In the digital age, businesses have access to extensive information about their customers. This data can help businesses personalize offerings and reach consumers in a way that reflects their individuality. Advances in data analytics make it easier to combine information, such as preferences, shopping patterns, and sensitivity to price into useful templates for suggesting products. This seems like a win-win for marketers, who can identify those who are most likely to want their products, and end users, who receive communications tailored specifically to them.
Privacy, however, is a major issue when it comes to using customer data. As more people share information online and breaches become more common, the importance of protecting individuals’ identities has grown. Despite trying to preserve the privacy of their customers, companies sometimes run into major problems.
Before diving into the potential data privacy issues that businesses can run into, you need to understand why data privacy is so important. Here’s a look into why privacy should be at the forefront of all data science and data analytics activities.
What Is Data Privacy and Why Is It Important?
Data privacy is a branch of data security concerned with the proper handling of data.
When organizations collect and use consumer data, they have an obligation to consumers to handle the data ethically and responsibly. By failing to inform consumers that their personal information is being collected or misusing the data in a way that threatens their privacy, organizations are not only putting their customers at risk, but also the organization’s reputation and legal standing.
Data privacy is important because it protects consumers’ personal information and helps organizations maintain ethical business practices, uphold their reputation, and avoid potential financial implications associated with the misuse of consumer data.
Here are three big data privacy issues companies should avoid and insight into how businesses can mitigate privacy risks associated with big data analytics.
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1. Failing to Mask Customer Data
To maintain the anonymity of customers and other data subjects, organizations need to effectively mask, or de-identify, sensitive data so it doesn’t end up in the wrong hands.
When companies de-identify a dataset, they remove all personally-identifiable information (PII). Sometimes, PII can be replaced with modified content to keep analytics intact while protecting the subjects’ privacy. However, PII can still slip through the cracks and have devastating consequences.
In October 2006, Netflix offered $1,000,000 to any individual or group who could figure out a way to improve its DVD recommendations to subscribers by 10 percent or more. It released a de-identified historical dataset with information from hundreds of thousands of users about the grades they’d given to various movies.
Although they stripped names and ID numbers from the data, many Netflix customers used other rating sites, such as IMDB. Comparing ratings on IMDB with those in the de-identified Netflix database allowed researchers to accurately determine the user’s identity. This is called re-identification, and ultimately led to an expensive legal settlement.
It was later found that Netflix could have invested in data masking technology to avoid issues with anonymizing customer data. This would've cost about $50,000—a small amount compared to their expensive legal settlement.
2. Taking Targeted Advertisement Too Far
Companies often track consumers’ digital behavior and combine this data with demographic information to personalize advertisements for products and services.
These targeted advertisements are intended to provide consumers with value by presenting them with products that they actually want. However, targeted advertisements are often seen as an invasion of privacy, and it can be difficult for companies to determine where to draw the line between helpful and invasive.
For example, in 2010, Target implemented a new algorithm that analyzed changes in customers’ buying habits to identify women who were newly pregnant. Target was able to reach out to these women and offer them products that would be useful to them. Because pregnancy and its associated changes happen quickly, a rapid algorithm was valuable.
However, the company found itself in the middle of a scandal when it sent ads for baby products to a teenage girl living with her parents, whom she had not yet told about her pregnancy. This story exploded over the news and social media.
Target has since eased up on its direct marketing and now includes products of interest to a wider audience along with any targeted promotions to avoid similar situations in the future.
3. Using Data Without Permission
Using PII without consent is both unethical and potentially illegal.
Companies must receive explicit consent before they can collect and utilize personal data from customers. However, businesses often overlook this crucial step in the data collection process.
On Black Friday in 2011, two malls used a new mobile technology to track shoppers as they moved through the mall, allowing them to send location-specific alerts to customer’s phones. In addition to helping marketers target the right people, monitoring the flow of shoppers through the mall would help stores determine how to staff during the busy holiday season. Unfortunately, this was done without the knowledge or consent of shoppers.
Not only were mall visitors upset about marketers’ use of their phones for tracking purposes, but Senator Chuck Schumer (D-NY) denounced the practice at a press conference. Both malls cancelled the program, which was intended to run through New Year’s Day, within a week.
This example highlights the importance of allowing customers to opt-in and voluntarily provide their data to preserve their right to privacy. Rather than technology that collects data from any mall visitor who hasn’t turned off their phone, some stores are now using a similar technology, but only with customers who choose to install an app on their phone and provide consent.
Maintaining Privacy and Using Data Ethically
Data is a powerful tool that companies can harness to inform business decisions and boost profitability. But as the saying goes, with great knowledge comes great responsibility.
Companies must do everything they can to use customer data ethically, preserve customers' privacy, keep them informed of how their data is being used, provide consumers with options to opt in or out, and walk the fine line between serving up relevant, targeted content and overstepping boundaries.
Whether you’re a data scientist, data analyst, or anyone else working with data, expanding upon your data science skill set can help you learn best practices regarding data privacy and teach you to work with data more effectively, efficiently, and ethically.
Are you interested in learning more about data science and how you can become adept at working with data? Download our Beginner’s Guide to Data & Analytics to learn more about data science concepts and applications.
This post was updated on March 19, 2021. It was originally published on September 1, 2015.