With data management and analysis becoming such an integral part of a business’ success, it’s easy to solely focus on the exciting aspects of the vast data that can be attained. Despite the potential of data analytics as a field, it’s important that data scientists and analysts understand the ethical implications of using that information. Ethical use of the data is just as important as the data itself, and it’s integral for businesses to understand what data ethics is, and how it should be adhered to.
What is data ethics?
Data ethics is the complex moral and ethical issues related to data management, recording and sharing, along with data algorithms and related practices. And while businesses and data scientists are understandably interested in the potential of big data management, handling such large amounts of data brings into question the ethical pitfalls and the behaviour of its users.
Why data ethics matters
With highly publicised examples of data misuse, public trust in data management has been shaken. While there remains some confusion around regulations and legislation regarding privacy, businesses should have in place their own code of ethical data management, helping to keep their clients’ trust by their own means.
The five key principles that data analysts need to consider:
The primary ethical and moral question around the sharing of data is whether your practices respect a person or group’s privacy.
When managing data, the primary concern for any analyst should focus on how the data is being utilised in a business because the misuse of data can impact entire communities or demographics.
In your business, you need to balance goals alongside privacy and develop a specific plan for each time you on share data. You need to know why that data is collected in the first place, how it’s being managed and is it ethically responsible in that case to disrupt a person or group’s privacy?
Another way of respecting user/customer data is through transparent practices. You need to be completely transparent regarding:
- What data is being shared
- How it’s being shared and collected
- What the data will be used for
- How they can opt out of a data sharing process – this must be easy and available whenever the other party would like to.
Transparency is the ethical behaviour that keeps your customers onside, as you explain the process and make it fully known how it will work. You’ll often see this played out on websites with pop-up notifications that explain how data, or ‘cookies’, is being used.
- Oversight and review
Artificial Intelligence (AI) and machine learning are constantly evolving, so there should be a constant evaluation of your practices. By creating a diverse ethical board or panel that meets once a month, you’re able to assess and adapt ethical processes to fit the current applications of evolving data management programs. You’re able to constantly shape future policies by assessing past issues and make sure that your data management is always of the highest ethical standard.
It’s important for all data scientists, data analysts and businesses to understand the regulations around data management, to ensure you’re adhering to legal (and ethical) guidelines. The European Union’s General Data Protection Regulation (GDPR) was enforced in May of 2018 and does have effects on some Australian businesses, while Australia’s Privacy Act of 1988 should also be considered.
After taking policies and regulations into account, it’s important that such legislation is balanced out with fairness (and transparency).
It’s vital to follow laws, but you should also understand that they’re quite often a minimum standard. Laws have in part failed to keep up with the modern and adaptive nature of many technical innovations, so it’s important for you to develop your own updated ethical frameworks.
Discover more about data ethics for your business
Ethics in data is essential and it should be maintained in all aspects of a business. If you want to be more knowledgeable about data and ethics, there are many online courses that allow an analyst to keep working in their role, while upskilling and learning more data skills to diversify your expertise. A Master of Analytics can help you grow your knowledge of data.