In today's data-centric world, data is the new gold. Perhaps nowhere is that truer than with data brokers. In the simplest terms, data brokers source and gather data and then resell the most important parts. Today, some extremely large and well-known companies, including Oracle, McKinsey, Accenture, and Experian, act as data brokers. It is a multi billion-dollar-a-year industry that is not just growing rapidly from a business perspective, but ballooning along with all the sources of data.
From a health perspective, a large percentage of the data that data brokers have is from hospitals, doctor's offices, ACOs, and so on. That data must be anonymized prior to sale. In fact, Health and Human Services (HHS) has a 32-page document stipulating the requirements around de-anonymized information.10 Quite often, though, the byproduct of the anonymization process is that age, gender, partial ZIP codes, and doctors' names are still in the data.11 It becomes relatively easy in most cases to match that data with other large data sources. In fact, that is a challenge for healthcare adjacent organizations that work with the data. They can and do send out unmatched data against alternate sources in order to accurately identify individuals—all in the name of helping people. Many of these legitimate companies do not permanently save that information. That is not to say that data brokers are illegal; it is just that they have found a way to profit off a loophole in privacy regulation given more modern capabilities.
We, as individuals, in most states, do not have the right to prevent that data from going to data brokers—or to delete it once it is part of that data ecosystem. That means our data is often shared without our knowledge with an array of different brokers that can make use of the information in literally untold ways. In some cases the data really does move science forward, but in other cases it is used for advertising purposes. Each piece of data is valuable in different ways to different organizations.
We are just beginning to learn about that market, but Patientory estimates that it is a multi billion-dollar-a-year market for healthcare data.12 Putting together the full list of your doctor visits, blood tests, prescriptions, IoMT, and so on, is extraordinarily valuable. But so far we have been talking about data from covered entities that are being used. Data brokers also pull data from health applications, fitness watches, and genetic testing. Tie these data sources together and it is a cornucopia of valuable data. In the end, data brokers can gather thousands of data points on any given person. They quietly sell your personal information without any of us being the wiser.
If we sidestep the conversation about HIPAA data and look more keenly at some of the uses of other kinds of data, the story takes an interesting turn. A tremendous amount of available data relates to location data from a variety of sources. Some of the sources include loyalty cards, public records, social media posts, cell phone data, browser behaviors, and so on in order to get as complete a view as possible about consumers—about you and me. 13
There are some concerns about the accuracy of the data, however. Harvard Business Review did a test by gathering data from multiple data providers to assess how accurate it was. The results left much to be desired. For example, identifying if someone was male or not only had a 42.5% accuracy. Age was a little better at 77% accuracy.14 But what does this mean if inaccurate information is mixed in with personal details such as name, address, and phone number? On the innocent side of things, you will gain advertisement you may or may not be interested in. On the opposite side of things, false information can prevent you from getting a job, or worse.
To address this, the Federal Trade Commission (FTC) is looking into studying the harm that can befall consumers as a result of bad data. Some of the harms they have noted include predatory pricing and racial profiling. Many key companies change their prices depending on the location of the individuals or their racial and ethnic backgrounds.15 In another case the FTC cites, Google was allowing illegal pharmacies to target users in the search engines—for which they paid a $500 million civil forfeiture.16 The FTC even goes so far as to say big data helped to facilitate the subprime mortgage crisis in the mid-2000s.17 Years later, there is relatively little oversight of big data despite these not so insignificant problems.
Still, advertisers love to know this information so they can send targeted advertising. That seems innocent enough if that is all that is being done with the data. Mobilewalla, a Singapore-based search portal for applications that target mobile devices, decided to publish age, sex, and ethnicity data related to the George Floyd protests. For many, this was a wake-up call for how much data is leaking out of our mobile phones.18 It should also be a wake-up call that information about American citizens can be bought and sold all over the world. Why does a Singapore company have that much data on United States citizens? Why bother hacking organizations if any country can buy any information on any citizen? Internet trolls can also use this kind of information to target individuals. They know who you are and what your preferences are and can use that information to manipulate public opinion. This was a big concern in the 2016 election, no matter what side of the political fence you straddled.
From a legal perspective, unless the data broker uses the data from credit, employment, insurance, or housing, there is no requirement to keep the data private. They can even sell information about health conditions—so long as the data is either anonymized or not from a covered entity.19 This means that there are robust amounts of data with very few protections.
What makes data brokers more interesting is that today we have more data than at any time in history. The digitization of information makes it far easier to stream data all over the planet in a comparatively short period of time. Sifting through that data is also far easier, which means corporations, governments, and people have that information. We truly are in the era of big data.
Big Data
Big data has been around since at least 1937, on a project that Franklin Roosevelt's administration had in relation to the Social Security Act, whose goal was to keep track of 26 million Americans. IBM developed the punch card to keep track of the process.20 It wouldn't be until 2005 when the term “big data” would be coined by Roger Mougalas.21 Big data is exactly what you might think it is—very large sets of data. From a hyperconnected perspective, it ties into many different data sets. The more data from more sources, as long as it is accurate, the better discoveries that can be made as a result of the assessment. It is generally accepted that there are four Vs that go along with big data—volume, velocity, variety, and veracity. All are critically important to the accuracy of information and helping advertisers more accurately target individuals.
Volume is really critical to big data because the more information you have, the better chance you have of having a particular piece of data. Think about it from a COVID-19 perspective. If you had only two people and those two people died as a result of COVID-19, you might come to the erroneous conclusion that COVID-19 was 100% fatal. While that example is absurd, having a large volume of data helps to weed out the statistical improbabilities that a small volume of data might indicate. The larger the data set, the more reliable that data tends to be.
Velocity, generally speaking, centers around the analysis of
