In this course, you will explore your organization’s current data privacy approach and assess how vulnerable your customers’ personal identification data is to data breaches. First, you will explore various categories of data breaches, both intentional and unintentional. You will then determine the privacy risk for your customer data by classifying each data element’s identifiability and sensitivity. Next, you will explore the Organization Data Privacy Journey model and assess where your firm is in that journey. By the end of this course, you will have drawn the connection between data privacy and risk through the exploration of customer relationship management and digital advertising use cases.
In this course, you will determine the best defensive strategy for data usage under privacy constraints. You will explore the relationship between transparency and control by conducting an audit of your organization’s privacy policy. You will evaluate high-level protection approaches to masking customer data and examine data from a customer acquisition campaign to assess accuracy of the metrics. Finally, you will measure profitability loss from defensive data protection measures.

In this course, you will create the best data protection solution to optimize marketing insights for your organization. There are privacy issues and risks associated with customer data as well as various tactics to reduce the identifiability of individuals, while also maximizing revenue from that customer data. By exploring graphical techniques, you will choose the point at which you can best maximize profitability and customer privacy. This course will guide you through a four-step process that will transform your data. In addition to existing best practices, this course explores a cutting-edge technique that allows you to use synthetic data to maximize a firm's revenue.

Defensive Data Strategies is required to be completed prior to starting this course.

 

Unstructured data is more difficult to protect than data that is structured. In this course, you will examine new and emerging data privacy issues and solutions surrounding unstructured data. This data includes textual data, time series data, and spatial-temporal data (e.g., mapping and location apps). You will also explore Internet of Things (IoT) data, how it can add value to your organization, and the accompanying privacy issues associated with that data. While striving to maximize revenue for your organization and simultaneously respect the privacy of your customers, you will explore the privacy challenges associated with geospatial data and ways to mask your customers’ identities. In the final phases of this course, you will consider different strategies for achieving differential privacy to both satisfy your organizational business goals and protect your customers’ privacy.

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