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

As an accomplished professional with a Master Degree in Computer Science (Specialization in Software Engineering) and a PGD in Management Information Systems, over 20 years of hands-on experience in Information Technology, Data Governance, Data Management, and PDPL plus NCA Compliance. Expertise spans diverse sectors, including Aviation, Healthcare, Investment, Education, and Construction, allowing me to effectively bridge the gap between technical and business needs.

Leadership roles include Manager – Data Management (DMO), Manager Systems, Sr. Team Lead – Data Specialist, Manager Applications, and Adjunct Faculty Member among others. I am also deeply involved in applied research, focusing on Big Data Governance, Privacy, Security, and Compliance. My work has been published in prestigious international journals, offering valuable insights into solving industry challenges.

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.

With 45+ professional certifications, including Certified Data Management Professional (CDMP), GDPR, CCPA, Certified Data Protection Officer (CDPO), ITIL, EC-Council Disaster Recovery Professional, AWS Cloud Certified, Oracle Technologies including e-Business, Databases, and Oracle Cloud Infrastructure (OCI). I have continually enhanced my expertise in the ever-evolving field of data privacy and security. My competencies also include Disaster Recovery, Business Continuity, Regulatory Compliance (GDPR, NCA, CCPA, NDMO), Data Architecture, Metadata Management, and more.

Numerous initiatives in Data Governance, Data Management, Data Privacy, Data Protection, ensuring compliance with international, and local standards such as NCA, GDPR, KSA PDPL, and NDMO.