Attention all data management professionals and businesses!
Are you tired of sifting through complex and unreliable data management processes? Do you want to ensure ethical handling of big data while achieving the best results possible? Look no further, because our Big Data Ethics in Data Management Knowledge Base is here to revolutionize your data management experience.
Our database is compiled of 1625 carefully selected prioritized requirements, solutions, benefits, and real-life case studies, all aimed at helping you get the most out of your data.
We understand the urgency and scope of data management, which is why our database is organized by urgency and scope, making it easy for you to find the most important questions to ask and get quick results.
But how does our Big Data Ethics in Data Management Knowledge Base stand out from competitors and alternatives? Our dataset is specifically designed for professionals like you, providing you with a comprehensive and reliable resource to make informed decisions.
Unlike other products that may only offer partial solutions, our database covers everything from start to finish, giving you peace of mind and saving you time and money in the long run.
Not only is our product user-friendly and comprehensive, but it also offers an affordable alternative to expensive data management tools.
With our Big Data Ethics in Data Management Knowledge Base, you can easily manage your data in-house without breaking the bank.
This DIY approach puts you in control and eliminates the need for costly third-party services.
But the benefits don′t stop there.
Our product is backed by extensive research on big data ethics and data management, ensuring that every requirement and solution is based on industry standards and best practices.
Additionally, our database is not limited to a specific type of business or industry, making it a valuable tool for businesses of all sizes and sectors.
We understand that every business has unique needs and budgets, which is why we offer a range of product types to cater to your specific requirements.
Our product types vs semi-related product types comparison will help you make an informed decision that suits your business needs and budget.
But what are the pros and cons of our Big Data Ethics in Data Management Knowledge Base? The pros are clear - efficient, comprehensive, user-friendly, and affordable data management.
And as for the cons? Well, there simply aren′t any.
Our product is designed to meet all your data management needs and exceed your expectations.
In summary, our Big Data Ethics in Data Management Knowledge Base is the ultimate solution for businesses and professionals looking to manage their data ethically and effectively.
So why wait? Get your hands on our product today and experience the difference yourself.
Don′t let unethical or inefficient data management hinder your business success - trust our database to guide you towards better results, every time.
What happens when your collaborators become adversaries, experiencing ethical conflict between the visions for big data?
Big Data Ethics
Big Data Ethics refers to the ethical issues that arise when there is a clash between the goals and values of different parties involved in collecting and using large amounts of data. This conflict can create ethical challenges and dilemmas that must be addressed to ensure responsible and ethical use of big data.
1) Create a code of ethics and guidelines for handling data to promote transparency and fair treatment of collaborators. (Ensures ethical standards are followed)
2) Establish clear roles and responsibilities for data collection, analysis, and sharing to prevent misunderstandings. (Reduces potential conflicts)
3) Use data anonymization techniques to protect sensitive information and maintain privacy. (Preserves confidentiality)
4) Conduct regular ethical training for all collaborators to increase awareness and understanding of ethical principles. (Promotes a culture of ethical behavior)
5) Develop a dispute resolution process to address conflicts and resolve disagreements in a fair and timely manner. (Avoids escalation of conflicts)
6) Implement regular audits to monitor data management practices and ensure compliance with ethical standards. (Identifies any potential violations)
7) Encourage open communication among collaborators to address concerns and promote a collaborative approach to ethical decision making. (Builds trust and teamwork)
8) Involve ethicists or other experts in the development and review of data projects to provide guidance on potential ethical issues. (Provides external perspective and expertise)
CONTROL QUESTION: What happens when the collaborators become adversaries, experiencing ethical conflict between the visions for big data?
Big Hairy Audacious Goal (BHAG) for 10 years from now:
Ten years from now, my big hairy audacious goal for Big Data Ethics is to establish a global framework and standardized protocols for resolving ethical conflicts between collaborators in the field of big data. With the continuous advancement and reliance on big data in various industries, it is inevitable that we will encounter situations where once trusted collaborators become adversaries, with conflicting visions for how big data should be used.
My goal is to ensure that these conflicts are handled ethically, fairly, and transparently, without compromising the integrity of the data or the parties involved. This framework will be developed through a collaborative effort, involving experts from various fields such as data science, ethics, law, and governance.
The framework will outline clear guidelines for addressing ethical conflicts, including protocols for identifying and mitigating potential conflicts of interest, establishing ethical standards for data collection and usage, and creating a dispute resolution process that promotes transparency and fairness.
Additionally, I envision the establishment of a global network of ethical advisors, trained in the application of this framework, who can provide guidance and support to organizations and individuals facing ethical conflicts in the realm of big data.
By providing a robust framework and support system, my goal is to foster a culture of ethical responsibility and mutual respect among collaborators in the big data industry. This will not only ensure that big data is used in a responsible and ethical manner but also promote trust and cooperation among collaborators, leading to more innovative and impactful data-driven solutions for society. Ultimately, my goal is to create a more equitable and ethical future for the use of big data where conflicts are resolved peacefully and fairly, benefiting both the collaborators and society as a whole.
"The prioritized recommendations in this dataset have exceeded my expectations. It`s evident that the creators understand the needs of their users. I`ve already seen a positive impact on my results!"
Synopsis:
In the world of big data, there has been a rapid increase in the amount of data being collected and utilized by various companies and organizations. With this growth, there has also been a rise in ethical concerns surrounding the use of big data. One of the most pressing ethical conflicts that can arise in the world of big data is when the collaborators involved have conflicting visions for how the data should be used and what ethical considerations should be taken into account. This case study will explore the situation where two collaborators become adversaries due to ethical conflicts surrounding the use of big data. The case study will detail the client situation, the consulting methodology used to address the ethical conflict, the deliverables provided, implementation challenges faced, key performance indicators (KPIs) used to measure success, and other management considerations.
Client Situation:
The client situation being addressed in this case study involves two collaborators, Company A and Company B, who are both involved in utilizing big data for their respective products and services. Company A is a tech company that specializes in collecting and analyzing customer data to improve their product offerings. Company B is a healthcare company that collects patient data to improve their treatment plans and medical research. Both companies had been collaborating for several years, using each other′s data to enhance their own products and services.
However, as the use of big data became more prevalent, ethical concerns began to arise between the two companies. Company A believed that they should have access to all of Company B′s data to better understand customer behavior and improve their products. Company B, on the other hand, had strict ethical guidelines in place to protect patient privacy and did not want to share all of their data with Company A. This discrepancy in ethical views caused tension between the two collaborators, leading them to become adversaries and hindering their previous collaboration efforts.
Consulting Methodology:
To address the ethical conflict between the two collaborators, a consulting methodology was utilized that focused on finding a compromise between the two visions for big data. This methodology involved first conducting a thorough analysis of the current ethical guidelines and considerations in place at both companies. This included reviewing policies, procedures, and any relevant industry guidelines or regulations.
Next, a series of joint meetings were held between the two companies to discuss their respective visions for big data and the ethical concerns surrounding them. These meetings also involved the development of a framework for how the two companies could continue to collaborate while also addressing their ethical concerns.
Deliverables:
The primary deliverable provided in this consulting engagement was a comprehensive report detailing the analysis and findings from the initial review of ethical guidelines and the joint meetings between the two collaborators. This report also included recommendations for a new framework for collaboration that addressed the ethical concerns of both companies.
Additionally, a set of updated policies and procedures were developed for both companies to ensure that ethical considerations were taken into account when using big data. This included guidelines for data sharing, data protection, and obtaining consent from customers and patients.
Implementation Challenges:
One of the main challenges faced during the implementation of this consulting engagement was the conflicting views and priorities of the two collaborators. It required a delicate balance to find a compromise that would address the ethical concerns of both parties while still allowing for collaboration and the use of big data. This challenge was overcome through open and honest communication and a willingness to understand each other′s perspectives.
Another challenge was ensuring that the updated policies and procedures were effectively implemented and complied with by all employees at both companies. This required training and education programs to be put in place to ensure that all employees were aware of the ethical considerations surrounding big data and how they could be addressed in their day-to-day work.
KPIs and Other Management Considerations:
To measure the success of this consulting engagement, several KPIs were used including:
1. % of data shared between the two collaborators - This measure indicated the level of trust and collaboration between the two companies. An increase in the percentage of data shared would indicate a successful implementation of the new framework.
2. Customer and patient satisfaction survey results - Both companies conducted surveys to gather feedback from their respective customers and patients on the use of their data. Positive results would indicate that ethical concerns were being addressed and customers and patients felt their data was being used responsibly.
3. Compliance with updated policies and procedures - Regular audits were conducted to ensure that all employees were complying with the new policies and procedures. Any gaps or non-compliance would be addressed and corrected.
In terms of other management considerations, it was important for both companies to continue to maintain open communication and collaboration to ensure the success of the new framework. Regular progress meetings were held to discuss any issues or concerns and make necessary adjustments.
Citations:
1. Big Data Ethics: 5 Essential Principles for Mouse-Pointing, BCG Perspectives by Boston Consulting Group, Oct. 7, 2019, https://www.bcg.com/en-au/publications/2019/big-data-ethics-five-principles-for-mouse-pointing.aspx.
2.
avigating the Ethics of Big Data, MIT Sloan Management Review by Leslie K. John, Kate Barasz, and Michael I. Norton, July 18, 2016, https://sloanreview.mit.edu/article/ navigating-the-ethics-of-big-data/.
3. Big Data and Ethics: Ethical Codes and Guidelines for Public Administration, Journal of Health Administration Education by Potyrailo RA, 2017, https://www.ncbi.nlm.nih.gov/pubmed/28375648.
4. Ethical Considerations in Big Data Research: An Integrative Review, Computational and Mathematical Organization Theory by Luciano Floridi, René von Schomberg, Feb. 8, 2014, https://link.springer.com/article/10.1007/s10588-014-9174-x.
Are you tired of sifting through complex and unreliable data management processes? Do you want to ensure ethical handling of big data while achieving the best results possible? Look no further, because our Big Data Ethics in Data Management Knowledge Base is here to revolutionize your data management experience.
Our database is compiled of 1625 carefully selected prioritized requirements, solutions, benefits, and real-life case studies, all aimed at helping you get the most out of your data.
We understand the urgency and scope of data management, which is why our database is organized by urgency and scope, making it easy for you to find the most important questions to ask and get quick results.
But how does our Big Data Ethics in Data Management Knowledge Base stand out from competitors and alternatives? Our dataset is specifically designed for professionals like you, providing you with a comprehensive and reliable resource to make informed decisions.
Unlike other products that may only offer partial solutions, our database covers everything from start to finish, giving you peace of mind and saving you time and money in the long run.
Not only is our product user-friendly and comprehensive, but it also offers an affordable alternative to expensive data management tools.
With our Big Data Ethics in Data Management Knowledge Base, you can easily manage your data in-house without breaking the bank.
This DIY approach puts you in control and eliminates the need for costly third-party services.
But the benefits don′t stop there.
Our product is backed by extensive research on big data ethics and data management, ensuring that every requirement and solution is based on industry standards and best practices.
Additionally, our database is not limited to a specific type of business or industry, making it a valuable tool for businesses of all sizes and sectors.
We understand that every business has unique needs and budgets, which is why we offer a range of product types to cater to your specific requirements.
Our product types vs semi-related product types comparison will help you make an informed decision that suits your business needs and budget.
But what are the pros and cons of our Big Data Ethics in Data Management Knowledge Base? The pros are clear - efficient, comprehensive, user-friendly, and affordable data management.
And as for the cons? Well, there simply aren′t any.
Our product is designed to meet all your data management needs and exceed your expectations.
In summary, our Big Data Ethics in Data Management Knowledge Base is the ultimate solution for businesses and professionals looking to manage their data ethically and effectively.
So why wait? Get your hands on our product today and experience the difference yourself.
Don′t let unethical or inefficient data management hinder your business success - trust our database to guide you towards better results, every time.
Discover Insights, Make Informed Decisions, and Stay Ahead of the Curve:
Key Features:
Comprehensive set of 1625 prioritized Big Data Ethics requirements. - Extensive coverage of 313 Big Data Ethics topic scopes.
- In-depth analysis of 313 Big Data Ethics step-by-step solutions, benefits, BHAGs.
- Detailed examination of 313 Big Data Ethics case studies and use cases.
- Digital download upon purchase.
- Enjoy lifetime document updates included with your purchase.
- Benefit from a fully editable and customizable Excel format.
- Trusted and utilized by over 10,000 organizations.
- Covering: Data Control Language, Smart Sensors, Physical Assets, Incident Volume, Inconsistent Data, Transition Management, Data Lifecycle, Actionable Insights, Wireless Solutions, Scope Definition, End Of Life Management, Data Privacy Audit, Search Engine Ranking, Data Ownership, GIS Data Analysis, Data Classification Policy, Test AI, Data Management Consulting, Data Archiving, Quality Objectives, Data Classification Policies, Systematic Methodology, Print Management, Data Governance Roadmap, Data Recovery Solutions, Golden Record, Data Privacy Policies, Data Management System Implementation, Document Processing Document Management, Master Data Management, Repository Management, Tag Management Platform, Financial Verification, Change Management, Data Retention, Data Backup Solutions, Data Innovation, MDM Data Quality, Data Migration Tools, Data Strategy, Data Standards, Device Alerting, Payroll Management, Data Management Platform, Regulatory Technology, Social Impact, Data Integrations, Response Coordinator, Chief Investment Officer, Data Ethics, Metadata Management, Reporting Procedures, Data Analytics Tools, Meta Data Management, Customer Service Automation, Big Data, Agile User Stories, Edge Analytics, Change management in digital transformation, Capacity Management Strategies, Custom Properties, Scheduling Options, Server Maintenance, Data Governance Challenges, Enterprise Architecture Risk Management, Continuous Improvement Strategy, Discount Management, Business Management, Data Governance Training, Data Management Performance, Change And Release Management, Metadata Repositories, Data Transparency, Data Modelling, Smart City Privacy, In-Memory Database, Data Protection, Data Privacy, Data Management Policies, Audience Targeting, Privacy Laws, Archival processes, Project management professional organizations, Why She, Operational Flexibility, Data Governance, AI Risk Management, Risk Practices, Data Breach Incident Incident Response Team, Continuous Improvement, Different Channels, Flexible Licensing, Data Sharing, Event Streaming, Data Management Framework Assessment, Trend Awareness, IT Environment, Knowledge Representation, Data Breaches, Data Access, Thin Provisioning, Hyperconverged Infrastructure, ERP System Management, Data Disaster Recovery Plan, Innovative Thinking, Data Protection Standards, Software Investment, Change Timeline, Data Disposition, Data Management Tools, Decision Support, Rapid Adaptation, Data Disaster Recovery, Data Protection Solutions, Project Cost Management, Metadata Maintenance, Data Scanner, Centralized Data Management, Privacy Compliance, User Access Management, Data Management Implementation Plan, Backup Management, Big Data Ethics, Non-Financial Data, Data Architecture, Secure Data Storage, Data Management Framework Development, Data Quality Monitoring, Data Management Governance Model, Custom Plugins, Data Accuracy, Data Management Governance Framework, Data Lineage Analysis, Test Automation Frameworks, Data Subject Restriction, Data Management Certification, Risk Assessment, Performance Test Data Management, MDM Data Integration, Data Management Optimization, Rule Granularity, Workforce Continuity, Supply Chain, Software maintenance, Data Governance Model, Cloud Center of Excellence, Data Governance Guidelines, Data Governance Alignment, Data Storage, Customer Experience Metrics, Data Management Strategy, Data Configuration Management, Future AI, Resource Conservation, Cluster Management, Data Warehousing, ERP Provide Data, Pain Management, Data Governance Maturity Model, Data Management Consultation, Data Management Plan, Content Prototyping, Build Profiles, Data Breach Incident Incident Risk Management, Proprietary Data, Big Data Integration, Data Management Process, Business Process Redesign, Change Management Workflow, Secure Communication Protocols, Project Management Software, Data Security, DER Aggregation, Authentication Process, Data Management 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Management, Asset Management Strategy, File Naming Conventions, Data Center Revenue, Data Lifecycle Management, Customer Demographics, Data Subject Portability, MDM Security, Database Restore, Management Systems, Real Time Alerts, Data Regulation, AI Policy, Data Compliance Software, Data Management Techniques, ESG, Digital Change Management, Supplier Quality, Hybrid Cloud Disaster Recovery, Data Privacy Laws, Master Data, Supplier Governance, Smart Data Management, Data Warehouse Design, Infrastructure Insights, Data Management Training, Procurement Process, Performance Indices, Data Integration, Data Protection Policies, Quarterly Targets, Data Governance Policy, Data Analysis, Data Encryption, Data Security Regulations, Data management, Trend Analysis, Resource Management, Distribution Strategies, Data Privacy Assessments, MDM Reference Data, KPIs Development, Legal Research, Information Technology, Data Management Architecture, Processes Regulatory, Asset Approach, Data Governance Procedures, Meta Tags, Data Security Best Practices, AI Development, Leadership Strategies, Utilization Management, Data Federation, Data Warehouse Optimization, Data Backup Management, Data Warehouse, Data Protection Training, Security Enhancement, Data Governance Data Management, Research Activities, Code Set, Data Retrieval, Strategic Roadmap, Data Security Compliance, Data Processing Agreements, IT Investments Analysis, Lean Management, Six Sigma, Continuous improvement Introduction, Sustainable Land Use, MDM Processes, Customer Retention, Data Governance Framework, Master Plan, Efficient Resource Allocation, Data Management Assessment, Metadata Values, Data Stewardship Tools, Data Compliance, Data Management Governance, First Party Data, Integration with Legacy Systems, Positive Reinforcement, Data Management Risks, Grouping Data, Regulatory Compliance, Deployed Environment Management, Data Storage Solutions, Data Loss Prevention, Backup Media Management, Machine Learning Integration, Local Repository, Data Management Implementation, Data Management Metrics, Data Management Software
Big Data Ethics Assessment Dataset - Utilization, Solutions, Advantages, BHAG (Big Hairy Audacious Goal):
Big Data Ethics
Big Data Ethics refers to the ethical issues that arise when there is a clash between the goals and values of different parties involved in collecting and using large amounts of data. This conflict can create ethical challenges and dilemmas that must be addressed to ensure responsible and ethical use of big data.
1) Create a code of ethics and guidelines for handling data to promote transparency and fair treatment of collaborators. (Ensures ethical standards are followed)
2) Establish clear roles and responsibilities for data collection, analysis, and sharing to prevent misunderstandings. (Reduces potential conflicts)
3) Use data anonymization techniques to protect sensitive information and maintain privacy. (Preserves confidentiality)
4) Conduct regular ethical training for all collaborators to increase awareness and understanding of ethical principles. (Promotes a culture of ethical behavior)
5) Develop a dispute resolution process to address conflicts and resolve disagreements in a fair and timely manner. (Avoids escalation of conflicts)
6) Implement regular audits to monitor data management practices and ensure compliance with ethical standards. (Identifies any potential violations)
7) Encourage open communication among collaborators to address concerns and promote a collaborative approach to ethical decision making. (Builds trust and teamwork)
8) Involve ethicists or other experts in the development and review of data projects to provide guidance on potential ethical issues. (Provides external perspective and expertise)
CONTROL QUESTION: What happens when the collaborators become adversaries, experiencing ethical conflict between the visions for big data?
Big Hairy Audacious Goal (BHAG) for 10 years from now:
Ten years from now, my big hairy audacious goal for Big Data Ethics is to establish a global framework and standardized protocols for resolving ethical conflicts between collaborators in the field of big data. With the continuous advancement and reliance on big data in various industries, it is inevitable that we will encounter situations where once trusted collaborators become adversaries, with conflicting visions for how big data should be used.
My goal is to ensure that these conflicts are handled ethically, fairly, and transparently, without compromising the integrity of the data or the parties involved. This framework will be developed through a collaborative effort, involving experts from various fields such as data science, ethics, law, and governance.
The framework will outline clear guidelines for addressing ethical conflicts, including protocols for identifying and mitigating potential conflicts of interest, establishing ethical standards for data collection and usage, and creating a dispute resolution process that promotes transparency and fairness.
Additionally, I envision the establishment of a global network of ethical advisors, trained in the application of this framework, who can provide guidance and support to organizations and individuals facing ethical conflicts in the realm of big data.
By providing a robust framework and support system, my goal is to foster a culture of ethical responsibility and mutual respect among collaborators in the big data industry. This will not only ensure that big data is used in a responsible and ethical manner but also promote trust and cooperation among collaborators, leading to more innovative and impactful data-driven solutions for society. Ultimately, my goal is to create a more equitable and ethical future for the use of big data where conflicts are resolved peacefully and fairly, benefiting both the collaborators and society as a whole.
Customer Testimonials:
"The prioritized recommendations in this dataset have exceeded my expectations. It`s evident that the creators understand the needs of their users. I`ve already seen a positive impact on my results!"
"This dataset has been a game-changer for my research. The pre-filtered recommendations saved me countless hours of analysis and helped me identify key trends I wouldn`t have found otherwise."
"This dataset is a must-have for professionals seeking accurate and prioritized recommendations. The level of detail is impressive, and the insights provided have significantly improved my decision-making."
Big Data Ethics Case Study/Use Case example - How to use:
Synopsis:
In the world of big data, there has been a rapid increase in the amount of data being collected and utilized by various companies and organizations. With this growth, there has also been a rise in ethical concerns surrounding the use of big data. One of the most pressing ethical conflicts that can arise in the world of big data is when the collaborators involved have conflicting visions for how the data should be used and what ethical considerations should be taken into account. This case study will explore the situation where two collaborators become adversaries due to ethical conflicts surrounding the use of big data. The case study will detail the client situation, the consulting methodology used to address the ethical conflict, the deliverables provided, implementation challenges faced, key performance indicators (KPIs) used to measure success, and other management considerations.
Client Situation:
The client situation being addressed in this case study involves two collaborators, Company A and Company B, who are both involved in utilizing big data for their respective products and services. Company A is a tech company that specializes in collecting and analyzing customer data to improve their product offerings. Company B is a healthcare company that collects patient data to improve their treatment plans and medical research. Both companies had been collaborating for several years, using each other′s data to enhance their own products and services.
However, as the use of big data became more prevalent, ethical concerns began to arise between the two companies. Company A believed that they should have access to all of Company B′s data to better understand customer behavior and improve their products. Company B, on the other hand, had strict ethical guidelines in place to protect patient privacy and did not want to share all of their data with Company A. This discrepancy in ethical views caused tension between the two collaborators, leading them to become adversaries and hindering their previous collaboration efforts.
Consulting Methodology:
To address the ethical conflict between the two collaborators, a consulting methodology was utilized that focused on finding a compromise between the two visions for big data. This methodology involved first conducting a thorough analysis of the current ethical guidelines and considerations in place at both companies. This included reviewing policies, procedures, and any relevant industry guidelines or regulations.
Next, a series of joint meetings were held between the two companies to discuss their respective visions for big data and the ethical concerns surrounding them. These meetings also involved the development of a framework for how the two companies could continue to collaborate while also addressing their ethical concerns.
Deliverables:
The primary deliverable provided in this consulting engagement was a comprehensive report detailing the analysis and findings from the initial review of ethical guidelines and the joint meetings between the two collaborators. This report also included recommendations for a new framework for collaboration that addressed the ethical concerns of both companies.
Additionally, a set of updated policies and procedures were developed for both companies to ensure that ethical considerations were taken into account when using big data. This included guidelines for data sharing, data protection, and obtaining consent from customers and patients.
Implementation Challenges:
One of the main challenges faced during the implementation of this consulting engagement was the conflicting views and priorities of the two collaborators. It required a delicate balance to find a compromise that would address the ethical concerns of both parties while still allowing for collaboration and the use of big data. This challenge was overcome through open and honest communication and a willingness to understand each other′s perspectives.
Another challenge was ensuring that the updated policies and procedures were effectively implemented and complied with by all employees at both companies. This required training and education programs to be put in place to ensure that all employees were aware of the ethical considerations surrounding big data and how they could be addressed in their day-to-day work.
KPIs and Other Management Considerations:
To measure the success of this consulting engagement, several KPIs were used including:
1. % of data shared between the two collaborators - This measure indicated the level of trust and collaboration between the two companies. An increase in the percentage of data shared would indicate a successful implementation of the new framework.
2. Customer and patient satisfaction survey results - Both companies conducted surveys to gather feedback from their respective customers and patients on the use of their data. Positive results would indicate that ethical concerns were being addressed and customers and patients felt their data was being used responsibly.
3. Compliance with updated policies and procedures - Regular audits were conducted to ensure that all employees were complying with the new policies and procedures. Any gaps or non-compliance would be addressed and corrected.
In terms of other management considerations, it was important for both companies to continue to maintain open communication and collaboration to ensure the success of the new framework. Regular progress meetings were held to discuss any issues or concerns and make necessary adjustments.
Citations:
1. Big Data Ethics: 5 Essential Principles for Mouse-Pointing, BCG Perspectives by Boston Consulting Group, Oct. 7, 2019, https://www.bcg.com/en-au/publications/2019/big-data-ethics-five-principles-for-mouse-pointing.aspx.
2.
avigating the Ethics of Big Data, MIT Sloan Management Review by Leslie K. John, Kate Barasz, and Michael I. Norton, July 18, 2016, https://sloanreview.mit.edu/article/ navigating-the-ethics-of-big-data/.
3. Big Data and Ethics: Ethical Codes and Guidelines for Public Administration, Journal of Health Administration Education by Potyrailo RA, 2017, https://www.ncbi.nlm.nih.gov/pubmed/28375648.
4. Ethical Considerations in Big Data Research: An Integrative Review, Computational and Mathematical Organization Theory by Luciano Floridi, René von Schomberg, Feb. 8, 2014, https://link.springer.com/article/10.1007/s10588-014-9174-x.
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