Data Handling Ethics – Unethical Data Practices – Scenarios

Posted on December 20, 2021 by Muhammad Rawish Siddiqui

Data Governance Ethics – Examples of Unethical Data Handling

Scenario	Description
Timing	It is possible to lie through omission or inclusion of certain data points in a report or activity based on timing.
Misleading Visualizations	Bias refers to an inclination of outlook. On the personal level, the term is associated with unreasoned judgments or prejudices. In statistics, bias refers to deviations from expected values. These are often introduced through systematic errors in sampling or data selection. Bias can be introduced at different points in the data lifecycle: when data is collected or created, when it is selected for inclusion in analysis, through the methods by which it is analyzed, and in how the results of analysis are presented. There are several types of bias, such as Data Collection for pre-defined results, Biased use of data collected, Hunch and search, Biased sampling methodology, and Context and Culture.
Unclear Definitions or Invalid Comparisons	The ethical thing to do, in presenting information, is to provide context that informs its meaning, such as a clear, unambiguous definition of the population being measured and what it means to be “on welfare.” When the required context is left out, the surface of the presentation may imply that the data is not supported. Whether this effect is gained through the intent to deceive or through simply clumsiness, it is an unethical use of data. It is also simply necessary, from an ethical perspective, not to misuse statistics.
Bias	Data integration presents ethical challenges because data is changed as it moves from system to system. If data is not integrated with care, it presents risk for unethical or even illegal data handling. These ethical risks intersect with fundamental problems in data management, including Limited knowledge of data’s origin and lineage, Data of poor quality, Unreliable Metadata, and no documentation of data remediation history.
Transforming and Integrating Data	Bias refers to an inclination of outlook. On the personal level, the term is associated with unreasoned judgments or prejudices. In statistics, bias refers to deviations from expected values. These are often introduced through systematic errors in sampling or data selection. Bias can be introduced at different points in the data lifecycle: when data is collected or created, when it is selected for inclusion in analysis, through the methods by which it is analyzed, and in how the results of analysis are presented. There are several types of bias, such as Data Collection for pre-defined result, Biased use of data collected, Hunch and search, Biased sampling methodology, and Context and Culture.
Obfuscation / Redaction of Data	Obfuscating or redacting data is the practice of making information anonymous or removing sensitive information. But obfuscation alone may not be sufficient to protect data if a downstream activity (analysis or combination with other datasets) can expose the data. This risk is present in the following instances: Data aggregation, Data marking, and Data masking.

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Muhammad Rawish Siddiqui

Master’s degree in computer science with Specialization in Software Engineering and Earned PGD in Management Information System, in addition to 45+ Online Professional Certifications.

Accumulated 20+ years’ Information Technology, Data Governance, Data Management, and Compliance related consecutive hands-on experience, possess diversified business and technical background across the Aviation, Healthcare, Investment, Education, and Construction sectors.

Engaged in Applied Research with a focus on Big Data Governance, Privacy, Security, and Compliance, resulting in the publication of numerous articles and research manuscripts in prestigious international journals, including the Journal of Sensor Networks and Data Communications, the Journal of Electrical Electronics Engineering, and the Innovative Journal of Applied Science. These works contribute practical insights and solutions to address industry challenges effectively.

Experience includes leading data governance and management initiatives, ensuring compliance with NDMO, GDPR and KSA PDPL, and implementing/improving data security, privacy, & protection.

Served in leadership capacities such as Manager – Data Management, Adjunct Faculty Member, Sr. Team Lead – Database Specialist, Sr. Team Lead Consultant, Sr. Applications DBA, Sr. Resident Consultant, and Manager Systems.

Possesses extensive expertise spanning a diverse range of platforms, tools, technologies, hardware, software, and operating systems, effectively leveraging this knowledge to address complex challenges in Data governance, security, privacy and compliance.

45+ Online Professional Certifications, which includes:
• CDMP – Certified Data Management Professional
• CDPO – Certified Data Protection Officer
• GDPR – General Data Protection Regulation Practitioner
• EDRP – EC-Council Disaster Recovery Professional
• AWS Certified Cloud Practitioner
• Oracle Enterprise Data Management Professional
• Oracle Database Cloud Certified Professional
• ITIL – Information Technology Infrastructure Library (ITSM – IT)

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