| 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 | Charts and graphs can be used to present data in a misleading manner. For instance, changing scale can make a trend line look better or worse. |
| 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 required context is left out, the surface of the presentation may imply meaning that the data does not support. 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 | 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 per-defined result, Biased use of data collected, Hunch and search, Biased sampling methodology, and Context and Culture. |
| Transforming and Integrating Data | 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. |
| 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. |
Data Handling Ethics – Unethical Data Practices – Scenarios
Master’s degree in computer science with Specialization in Software Engineering and Earned PGD in Management Information System. 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.
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)
Experience includes leading data governance and management initiatives, ensuring compliance with NDMO, GDPR and KSA PDPL, and implement/improve data security, privacy, & protection. Expertise spans a wide variety of platforms, tools, technologies, hardware, software, and operating systems.
Served in leadership capacities such as Manager – Data Management, Sr. Team Lead – Database Specialist, Sr. Team Lead Consultant, Sr. Applications DBA, Sr. Resident Consultant, and Manager Systems.