Data privacy has made it to the U. The CCPA is, in some ways, similar to GDPR regulation but differs in that it requires consumers to opt out of data collection rather than putting the onus on service providers. It also names the state as the entity to develop applicable data law rather than a company's internal decision-makers. Data privacy regulations are changing the way businesses capture, store, share and analyze consumer data.
Businesses that are so far untouched by data privacy regulations can expect to have a greater legal obligation to protect consumers' data as more consumers demand privacy rights. Data collection by private companies, though, is unlikely to go away; it will merely change in form as businesses adapt to new laws and regulations.
Adam Uzialko also contributed to the reporting and writing in this article. Some source interviews were conducted for a previous version of this article. Max Freedman. Many businesses collect data for multifold purposes. Here's how to know what they're doing with your personal data and whether it is secure. Types of consumer data businesses collect The consumer data that businesses collect can be broken down into four categories: Personal data.
This category includes personally identifiable information such as Social Security numbers and gender as well as nonpersonally identifiable information, including your IP address, web browser cookies, and device IDs which both your laptop and mobile device have. Engagement data. This type of data details how consumers interact with a business's website, mobile apps, text messages , social media pages, emails, paid ads and customer service routes. Behavioral data.
This category includes transactional details such as purchase histories, product usage information e. Attitudinal data. This data type encompasses metrics on consumer satisfaction, purchase criteria, product desirability and more.
How do businesses collect your data? Visit Site The bottom line, though, is that companies are using a cornucopia of collection methods and sources to capture and process customer data on metrics, with interest in types of data ranging from demographic data to behavioral data, said Liam Hanham, data science manager at Workday.
Turning data into knowledge Capturing large amounts of data creates the problem of how to sort through and analyze all that data. When companies monetize their data, they get the obvious benefit of increased revenue, but they also get other advantages that are less apparent.
Data monetization directly benefits businesses by increasing their revenue. Directly boosting the bottom line is an obvious benefit for any organization. Companies can earn money from their data by selling it, after taking steps to prepare it for other organizations. They can also sell insights derived from their data or other data-related products. Selling data also allows companies to form mutually beneficial business relationships with other organizations. When selling data to other organizations either directly or through a second-party data marketplace, you communicate with those organizations, which can begin the relationship.
You may find you can continue selling data to that organization in the future or even regularly exchanging data back and forth. In this way, data can help you find lasting business partners. Before you can sell your data, you must take some steps to prepare it. Ensure it is accurate, organized and secure. Set up an efficient means of collecting and organizing your data.
Because of this, selling your data provides you with revenue directly and also helps you get more out of your data yourself. The ability to make money from your data may also give your company the motivation it needs to invest more in the quality of your data.
If you plan to sell your data, you need to pay close attention to its quality. If you already collect data, you could be sitting on a potential goldmine. Your data might also lack value or, more likely, it may not yet be ready for monetization. Before you attempt to earn revenue from your information, make sure it is:.
The first point has to do with data quality. For your data to be valuable, you need to be able to trust its accuracy.
You also need enough data and the right data to have a representative sample — one that you can reasonably assume represents reality. Say, for example, that you own a newspaper website and are considering selling data to local car dealerships. For your data to be worth anything to an organization, it has to be relevant to their business needs. Those needs, of course, will vary from company to company. Determining whether information will be useful to a buyer requires you to understand their goals and figure out how your information can help.
Data about whether certain customers are in the market for a new car would be valuable to a car dealership because it allows them to market directly to people who are interested in their products. Segmenting your data refers to organizing it into relevant categories to make it more useful. The newspaper publisher could segment their audience into groups based on the types of articles people read and the terms they search for. They could then sell data on the users that read car-related stories to car dealerships.
This segmented data would be much more useful to them than a batch of unfiltered, generic information. Keeping your data protected helps to preserve its value and, when using customer information, is absolutely vital for privacy and security. Insufficient security can lead to a loss in customer trust and even legal trouble. If the data includes personally identifiable information, it carries potentially serious risks.
Because of this, you need to strike the right balance between access and security. In some cases, this might require anonymizing data. Some useful data security tools include:. Depending on the type of records you have and how it will be used, you might wish to make sure you also include other aspects before you try to monetize it.
These four things, though, are the main features you should always check for. Determine what data you have available as well as what other information you need, if any, to make that data valuable or enhance its value.
This step basically involves taking stock of everything you have and how it all relates. You should start thinking at this point about how someone could use this data so you can identify any other information you want to collect to make it more marketable. A sample of companies with which PayPal shares user data:.
While text, voice or email messages are encrypted — meaning only the sender and receiver can see or hear the content of the message — the metadata around it can be revealing, Angel says. Metadata refers to the information around the content, such as the identity of the sender and recipient, the time of day it was sent and how often the communication occurred. Metadata might seem harmless, but it can be privacy invasive. For example, if the metadata shows that you called an oncologist, one could infer that you or someone you know has or might have cancer.
However, algorithms can look at other signals. For example, they can track the resolution of your computer screen, the size of the browser, how you move your mouse around, and others. Whether a company looks at aggregate or individual data depends on what they want to do, Angel continues. If they want to find market trends, then grouped data would work. But if they want to send customized services, then individual information is key. For example, one query could be finding out what were the top 10 purchases on a shopping site over the last 12 days, he says.
One way they are lulled into complacency is by the presence of a privacy policy on a website, app or mobile service. Even the label itself — privacy policy — is misleading. The first tactic is placation. The company could reinforce the statement with a video on its website where a smiling employee repeats that data privacy is important. But if they did read the policy, they could find such invasive practices as the intention to share third-party cookies and collect personally identifiable information like name and address, he notes.
The second tactic is diversion. For example, some privacy policies disclose that they gather information from sources such as consumer search firms and public databases. So users know that the company gets information about them from other places. But where exactly? These four tactics — placation, diversion, misnaming and using jargon — contribute to a feeling of resignation among consumers.
The research may explain why initial grassroots efforts to quit Facebook — sparked by the Cambridge Analytica data privacy scandal — died out. The reason is that users feel resigned.
It happens every day: An app, online service or a website will not let consumers use their service or access their content until people accept the terms of service, which most do. There are two kinds of differential privacy.
That means individual data becomes jumbled so it is not useful to the company. They got to learn about their genetic ancestry, use a mobile app, or browse the latest footwear trends from the comfort of their computer. This is the same sort of bargain Facebook and Google offer. You pay with your personal data, which is used to target you with ads.
The trade-off between the data you give and the services you get may or may not be worth it, but another breed of business amasses, analyzes, and sells your information without giving you anything at all: data brokers.
These firms compile info from publicly available sources like property records, marriage licenses, and court cases. They may also gather your medical records , browsing history, social media connections, and online purchases.
Depending on where you live, data brokers might even purchase your information from the Department of Motor Vehicles. Retail stores sell info to data brokers, too. The information data brokers collect may be inaccurate or out of date. Still, it can be incredibly valuable to corporations, marketers, investors, and individuals.
Data brokers are also valuable resources for abusers and stalkers. While you can delete your Facebook account relatively easily, getting these firms to remove your information is time-consuming, complicated, and sometimes impossible. In fact, the process is so burdensome that you can pay a service to do it on your behalf.
Amassing and selling your data like this is perfectly legal. While some states, including California and Vermont , have recently moved to put more restrictions on data brokers, they remain largely unregulated. The Fair Credit Reporting Act dictates how information collected for credit, employment, and insurance reasons may be used, but some data brokers have been caught skirting the law.
There are also few laws governing how social media companies may collect data about their users. In the United States, no modern federal privacy regulation exists, and the government can even legally request digital data held by companies without a warrant in many circumstances though the Supreme Court recently expanded Fourth Amendment protections to a narrow type of location data.
The good news is, the information you share online does contribute to the global store of useful knowledge: Researchers from a number of academic disciplines study social media posts and other user-generated data to learn more about humanity. Personal data is also used by artificial intelligence researchers to train their automated programs.
Every day, users around the globe upload billions of photos, videos, text posts, and audio clips to sites like YouTube, Facebook, Instagram, and Twitter. Your selfies are literally making the robots smarter.
Humans have used technological devices to collect and process data about the world for thousands of years. Two millennia later, in the late s, Herman Hollerith invented the tabulating machine , a punch card device that helped process data from the United States Census.
Hollerith created a company to market his invention that later merged into what is now IBM. By the s, the US government was using powerful mainframe computers to store and process an enormous amount of data on nearly every American.
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