Why data ethics is important for the privacy and protection of your customer data

With customer data playing an increasingly important role in the way businesses are run, it has become important to reflect on the notion of “data ethics” – the ethical ways to manage the massive amount of information collected through digital systems every day.

The introduction of technologies and methodologies for the analysis of massive amounts of data has changed the way companies do business. Customer data is collected by companies (not just web companies) to discover the links between different events and predict future behaviors in order to gain a competitive advantage over their competitors. 

In marketing, data analysis is now a common practice, because it allows businesses to drive company growth by building more lasting relationships with customers. They do so through the collection of factual information about their tastes, interests, and habits. The ability to extrapolate and analyze a huge amount of data – both structured and unstructured – continues to help companies scale their businesses.  The question of how to manage information that is often sensitive has gathered increasing attention.

In this article, we will discuss what a business can do to ensure to protect the privacy of its customers. We also look at how an organization can establish analysis systems and knowledge generation that account for ethical principles that respect and protect internet users.

What is data ethics?

For the majority of companies operating online, data management has become a strategic pillar of the business. A data-driven approach helps to make informed decisions, based on objective facts and not on personal feelings. Collecting accurate and fresh customer data is critical in the fast-paced digital world we live in today. This data helps to understand KPIs, generate statistics, and identify technical problems that may be occurring during the company’s life.

Big Data is a powerful tool, especially in companies where advanced analytics are used to manage production or sales. However, dealing with behavioral information can raise ethical issues that must be addressed to help find the balance between improved user experience and the protection of users’ privacy.

When we speak of data ethics, we refer to those principles that guide the correct use of data from a moral standpoint. Determining what is the right (and wrong) way to collect, use, store, and analyze data collected through digital means has an impact on both individual users and society as a whole. Specifically, it’s important to consider who has ownership of the data, how transparent the communication regarding its use is, whether users have given their consent, and how well privacy is protected.

A first step toward the establishment of a fixed set of principles valid for every business, was made by the European Union, which introduced the General Data Protection Regulation (GDPR) in 2016. It helped identify ethical parameters to follow when collecting data and extracting knowledge. Let’s take a look at the key principles of data ethics.

Customer data protection as a service: six GDPR principles of data ethics

The new European General Data Protection Regulation (GDPR) states that the processing of all personal data must be aligned with the principles defined in the regulation. As part of the effort to implement the regulation, it is important to understand the six key principles of the GDPR. These principles form the basis of the GDPR requirements – let’s understand what they are and what a company needs to account for in order to comply.

1. Transparency and lawfulness

Companies that collect customer data need to make sure that their users know for what reasons information is handled and processed. To legally collect data, an organization must make its privacy policy public, explaining with maximum clarity the purpose of the data collection and how the company intends to use the information.

2. Specific reasons for collection

The processing of personal data must be limited to the legitimate purpose for which such personal data was originally collected. In other words, a company should make clear in advance why it is collecting data and, if a user agrees on such terms, use the data only for the purpose that is specified. This effectively prevents the processing of personal data outside of the legitimate purposes for which the customer data was collected.

3. Process only the customer data that is necessary

This principle is known as “data minimization” and means that when collecting data, a company may only request personal data that is absolutely necessary for the purpose that is specified. For the GDPR, it is not allowed to request or store data that is not essential for the stated goals of the company. It is important to limit data analysis to an anonymized data set, a data set for which consent has been obtained, or for which there is a clear legitimate processing purpose.

4. Data should be accurate

Organizations should take measures to make sure the data collected is always accurate and updated. Companies are asked to ensure that customer data is kept as accurate as possible at all times and that users from which the data has been collected can update their data when necessary. People have the right to request that their personal information is canceled or rectified if inexact or incomplete, and following such requests the company owner has 30 days to fix the information.

5. Limited retention

Organizations should delete personal data when it is no longer necessary for their purposes. This is not straightforward – the timing changes from company to company and from industry to industry –  and must be determined in accordance with applicable laws, which may sometimes require that personal data be retained for a longer period than the originally intended processing purpose.

6. Security

Personal data must be processed in a manner that ensures appropriate security of personal data, including protection against unauthorized access and from accidental loss, destruction, or damage. The GDPR does not specify how such measures should be implemented and while the most common method remains encryption, companies are free to choose systems suitable to their objectives.

Ethical by design in the digital economy: building trust with customers

The discussion surrounding data ethics is still evolving and with the introduction of artificial intelligence systems to manage large amounts of information, issues related to algorithm bias are becoming increasingly important. It’s not just humans who are responsible for the correct management of customer data, but also those who program machines to process such data.

“Ethical by design” means that machine-learning algorithms should be created with ethical principles in mind in an attempt to contrast bias that can be present – perhaps unconsciously – in human programmers. There are three steps to avoid AI bias, which should always be part of the process of designing ethically:

• Train algorithms with datasets that are representative of the population. Machines learn based on the data sets they are fed with. By using information that originates from a small section of the population, algorithms may produce results that favor one group over the other. A system that traces the origin of data sets can help identify potential biases.

• Check feedback loops. While machines can be trained through selected datasets, once they are put to work they continue learning through users’ feedback. If an action is repeated by many users, the machine will interpret such action as predictable, favoring some results over others. If, for instance, photographs of a specific body type tend to be “liked” more often on a social media platform, an algorithm may begin showing that body type more often to please the audience, excluding other images.

• Human intervention. It’s important to think about how humans and machines can work together, rather than expecting the algorithm to run on its own. Systems known as “human-in-the-loop” can provide insights into ways in which humans can control how the machine behaves and intervene when it becomes necessary to correct the algorithm.
  

No algorithm is perfect but addressing potential issues related to bias before they impact users can help prevent ethical issues that may arise due to unconscious bias.

Rethinking customer data: some final words

Data analysis has become essential for any business operating online, but with an increasing amount of personal information circulating globally, the question of how to ethically manage such information has gathered increasing attention. How data is collected, used, and shared can impact behaviors, decisions, and perceptions of both individuals and communities. Therefore, it is important to consider ethical principles when collecting information and designing algorithms.

In this article, we have explored the key principles to keep in mind when working with data, but the discussion around ethics remains open. What’s important is to remember that behind every piece of data there are people, and just like you and me, they like to be treated ethically

Do you need help with collecting, understanding, and analysing data for business growth? Get in touch today and let’s discuss your project.