaning/Context? ..............................185
9.5.1 Why Is This Approach for the Solution Valuable? .........................185
9.5.2 What Are the Results and Their Interpretation? ............................185
9.6 How Are These Results Meaningful for Organizations and for Future Research?..................................................................................................186
9.6.1 Where, How, and When to Use It? ...............................................186
9.7 Conclusions and Recommendations .........................................................186
9.7.1 Were the Objectives of the Research Achieved? ............................186
9.7.2 Operational and Tactical ..............................................................186
9.7.3 Strategic ........................................................................................187
Context
The acute health care sector is a data-rich and information-poor environment in Australia. Conversely, information is a crucial yet underutilized asset for managing patients in health care organizations. To ensure that information being mined and analyzed is of quality and to leverage the power of data analytics tools, a data governance framework
needs to be in place. The “data concierge” function will provide such a framework for an organization in analyzing its data. Analytics and predictive analytics are used to manage current and future requirements both from a management and “changing models of care” perspective.
Analytics can work hand-in-hand with an organization’s strategic plan that can provide evidence-based data and information to support its plan.
Purpose
We aim to enhance the understanding of how data governance and analytics can help address current issues in the Australian context of health care in order to achieve better information outcomes in the acute health sector.
Design
In this exploratory research investigation, we present scenarios, that is, clinical and nonclinical case studies that demonstrate the use of multiple tools and methodologies for delivery of quality information to the acute health sector in Australia. These scenarios build the case for health data governance. Subsequently, we aim to enhance the under-
standing of how data governance and analytics can help address current issues in the Australian context of health care to achieve better information outcomes.
Findings
Data quality has become a common goal across all performance areas.
For this purpose, it is best to adopt a framework, illustrated in this chapter, that provides the context of care for each activity undertaken. This can result in the convergence of good quality data that can result in better patient outcomes. For example, good quality data can be used for continuum of cancer care and in support of research for curing cancer.
Building governance arrangements into the regular business of health practices is advisable rather than managing information governance as a project. For this purpose, team participation is required with all stakeholders involved understanding their roles.
Prioritize and execute modestly scoped activities where there is relevance and support.
Data quality needs to be measured so that it gets managed efficiently.
Even very basic data quality reporting helps instill it as a relevant business activity.
Keep it simple; for example, just go for a basic data dictionary in the first instance. Implement the governance organization as data subjects are addressed, and implement data quality reporting as subject areas are addressed.
Research Limitations/Implications
In addition to contributing to the body of knowledge, the findings will enable a better appreciation of the analytics and data governance framework and how they apply to health care practices.
Practical Implications
Practical recommendations are offered for establishing and operating analytics and data governance frameworks as well as approaches for justifying the investment for health practices.
Originality/Value
The Australian health care and acute care framework has a long way to go before the desired data quality is achieved. We have visualized some scenarios and a framework that can be applied to health care practices in this chapter. After the e-health records implementation occurs nationwide, the Australian health care sector needs to strive to achieve data quality, using the data governance and concierge techniques reviewed in this chapter. These techniques are valuable for the Australian health sector as, only then, the distant dream of acute health care management and continuum of care for Australians within their homes can become a reality.
Contents
10.1 Introduction .............................................................................................193
10.1.1 Purpose of the Study .....................................................................193
10.1.2 About the Background of This Work ............................................195
10.1.3 The Australian Health Milieu .......................................................195
10.1.4 What Is the Scope of This Work? ..................................................196
10.1.5 Definition of the Key Concepts ....................................................196
10.2 What Is the Work Performed in This Field? .............................................197
10.3 Description of the Problem ......................................................................197
10.3.1 Definition of the Problem That Is Analyzed..................................197
10.3.2 Research Questions or Hypotheses ...............................................197
10.4 What Was the Methodology of the Problem Resolution? .........................197
10.5 How Was the Research Designed? ............................................................198
10.5.1 Developing the Conceptual Framework ........................................198
10.5.2 What Data Was Used? ..................................................................199
10.6 What Were the Models and Concepts Used in This Study? ..................... 200
10.6.1 Intersection of Innovation Translation and ANT ........................ 200
10.6.2 In What Way Were the Hypotheses/Research Questions
Tested/Answered? ........................................................................ 200
10.6.3 About Validity and Reliability in This Work .................................202
10.7 What Were the Results and Their Meaning/Context? ..............................202
10.7.1 Why Is This Approach for the Solution Valuable? .........................202
10.7.2 What Are the Results and Their Interpretation? ............................202
10.8 How Are These Results Meaningful for Organizations and for Future
Research?................................................................................................. 204
10.8.1 Where, How, and When to Use It? ...............................................205
10.9 Conclusions and Recommendations ........................................................ 206
10.9.1 Are the Objectives of the Research Achieved? .............................. 206
10.9.2 Operational and Tactical ..............................................................207
10.9.3 Strategic ........................................................................................207
References .........................................................................................................207
Context
This research is located in Australian hospitals within the knowledge domain of health informatics and analytics. Radio frequency identification (RFID) is an evolving technology innovation that uses radio waves for data collection and transfer without human involvement. With its success worldwide in hospitals for improving efficiencies and thereby quality of care, the technology was piloted in Australian hospitals in the late 2000s. However, existing literature (in 2013) reflected limited success with full-scale implementation and the emerging view that the sociotechnical factors in implementation are not being considered.
Purpose
Information systems researchers in Australia had begun emphasizing sociotechnical approaches in innovation adoption and translation of technology in the context as well as visualizing the information using analytical techniques.
Design
A qualitative research study with a multiple case study method was set in this premise in 2007 and aimed at addressing the knowledge gap. Information for the case studies was obtained through a rigorous data collection process, through semistructured interviews, focus groups, concept mapping, and documentation analysis. The findings were then validated for currency with practitioners in the field in 2013.
Innovation translation is an approach that posits that any innovation needs to be customized and translated into context before it can be adopted. To understand the “social” aspects that may be involved in adoption, a lens informed by the actor–network theory (ANT) helps reconstruct the implementation process, investigating social networks and relationships that influenced innovation translation. The innovation translation approach to theorization and knowledge abstraction, informed by the ANT removed the need for considering the “social” and the “technical” in separate modes. More importantly, an ANT-informed lens acted as an augmented filter, enabling an in-depth view
of the data and information visualization of knowledge abstracted in this research investigation.
Findings
The network of actors and their relationships is extremely complex and is key to operations in Australian hospitals. As found in the initial period of investigations, both orderlies and nurses felt disempowered by technology introduction. The nurses did not endorse imposing a technology that disrupted the workflow. It had taken three years and a nurse-in-charge to redeploy the technology that was then accepted with ease by all stakeholders, including clinicians. As was also found to be true in the final phase of investigations, the introduction of the technology by a nurse enabled successful translation of RFID. The findings revealed a silent web of relationships between the key actors in hospitals in relation to promoting RFID technology. The constant communication flow between orderlies to orderlies, nurses to orderlies, and nurses to nurses across the private and public areas of Case 1 and nurse–nurse, nurse–clinician, and nurse–ICT relationships, which are not clearly visible at the onset, is indeed the most powerful social factor for RFID implementation.
Additionally, expert opinion/validation from the industry sector indicates that, in Australian hospitals, the nurse is the powerful and influential factor in technology translation. For example, the ICT department feels imposed upon by medical directors, but if the nurse is the person raising the issue, they will accept it, take it on board, and enable it. Doctors do not question nurses. Neither do the patient care orderlies. The nurse happens to be the lynchpin in Australian hospitals. This knowledge abstraction is significant from the Australian perspective, which would not have been possible without the interpretive stance of this investigation, visualizing the knowledge through an ANT-informed lens and eliciting the key social factors in translation further validated by industry experts.
The finding is of significant value to large hospitals on the verge of RFID deployment. Conversely, the research extended the innovation translation theory framework and augmented the field of ANT through visualization techniques. This value addition has significant implications for academia as it added to the body of knowledge that is currently rather limited in the field of health informatics within Australia.
Research Limitations/Implications
This research investigation was done over a period of seven years when RFID as a technology was still being considered and not actively being deployed by Australian hospitals. However, after the publication of this study, hospitals in Australia have actively begun considering the technology in various parts of their operations. Further case investigations on this progress and the use of analytical techniques in visualization would be highly valuable for the health informatics sector.
From a global perspective, this study could become a framework for commissioning similar investigations based in other countries, such as Canada, within the health sector.
Practical Implications
It is evident that RFID innovation translation requires commitment of a key stakeholder in Australian hospital operations who is able to influence others, a view that is validated by industry practitioners. This key stakeholder is a nurse. Similarly, other countries and hospitals within Australia that are considering RFID deployment may find this as a strong practical consideration for their success.
Originality/Value
The contributions of this research are in that it addresses the sociotechnical gap evident in academic literature pertaining to RFID technology adoption in Australian hospitals; it augments the body of knowledge concerning innovation translation in health informatics informed by ANT analysis. The ANT lens visualizes that RFID was facilitating the renegotiation and improvement of network relationships between the people involved as well as the technology. The knowledge abstraction and visualization enabled by ANT is a major contribution to the field.
Contents
11.1 Introduction...........................................................................................213
11.1.1 Purpose of the Study .................................................................213
11.2 About the Background of This Work .....................................................216
11.3 What Is the Scope of This Work? ...........................................................218
11.4 Definition of the Key Concepts .............................................................218
11.5 Description of the Problem ....................................................................221
11.5.1 Research Questions or Hypotheses .......................................... 222
11.6 What Was the Methodology of the Problem Resolution? ...................... 222
11.7 How Was the Research Designed? ........................................................ 222
11.7.1 What Data Was Used? ............................................................. 222
11.8 What Were the Models and Concepts Used in This Study? ...................229
11.8.1 What Was the Way to Test/Answer the Hypotheses/Research Questions? ................................................................ 234
11.9 What Are the Results and Their Interpretation? .................................... 234
11.9.1 How Are These Results Meaningful for Organizations and for Future Research? ...........................................................235
11.10 Conclusions and Recommendations ......................................................235
11.10.1 Are the Objectives of the Research Achieved? ...........................235
Context
Annual influenza epidemics impose great losses in both human and financial terms. A key question arising in large-scale vaccination programs is the need to balance program costs and public benefits. Risks occur in the vaccination supply chain due to the stochastic nature of the vaccination process, which fluctuates from year to year, depending
on many factors that are difficult to predict and control. Large data sets representing the information involved are Big Data sets of sizes far beyond the ability of commonly used software packages to capture, process, and manage data within a reasonable computing time. We suggest an entropic approach to handle this challenging problem.
Purpose
For a vaccination supply chain consisting of manufacturers, distribution centers, warehouses, pharmacies, clinics, and customers, we seek to reduce the problem size and then decrease the total expenses of all stakeholders while taking into account public benefits on a nationwide level. We propose an analytics-driven research approach for enhancing the efficiency of influenza vaccination programs, using supply chain concepts. We seek to minimize the total cost of the vaccination supply chain while upholding the individual interests of its stakeholders.
Design
Information entropy is widely used in information control and management as a measure of uncertainty in a random environment. Extending Shannon’s classical information entropy concept used in information theory, we use the term to quantify and evaluate the expected value of the information contained in a supply chain with uncertain but predictable data about the costs and benefits. An integer-programing model is developed in which the problem of minimizing the total loss is effectively solved in a reduced vaccination supply chain.
Findings
Knowing the history of adverse events, we estimate the entropy and knowledge about the risks occurring in the vaccination supply chain, reduce the problem size, define the most vulnerable components in the supply chain, and evaluate the economic loss. This new analytics approach permits us to estimate and balance the manufacturing, inventory, and distribution costs with possible public benefits and reduce the incurred losses.
Research Limitations/Implications
In this chapter, we assume that the data on the vaccination demands are deterministic and known in advance to a decision maker. In our future research, we intend to lift this limitation and accomplish a more scrupulous analysis of links between the entropy as a data uncertainty measure and the costs in medical supply chains. Moreover, we intend
to perform a more sophisticated cost–benefit–risk analysis of the vaccination supply chains, taking into account the stochastic behavior of demands for different population groups.
Practical Implications
A case study has been implemented to test the suggested methodology; we successfully used our approach to analyze and improve the nationwide vaccination program carried out by the CLALIT Health Services (Israel). We believe that the suggested analytics methodology can be used for wider applications in other types of health care supply chains.
Originality/Value
This chapter develops a novel integrated approach for optimizing costs and public benefits within the influenza vaccine supply chain. The methodology is applicable for wider health care management applications.
12.2 The Big Dangers of Big Data ....................................................................241
12.3 Defining the Key Concepts ......................................................................241
12.4 Getting Started with Analytics .................................................................242
12.4.1 Knowledge and the Need to Share It ............................................243
12.4.2 Knowledge Sharing: Easier Said Than Done ................................ 244
12.5 Examples of Success and Failure in Knowledge Sharing .......................... 246
12.6 Conclusions ............................................................................................. 246
Advances in technology have made sharing data—even Big Data—very much easier, but does sharing data by itself achieve anything? There is a lack of research into exactly how using analytics pays off and into what needs to be taken into account in order to run an effective analytics program. This chapter examines some of the issues that have to
be addressed if analytics and Big Data are to have a positive effect on organizational performance by reviewing the published literature and taking specific examples from the domain of oil and gas exploration and production. The message overall is that technology is not enough; that knowledge needs to be shared as well as data; and that people,
processes, structure, and technology together make up a whole that is much greater than the sum of its parts. Thus, the chapter acts in part as an antidote to the hype that is sometimes written, especially by software vendors, about analytics and Big Data.
Analytics can identify useful patterns and relationships, but they must be based on solid and reliable foundations: Garbage in, garbage out is as true in the era of Big Data as it ever was. And only people can decide which relationships are truly useful to the business. Sharing data, even integrating data, will only help your business if the purpose for the sharing or integration is clear; it is not just a question of technology or algorithms.
The crucial need is for shared understanding, first of terminology, then of interpretation (the most difficult part of boundary spanning), then of action.
Big Data and analytics are undeniably useful, but their contribution has to be given meaning by people, all the way from setting a guiding vision through to understanding outputs. This then needs to be filtered through and incorporated into the skilled practice of the people and the organization. Thus, the understanding moves from terminology, to interpretation, then to action. Future technological developments in how to acquire, store, and analyze Big Data are most unlikely to affect any of these arguments. So they remain valid for the foreseeable future of analytics and Big Data.
We began with a question: How can we share data, integrate data, and use analytics on that data more effectively so that we end up sharing knowledge, not just sharing data? This could be rephrased as how to really make a difference with analytics. Our answer to both questions is simple to state, but not so simple to achieve:
Knowledge sharing, not just data sharing, has to become part of your organization’s core values and culture.
