In 2021, internet users generated 79 zettabytes (79 billion terabytes) of data. In 2025, they are expected to generate a whopping 180 zettabytes of data. Needless to say, the data bonanza is a huge boon to marketers who want to understand the needs, wants, behaviour, and shopping habits of any target demographic. Businesses can use this data to produce winning products and services, create targeted ads to meet users’ needs, and set prices to generate maximum profits without driving consumers to a competitor.
At the same time, the massive amount of data available and the many ways in which it can be used can pose ethical challenges for business owners, entrepreneurs and marketers. The fact is that mistakes can be made with respect to data collection, storage, and usage that could lead to a loss of customer trust or even legal action against the company. To this end, every single business requires a code of data ethics, which specifies how data is to be handled and used.
“Data ethics” considers the moral obligations of how we handle data, and how it affects individuals and society. All businesses should consider data ethics, and have a plan in place to follow best practices and regulation with respect to data privacy, security, transparency and more.
The handling of data touches on a range of issues, including data privacy, data ownership, trust and fairness. Managing data in a responsible manner can have a significant impact on a company’s reputation.
Some key pillars of data ethics
For us as marketers, it is very imperative that we understand some of the most important pillars of data ethics, to be able to handle data ethically. As a result, we will become more aware of the importance of data ethics and pay closer attention to our actions on a day-to-day basis.
Preserving data privacy
While recent surveys show that most consumers are happy to share additional personal data in exchange for faster, better, and more personalized services, a business cannot take this as a license to collect data indiscriminately without following the principles of privacy law. Under Canada’s privacy law, PIPEDA, this includes the principles of transparency, consent and security safeguards – to name a few. Transparency is more important than ever. Before collecting, using and sharing consumer data, it is imperative to ensure consumers are aware of what data is collected and how it will be used. Consent is equally important, and in Canada, marketers require some form of consent (or for an exception to apply) to collect, use or share data. Additionally, businesses must take measures to ensure that all personal data is protected and stored safely to prevent unauthorized access.
As government laws and regulations are in the process of being reformed to keep up with advancements in technology, it’s up to businesses to seek legal advice and go beyond legal requirements to ensure consumer data is only used in accordance with consumer wishes. Apple and Google, for instance, are setting the standard for this behaviour by committing themselves to restrict third-party tracking cookies.
Fair data processing
Companies aggregating data that has been collected from multiple sources must ensure that the data is neither manipulated nor used to identify the data subject. While consumer data can be used to provide insight into trends and consumer spending habits, it cannot be used to uncover information that could lead to discrimination against certain groups of people. For instance, a hiring algorithm trained to research information on prospective employees should not be used to collect information related to that individual’s racial background or sexual orientation.
Data quality auditing
Data quality auditing prevents unfair discrimination by ensuring that the data a company uses is complete and representative of the whole population rather than a select segment of individuals.
It’s essential for companies using AI for marketing purposes to ensure the data that is fed into the system is audited for quality on an ongoing basis. Without human supervision and intervention, an automated system may create biases against certain individuals based on past shopping habits or geographic location, for example.
Interdisciplinary algorithmic auditing
Ensuring that the data which is fed into an AI system is accurate and free from bias is important, but it’s not enough on its own. Interdisciplinary algorithmic auditing is necessary to ensure data that is accurate today does not exacerbate current biases.
Interdisciplinary auditing will require the help of professionals in fields such as psychology, behavioural economics, ethics and social science. Hiring algorithms, for instance, must be audited to ensure applications from employees of all walks of life and ethnicities are processed fairly and without discrimination. Algorithms displaying recommended products to consumers must be monitored to avoid biases against new or previous customers from certain geographic locations.
Combating deliberate disinformation
Technology companies have taken heat in recent years for inadvertently amplifying fake news and memes that are either misleading or discriminatory in nature. However, combating misinformation isn’t just the job of technology companies alone. All businesses must verify that any information shared on their social media accounts and websites is accurate. This is an ongoing task, as marketers will need to monitor current and past ad campaigns to ensure the information that has been identified as false or outdated is no longer posted on company accounts.
Final thoughts
The proper handling of data in marketing can benefit companies and consumers alike. However, business owners and marketers must take care to ensure data is collected ethically, stored securely, and used in a transparent and unbiased manner. Using data ethically and responsibly will not only enable a marketer to grow their business, but also set it apart from the competition by building and maintaining consumer trust. However, creating and adhering to policies and procedures related to data ethics is not something marketers can do on their own. Seeking legal help and professional assistance from experts is a must to ensure data is collected, stored, and used appropriately now and into the future.
While the amount of customer data we have access to continues to grow, its value depends on our capabilities as marketers and on whether we use it in an ethical manner. Let’s do our part and use it responsibly.
Additional sources of reference:
5 Principles of Data Ethics for Business, HBS
Data ethics: What it means and what it takes, McKinsey
The rise of data and AI ethics, Deloitte
Why digital trust truly matters, McKinsey
Building data and AI ethics committees, Accenture
Localization of data privacy regulations creates competitive opportunities, McKinsey
Why We Need to Audit Algorithms, HBR
Bias in AI: What it is, Types, Examples & 6 Ways to Fix it, AI Multiple
AI Bias – What Is It and How to Avoid It?, Levity
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