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[Google] Foundations of Digital Marketing and E-commerce: Data ethics

Data can contain information about user interests and behaviors and even individual customer purchases. This reading introduces you to data ethics. Knowing how to work with user data responsibly and legally is critical to the integrity of your organization, role, and projects.

Data ethics

Data ethics is the study and evaluation of moral challenges related to data collection and analysis. When it comes to data, businesses apply ethical practices so they can:

  • Follow regulations
  • Demonstrate trustworthiness in protecting customer data
  • Ensure the use of customer data is fair and without bias

Follow regulations

Many countries have laws regarding the generation, recording, curating, processing, sharing, and use of personally identifiable data. Personally identifiable data (PII) is information that can be used to directly identify, contact, or locate an individual. Make sure you are aware of your organization’s data security and privacy protocols. Data privacy refers to the proper handling of data. How you collect, process, analyze, share, archive, and delete data should be in accordance with the data privacy laws of the countries where your customers reside.

Protect customer data

One important way to protect customer data is data anonymization. Data anonymization refers to one or more techniques to mask or remove personal information from data to protect the identities of people. Data anonymization is often performed on data coming from multiple sources. After the data has been anonymized, it can be more widely and freely shared in an organization. Types of data often anonymized are names, telephone numbers, email addresses, photographs, account numbers, and purchase transactions.

Use data fairly and without bias

Another ethical data practice is making sure that the data you collect and use is for legitimate business purposes. Fair and reasonable use of data also means that you don’t use the data in a biased manner. Data bias is a type of human error that skews results in a certain direction. Note that data bias isn’t the same as selecting data from a target audience. For example, let’s say you want to review historical data from customers between the ages of 21 and 45. That’s not data bias. What would be considered data bias is if you exclude the data from customers who returned products because you don’t consider them loyal to your brand. However, even when including all available data, you’re not always free of bias. This is possible if historical data was from an audience that wasn’t representative of all potential customers. If you create future ad campaigns based on previous customer behaviors, you could unknowingly perpetuate a bias.

Pro tip: To minimize the risk of data bias, ask for peer review of critical data that you intend to use so you can incorporate different perspectives right away.

Key takeaway

Data ethics is important because it promotes the responsible use of customer data. Always be careful to follow the data privacy laws in your country and the countries where your customers live, protect customer data, and avoid data bias.