How organizations monetize your personal data

We mentioned in the previous chapter that “Data is the new oil”. In the same way that oil drove economic growth in the 20th century, data will be the fuel that drives economic growth in the 21st century.Companies collect and analyze your personal data with the objective of influencing your perspectives, decisions, and actions. Companies such as Facebook, Google, Amazon, Netflix, and Spotify monetize your data by uncovering the individual propensities and tendencies buried in your data and then using those personal propensities and tendencies to influence your purchase and usage decisions… and sometimes even your opinions.Figure 2.10 shows how Google leverages your free search requests to create a market for advertisers willing to pay to place their products and messages at the top of your search results.

Figure 2.10: Data flows in a typical real-time bidding system(taken from eff.org/deeplinks/2020/03/google-says-it-doesnt...

Summary

In this chapter, we explored the differences between data and big data, and how organizations use your personal data to understand your tendencies and propensities. We discussed how technologies such as the IoT and third-party data aggregators capture and analyze your transactions and activities to learn even more about your personal tendencies and propensities. We then explored data privacy efforts, both at the federal and state government levels to protect your data privacy, and how some organizations can just decide to ignore those protections for their own nefarious means. We wrapped up the chapter with a look at how one leading data monetization company – Google – provides free services in exchange for your data, which they subsequently monetize in a number of perfectly legal ways.So, how can one protect themselves from being abused by their own data? The first step is awareness and vigilance. We must always be aware of how organizations capture and exploit...

References

1. New York Times, We Read 150 Privacy Policies. They Were an Incomprehensible Disaster: https://www.nytimes.com/interactive/2019/06/12/opinion/facebook-google-privacy-policies.html
2. Mobile Privacy, What Do Your Apps Know About You?: https://symantec-enterprise-blogs.security.com/blogs/threat-intelligence/mobile-privacy-apps
3. TechCrunch, Judge orders Amazon to turn over Echo recordings in double murder case: https://techcrunch.com/2018/11/14/amazon-echo-recordings-judge-murder-case/

Navigating the analytics maturity index

I struggled with this section, but not because explaining the different analytic algorithms and concepts was hard. For me, the complicated problem was how to classify and group these different algorithms in a way that makes it easier for the average Citizen of Data Science to understand where and how to apply these algorithms.

Leading organizations are cultivating a culture of continuous exploration, learning, adaptation, and re-learning. This iterative process involves gathering insights from every customer engagement to understand their preferences and behaviors better. Similarly, interactions with products enable organizations to gain knowledge about product performance and behaviors while engagement with employees, suppliers, and partners provides valuable opportunities to improve the effectiveness and efficiency of business operations. To create a continuous learning culture, organizations need to understand the Analytics...

Summary

While you may never be asked to program a neural network or an unsupervised machine learning algorithm, Citizens of Data Science must have a rough idea of how these algorithms work and what these algorithms are good at doing. Hopefully, the chapter helped to demystify data science and advanced analytics and provided a foundation for everyone to feel comfortable and empowered to participate in these analytics conversations.

Finally, there is a dramatic conceptual difference between level-3 and level-4 analytics. The primary goal of level-3 analytics is to help improve, or optimize, human decision-making. However, level-4 analytics introduces an entirely new concept with learning analytics – analytics that can learn and adapt with minimal human intervention.

The next chapter will focus on understanding how AI works and the critical role of the AI utility function in guiding an AI model’s workings. I will also explain the vital role that you as humans play...