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was a result of the Target Corporation’s astute data analysis. Target’s analysts were able to identify expectant mothers early in their pregnancy based on their changing purchasing habits. When a new expectant mother was identified, Target would send unsolicited coupons for baby-related items. In one case, the coupons arrived in the mail before the mother had shared that she was pregnant; her family found out about the pregnancy from a retailer. Privacy is such a difficult issue because legitimate actions can violate a person’s privacy.

      Identifying criminals

Another aspect of privacy is when criminals, or other individuals who deliberately want to operate anonymously, hide their identities from exposure. Privacy may be important to the general population, but it's a necessity for criminal activity. The ability to deny, or repudiate, some action is crucial in avoiding discovery and capture, and to any subsequent defense. Money laundering and fraud are two activities in which privacy and anonymity are desired to obfuscate illegal activity.

      On the other hand, law enforcement needs the ability to associate actions with individuals. That’s why laws exist that protect the general public but allow law enforcement to conduct investigations and identify alleged perpetrators.

      Protecting the privacy of law-abiding individuals while identifying criminals has become important across a spectrum of organizations. To enable law enforcement to deal with online privacy issues, legislative bodies have passed various laws to address those issues directly.

      Examining common privacy laws

      Here are a few of the most important privacy-related laws you’ll likely encounter and may be compelled to satisfy:

       Children’s Online Privacy Protection Act (COPPA): Passed in 1998, COPPA requires parental or guardian consent before collecting or using private information about children under the age of 13.

       Health Insurance Portability and Accountability Act (HIPAA): Passed in 1996, HIPAA modernized the flow of healthcare information and contains specific stipulations on protecting the privacy of personal health information (PHI).

       Family Educational Rights and Privacy Act (FERPA): Passed in 1974, FERPA protects access to educational information, including protection for the privacy of student records.

       General Data Protection Regulation (GDPR): Passed in 2016 (and implemented in 2018), GDPR is a comprehensive regulation from the European Union (EU) protecting the private data of EU citizens. Every organization, regardless of location, must comply with GDPR to conduct business with EU citizens. The EU citizen must retain control over his or her own data, its collection, and its use.

       California Consumer Protection Act (CCPA): Passed in 2018, CCPA has been called “GDPR lite” to imply that it includes many of the requirements of GDPR. CCPA requires any organization that conducts business to protect consumer data privacy.

       Anti-Money Laundering Act (AML): AML is a set of laws and regulations that assists law enforcement investigations by requiring financial transactions to be associated with validated identities. AML imposes requirements and procedures on financial institutions that essentially make it very difficult to transfer money without leaving a clear audit trail.

       Know Your Customer (KYC): KYC laws and regulations work with AML to ensure that businesses expend reasonable effort to verify the identity of each customer and business partner. KYC helps to discourage money laundering, bribery, and other financial-based criminal activities that rely on anonymity.

Predicting Future Outcomes with Data

      Data can unlock lots of secrets. Data you collect through regular interactions with your customers and business partners can help you understand them and better meet their needs and wants. Assuming you have taken measures to protect individual privacy and have permission to collect and use the data, analyzing that data can benefit your organization and your customers (and partners, too).

      A common way to use data is to build analytics models that help to explain the data, uncover hidden information, and even predict future behavior. Data analytics is all about using formal methods to unlock secrets that your data is hiding. These secrets aren’t hidden on purpose — they just get lost in the mountains of data you collect. Without a structured approach to examining your data, you might miss some of its value that can lead to increased revenue.

      Classifying entities

An entity is any object that your data describes, such as a customer, a vendor, a product, an order, or anything else that has characteristics data items can describe. In traditional database terms, an entity would correspond to a record or a row. The concept of a row maps to a spreadsheet concept as well. Think of a spreadsheet of customers. Each row would contain all the data that describes a single customer. Figure 1-1 shows a collection of customers in a table format.

      

These customers are stored in a comma-separated value (CSV) text file named customer.csv, and displayed in Visual Studio Code using the Edit as CSV extension. To learn more about Visual Studio Code and its extensions, see Chapter 4.

      Note that each customer has a set of characteristics, such as name, address, and contact, stored in separate columns. Data analytics models use these different characteristics, also called features, to examine how different entities are related.

      FIGURE 1-1: Customer entities presented as a table.

One type of analysis is to examine the features of different entities to see if some features can help group entities or imply some relationship. For example, suppose you asked a group of people to name their favorite baseball team. You would expect that most people who answered “the Colorado Rockies” most likely live near Colorado. However, you can’t always make such simple associations. If you asked the same question in the 1990s, not everyone who answered “the Atlanta Braves” lived in Georgia. During the 1990s, cable TV was becoming popular and Turner Broadcasting System, whose owner also owned the Braves, broadcast all Braves games nationally. Many people who didn’t live in Georgia became Braves fans.

      The Braves example shows that analytics models cannot be trusted unconditionally. Data analytics can provide tremendous value but also requires care and diligence to build models that return results that hold true over time.

      Assuming that you invest sufficiently to build good models, classification models can help to identify entities that are similar. Similarity information helps organizations develop targeted marketing campaigns and services to give customers and partners the sense of being treated individually. You learn about several classification models in Chapter 7 and build a few in Chapter 10.

      Predicting behavior

      Although the capability to classify entities to identify groups of similarity can be valuable, analytics can also make predictions. Past behavior is a strong indication of future behavior. Humans tend to repeat actions and decisions, so you can use models that identify patterns to predict future actions. The capability to predict future actions can have tremendous value to organizations. If an organization can determine items that tend to be purchased together frequently, it can use that information to make additional purchase suggestions.

You’ve undoubtedly seen frequent item analysis results when you shop online.

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