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Foundations of Data Science
Module 2
Important ethical considerations for data professionals
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Important ethical considerations for data professionals
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One of the most important responsibilities for those of us in data-centered careers involves how we protect our organizations, manage and protect data. This has a lot to do with communication exchanges between a company and its customers. As you've been learning, almost all communication generates data, whether it's a shopping receipt, confirmation of an order, or even earning customer loyalty points. Businesses have a big responsibility to their customers, especially when it comes to maintaining and protecting user privacy. Any data gathered from individuals or consumers is referred to as personally identifiable information or PII. PII permits the identity of an individual to be inferred by either direct or indirect means. This includes things like biometric records, usernames, and Social Security or national identification numbers. Because this information is often associated with medical, financial, and employment records, PII is sensitive and must be managed with great care. After all, when someone's personal data is improperly handled, they become vulnerable to identity theft, fraud, and other issues. Recently, there have been great efforts to take a wider view of data collection practices and protect individuals. Industries are trending towards aggregate information. This is data from a significant number of users that has eliminated personal information. By aggregating the data and removing PII, this protects people and gives them more control over their own data. Similarly, as more industries become interconnected, the amount of data available to them increases. Just as with aggregate information, the more data collected, the more likely it is that it will be representative of a wider population rather than a single user. A key thing to keep in mind is that data gathering is a task managed by humans, and that process can be informed by different backgrounds, experiences, beliefs, and worldviews. These and other types of biases can affect the way that data is communicated and how the results are shared, which in turn can have an impact on business decisions. Effective data professionals know that, whether collecting, analyzing, interpreting, or communicating sensitive data, bias should always be considered. So be very careful when interpreting data where there is a clear source of bias and be on the lookout for subtle biases as well. In addition to thinking through bias in the data, data professionals should also try to emphasize that there can be a multitude of possible interpretations for every data insight. The main trick is avoid jumping to conclusions until you've really done your homework. One method of addressing bias is to make sure that the data that you're working with has the same characteristics as the greater population that you're interested in. In data analytics, this is called a sample. A good sample is a segment of a population that is representative of that entire population. Here's an example. A clothing company is analyzing sales in their highest growth market. They want to determine what color shirts will be most popular in the upcoming season. One person notes that red and blue shirts accounted for 80 percent of their sales in this market over the past three months. This is a big number. So they suggest ordering lots of red and blue shirts, but another person points out that the local sports team's colors are red and blue, and this team had recently won a championship. It's very likely that sales of red and blue shirts will have spiked as consumers purchase tease to support the local team. Plus, they note that although this market is high-growth, it only represents 40 percent of the retailer's total sales. With all this information in mind, decision-makers at this retailer instead choose to evaluate color popularity over a full year and across all markets. This will provide a much more complete picture. We'll investigate more about bias later in this program, and as you progress, you'll discover many more strategies for ensuring that you're aware of bias and proactively working to counter it in all of your data work.
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