Skip main navigation

Data ethics

Learn about data ethics

Ethical consideration is a key area of data analytics. As we move from human-intuited decision-making to data-driven, machine-oriented decision-making, the role of ethics expands and evolves rather than disappears.

We need to consider ethics in new and emerging contexts where consequences can be damaging. Ethics are the responsibility of practitioners, and the ethical nature of analysis should be a primary consideration on all projects.

Consequences of data misuse

Despite tough regulations, data breaches are common, and organisational ethics are scrutinised as a result. In response to the constant misuse of data, many countries such as Australia have introduced harsh fiscal penalties for improper data practice. In any organisation, people decide what data requires to be included and what not. In such cases, to avoid consequences, centralising the rules and regulations around ethical practices can be a good start.

With such wide availability of data in every form, for free in many cases, can cause an unconscious breach of data laws as well. So, as an organisation, one must have a moral and ethical position that lies as a sweet spot among what an organisation can technically do, legally do and logically do.

Graphic shows a Venn diagram of three circles: what can be done legally, what an organisation would like to do, what can be done technically. The overlapping section of the Venn diagram is labelled Ethical position.

Source: IBM [1]

The role of the data analyst

In a 2016 report titled Building Digital Trust [2], Accenture recommends a universal code of data ethics. The 12 principles in this code drive data ethics holistically at an organisational level, and integrate the imperative of data ethics and its considerations into the data analyst role.

The 12 principles are as follows:

  1. The highest priority is to respect the persons behind the data.
  2. Account for the downstream uses of datasets.
  3. The consequences of utilizing data and analytical tools today are shaped by how they’ve been used in the past.
  4. Seek to match privacy and security safeguards with privacy and security expectations.
  5. Always follow the law, but understand that the law is often a minimum bar.
  6. Be wary of collecting data just for the sake of having more data.
  7. Data can be a tool of both inclusion and exclusion.
  8. As far as possible, explain methods for analysis and marketing to data disclosures.
  9. Data scientists and practitioners should accurately represent their qualifications (and limits to their expertise), adhere to professional standards, and strive for peer accountability.
  10. Aspire to design practices that incorporate transparency, configurability, accountability, and auditability.
  11. Products and research practices should be subject to internal (and potentially external) ethical review.
  12. Governance practices should be robust, known to all team members and regularly reviewed.

Data analysts, and many other professions, use de-identification to safeguard individual or organisational privacy when working with data. De-identifying data involves removing the personal information of the subject and replacing it with unidentifiable information. The process of de-identification ensures that an individual cannot be reasonably identified from the data presented, or when the data is considered with other information.

Personal information includes:

  • name
  • date of birth
  • address (including physical and IP address)
  • photographs
  • Medicare number, TFN or other government-issued identifiers
  • health information
  • employment records
  • financial records, including credit history

What do you think?

Do you think there is any other kind of personal information that we might have missed out on in this list?

Share your answers with your fellow learners in the comments.


  1. Ethics of big data. [PDF]. IBM; 2014. Available from:
  2. Building digital trust: The role of data ethics in the digital age [PDF]. Accenture; 2016. Available from:
This article is from the free online

Data Visualisation with Python: Bokeh and Advanced Layouts

Created by
FutureLearn - Learning For Life

Reach your personal and professional goals

Unlock access to hundreds of expert online courses and degrees from top universities and educators to gain accredited qualifications and professional CV-building certificates.

Join over 18 million learners to launch, switch or build upon your career, all at your own pace, across a wide range of topic areas.

Start Learning now