What Netflix's “Great Hack” tells us about the future of our data
- The world has recently been exposed to the dark side of social media as our personal information is shared with corporations, oftentimes without our knowledge.
- Netflix's "The Great Hack" covers the now-famous Cambridge Analytica scandal and paints a picture of our current privacy landscape.
- Blockchain and other decentralized ledger technologies could be the answer for increasing data security and transparency.
The world promised by the internet and social media is one where physical barriers are a thing of the past and communication is instantaneous. The current reality has some of that promise, with communication faster and better than ever before. However, along with this ease of communication comes a dark side that we have only recently been exposed to. In order to power our instant messaging, personalized newsfeeds, and one-click shopping, we have agreed to lease out our personal information, even data we are unaware of, to corporations which are singularly focused on maintaining their bottom lines at all costs.
The recent Netflix documentary, "The Great Hack", covers the Cambridge Analytica scandal, a breach that saw the information of millions of unwitting Facebook users exposed and exploited for political gain. The documentary focuses on a specific case, but the lessons it imparts and the picture it paints of our current privacy landscape are chilling, to say the least. We are more exposed to corporations' whims today than we have ever been, and they are increasingly willing to leverage the massive troves of data they have spent years collecting from us.
Our unfortunate status quo is not completely bleak, however. This emerging awareness about the precarious state of our privacy and data security has led to a mass movement that seeks to restore the balance. Data analytics, and especially predictive analytics, are key cogs in our tech future. They offer a significant upgrade for our ability to understand our world. However, the benefits of these technologies must be tempered with a real focus on users' privacy.
Are Data Analytics Catalyzing Dystopia?
There is not a shadow of a doubt that data is quickly becoming the most important commodity in the world. Beyond consumer data, every single industry and service produces troves of data every day. According to the world economic forum, a single connected car alone produces 4 terabytes of data daily, while Facebook itself generates 4 petabytes of data. The WEF estimates that by 2025, we will produce 463 exabytes of data worldwide, every day. In this climate, user data plays a particularly important role.
For corporations like Facebook, Amazon, Netflix, and Apple—as well as myriad smaller companies—user data is a foundational aspect of their business model. Improving content algorithms, tailoring ads, and providing more personalized experiences are all made possible by this data, though this is only the positive side.
The power of predictive analytics, as evidenced by "The Great Hack", is that this stockpile of user data—which includes everything we do online, from search terms to clicks and even our friends' activities—can be used for other, more nefarious purposes. In Cambridge Analytica's case, it was used to drive voters towards the Brexit and Trump campaigns by presenting tailored, biased content that reinforced the campaigns' points.
More importantly, however, are the emerging concerns about data security and analytics. With the number of companies that hold some or all our private information, the fact that there have been so relatively few breaches is surprising. Even so, a single breach—such as Cambridge Analytica—can expose the data of millions of users, many times without them being aware of it. In this environment, finding alternatives that do not hamper analytics but protect users is increasingly challenging, though not impossible.
The Future Is Not All Bad
The future of data is undoubtedly concerning, though it is not necessarily all dark. Technologies like blockchain and decentralized ledgers offer an alternative that provides a potential infrastructure for analytics that respects data privacy and security. The current ecosystem clearly benefits a small group of data holders and users—corporations.
Facebook, Google, Amazon, and others' terms and conditions give them almost exclusive access to users' personal data to use as they see fit, and unless it clearly benefits them—as in the Cambridge Analytica case—they are loath to share their capability. With such a dearth of access to valuable data, researchers, small businesses, and even private individuals have limited analytics capabilities. Moreover, they must trust the security of their data to researchers who are not concerned with their privacy or data safety.
Blockchain and DLT support an alternative that could deliver greater transparency and easier access to data and the tools needed for analysis thanks to their decentralized structure. One of the major complaints users and advocates have regarding major corporations' use of data is their lack of transparency about how data is collected, used, and with whom it is shared. Here, decentralized ledgers offer a seemingly ready-made response. By their nature, DLT systems make data transparent as any transaction or operation is instantly synced across every node in a network. Although reality does not always align with this theory, it does offer a greater guarantee that users can see how their data is being used.
Additionally, while major corporations continue to be hit and blindsided by data breaches and hacks, blockchain data storage promises to be more secure thanks to its encryption standards and the difficulty of breaching blockchains themselves. While this also remains largely theoretical due to the relative recency of the technology and its still-unproven claims, it could provide researchers significantly safer and better access to data analytics tools.
Avoiding the Dystopian Data Future
Users are decidedly behind in terms of protecting their data from corporations. We have already given away the keys to the kingdom, so to speak, but it does not have to be a permanent arrangement. The current landscape is undoubtedly dark—our data is in the hands of corporations which are all too happy to use it for any purpose that may enrich them—but it can become brighter. Should new alternatives to the current analytics and big data models prove worthy and effective, we could see a major shift in access and transparency when it comes to our data and how it's invariably monetized.
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Rank 0.5 – Albert Einstein<img type="lazy-image" data-runner-src="https://assets.rebelmouse.io/eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9.eyJpbWFnZSI6Imh0dHBzOi8vYXNzZXRzLnJibC5tcy8yNDQ0NDY3NS9vcmlnaW4uanBnIiwiZXhwaXJlc19hdCI6MTYzNjI2NTU4OH0.FtBYC7oJz-ZOiiGC9y0Z50_JvQChmp-ONa3jhR3SuLA/img.jpg?width=980" id="d6f66" class="rm-shortcode" data-rm-shortcode-id="61288810a4f035ec2af8957fad4e9015" data-rm-shortcode-name="rebelmouse-image" />
Albert Einstein With Displaced Children From Concentration Camps. 1949.
Photo by Keystone-France/Gamma-Keystone via Getty Images
Rank 1<p>The group in this class of the smartest physicists included the top minds that developed the theories of quantum mechanics.</p><p><a href="http://en.wikipedia.org/wiki/Werner_Heisenberg" target="_blank">Werner Heisenberg</a> (1901 - 1976) - a German theoretical physicist, who's achieved pop-culture fame by being the name of Walter White's alter ego in <em>Breaking Bad</em>. He is known for the Heiseinberg Uncertainty Principle and his 1932 Nobel Prize award flatly states it was for nothing less than "the creation of quantum mechanics".</p><p><a href="http://en.wikipedia.org/wiki/Erwin_Schr%C3%B6dinger" target="_blank">Erwin Schrödinger</a> (1887 - 1961) - an Austrian-Irish physicist who gave us the infamous "Schroedinger's Cat" thought experiment and other mind-benders from quantum mechanics. The Nobel-prize-winner's <a href="https://en.wikipedia.org/wiki/Schr%C3%B6dinger_equation" target="_blank" rel="noopener noreferrer">Schrödinger equation</a> calculates the <a href="https://en.wikipedia.org/wiki/Wave_function" target="_blank">wave function</a> of a system and how it changes over time. </p>
Erwin Schrödinger. 1933.
Satyendra Nath Bose. 1930s.
Enrico Fermi. 1950s.
Rank 2.5<img type="lazy-image" data-runner-src="https://assets.rebelmouse.io/eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9.eyJpbWFnZSI6Imh0dHBzOi8vYXNzZXRzLnJibC5tcy8yNDQ0NDcwNy9vcmlnaW4uanBnIiwiZXhwaXJlc19hdCI6MTY0NDE1MDIxM30.Eg6tca61EredHxjqNH29HY3UeJbgBVa1nA13EhXTooU/img.jpg?width=980" id="90f86" class="rm-shortcode" data-rm-shortcode-id="0f1e6c5e13263a77b2061e1191fd8baf" data-rm-shortcode-name="rebelmouse-image" />
Lev Landau. 1962.<p><strong>Rank 2.5</strong> is where Landau initially ranked himself, rather modestly, thinking he didn't produce any foundational accomplishments. He later moved his prominence, as his achievement mounted, to the higher <strong>1.5.</strong></p>
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