Data Ethics – The Analyst’s Code of Conduct
To continue our masterclass on data integrity for the modern Reporting Analyst, we must move beyond technical frameworks like ROCCC and address the moral compass of our profession. While Good data and Bad data focus on accuracy, Data Ethics focuses on the rights of the individual.
At a Glance
As we automate more workflows in BI Tools, the responsibility to handle information correctly grows. This guide explores the fundamental pillars of Data Ethics to ensure your career as a Data Warrior remains beyond reproach.
The Warrior’s Code: Mastering Data Ethics
In the simplest terms, Ethics are well-founded standards of right and wrong that prescribe what humans ought to do. These standards are usually defined in terms of rights, obligations, benefits to society, fairness, or specific virtues. In our field, Data Ethics refers to the moral obligations we have when collecting, analyzing, and sharing information.
To maintain the high standards we set in our pillar post on Biased vs Unbiased Data, we must adhere to the 6 Aspects of Data Ethics.
1. Ownership: Who Truly Controls the Data?
The first pillar of Data Ethics is Ownership. It is a fundamental principle that individuals own the Raw data they provide.
As a Reporting Analyst, you must remember that the subject has primary control over:
- How their data is used.
- The specific ways it is processed.
- How (and with whom) it is shared.
Ownership means the data subject is the ultimate stakeholder, not the corporation collecting the bits and bytes.
2. Transaction Transparency: The End of Black-Box Algorithms
In the age of AI and complex SQL transformations, we often encounter “black boxes.” Transaction Transparency demands that all data-processing activities and algorithms be completely explainable.
If an individual provides their data, they have the right to understand exactly how the math works. If you cannot explain your DAX measure or your Power Automate logic to the person providing the data, you are likely violating this ethical pillar.
3. Consent: Explicit Intent Before Action
Consent is the individual’s right to know explicit details about how and why their data will be used before agreeing to provide it. Ethical data collection is never “sneaky.” It requires clear communication of intent, ensuring the subject is fully aware of the scope of the project before they click “accept.”
4. Currency: The Financial Value of Personal Info
In the digital economy, data is a form of Currency. Individuals should be fully aware of any financial transactions resulting from the use of their personal data.
This includes understanding the scale of these transactions. If a company is profiting from a user’s behavior patterns, the user has a right to know the commercial value of the information they have surrendered.
5. Privacy: Protecting the Subject at Every Step
Privacy is the ongoing effort of preserving a data subject’s information and activity any time a data transaction occurs. This goes beyond simple password protection; it involves ensuring that even during high-level analysis, the identity and sensitive habits of the individual remain shielded from unauthorized exposure.
6. Openness: The Standard for Open Data
Openness refers to the availability of free access, usage, and sharing of data. When data meets the highest levels of quality and ethical standards, it is referred to as Open Data.
However, a true Data Warrior knows that Openness must never come at the expense of the other five pillars. You cannot prioritize “sharing” if it violates a user’s Privacy or Consent.
Summary of the 6 Pillars of Data Ethics
| Pillar | Core Ethical Obligation |
| Ownership | Individuals own the raw data and control its usage. |
| Transparency | Algorithms and processes must be explainable. |
| Consent | Explicit details must be provided before data collection. |
| Currency | Users must know the financial value of their data. |
| Privacy | Information must be preserved during every transaction. |
| Openness | Free access is allowed only if it respects all ethical standards. |
Key Takeaways
Data Ethics defines the moral standards for handling information, focusing on fairness, obligations, and societal benefit. The 6 Aspects of Data Ethics include Ownership (user control of raw data), Transaction Transparency (explainable algorithms), Consent (informed agreement), Currency (awareness of financial data value), Privacy (protection during transactions), and Openness (accessibility of high-standard Open Data). For a Reporting Analyst, adhering to these pillars is essential for maintaining data integrity and ensuring that Biased and Unbiased Data practices are supported by a strong moral framework.
Next Steps for the Data Warrior: Ensure your automation scripts respect these boundaries! Check out our guide on Power Automate Desktop on the GeekHub page to see how to build ethical, transparent bots.
Related Reads
- The Open Data Debate: Balancing Public Access with Data Ethics and Privacy
- The Essential Guide to Data Anonymization: Protecting Privacy in Analytics
- 6 Pillars of Data Ethics: The Analyst’s Code of Honour
- 5 Powerful ways to determine Good data and Bad data (Make it ‘ROCCC’ Solid)
- The Essential Guide to Identifying 4 Types of Biases in Data