Proprietary Data

Proprietary data is data that someone owns – or claims to. Usually, companies will lay claim to data that is A.) unique to them, and B.) provides them a competitive advantage. Financial records, transaction data, intellectual property, and process documentation are all examples of proprietary data.

Most proprietary data originates internally. A company’s payroll sheets, industry research, secret recipes – all of these are directly created by the company itself.

Some proprietary data has external origins, and only becomes proprietary when a company adds value to it. For example, the layout of Milwaukee’s streets does not intrinsically belong to Google Maps. These streets were laid before Google was created, and plenty of other people mapped them before Google came along.

Google maintains their own proprietary representation of the streets in Google Maps. They continually add value to this data by photographing these streets, documenting information on local businesses, and providing instant directions straight to your smartphone. So Google Maps’ data on these streets is proprietary data, even if the layout of the streets does not inherently belong to them.

At the opposite end of the spectrum from proprietary data, you have public data, which no one person or organization has any claim to. A list of American presidents would be considered public data, for instance – this is factual information that is widely known, and is not unique to any one organization.

If an organization’s proprietary data becomes publicized, it may become public data. If Acme Donut Company publishes an article detailing their particular recipe for making donuts, it becomes harder to claim that ownership of that data is unique to them.

If a competitor copies their recipe, on the other hand, and then publishes their own version, this might also endanger Acme’s ownership of the recipe. If Acme wanted to press their claim in court, they could file suit – but because the recipe for donuts is common knowledge, they would have to prove that their method was truly unique.

How Companies Use Proprietary Data

Many companies directly market their data as an asset. SEO software providers, such as Ahrefs or Moz, gather data about the internet, and sell access to that data to marketers looking to reach digital audiences. When this data involves user data, it is usually anonymized so that anyone buying the data won’t uncover any sensitive data specific to a particular individuals.

Proprietary data has become more abundant – and more valuable – over time. Once upon a time, marketing firms had to do surveys and compile research to show their campaigns were having an effect. If sales went up in the wake of a new campaign, that might mean the ads were a success – but there could also have been other factors at play, such as seasonal fluctuations and changes in customers’ taste.

Digital companies, such as Google and Facebook, can measure the effectiveness of campaigns far more closely; these companies provide data on how many people viewed and clicked on a given ad, allowing advertisers to tweak and adjust their campaigns based on hard data.

And in recent years, the advent of machine learning has made proprietary data even more valuable. Machine learning relies on the use of training data to hone an artificial intelligence. Researchers use this data to test and grade the AI; naturally, more and better datasets can then lead to better artificial intelligences.

Naturally, as proprietary data becomes more valuable, it becomes ever more important for companies to protect their proprietary data from legal challenges and unauthorized individuals.

Protecting Proprietary Data

Proprietary data is often protected by patents, copyright laws, and legal contracts. It’s very common, for instance, for businesses to have new employees sign non-disclosure agreements in which they state they will not share the company’s proprietary data.

Companies also try to keep this data from being stolen. Access controls, for instance, limit who can view and modify the data to begin with. To access the data, a user must first submit credentials so as to verify their identity. By limiting access to data, companies can significantly limit opportunities for proprietary data to escape the organization.

Companies also encrypt data, so that it’s legible only to authorized individuals. This makes it considerably harder for unauthorized individuals to read or modify the data. It’s not uncommon for encrypted data to be stolen by a hacker with no ability to actually crack into the data itself.

Most importantly, someone at the company has to be designated responsible for the data. The person who holds that accountability is considered the data owner. Their job is not only to protect the data, but to ensure the company’s data policies align with regulatory requirements.

Usually, “data owner” is not a strict job title. This person might be a Chief Data Officer, or someone in the company’s IT or legal departments. What matters is that at the end of the day, someone is responsible for the data.

Current Issues with Proprietary Data

Questions of data ownership can become especially precarious when it comes to customer data. Does behavioral data pertaining to a user belong solely to the company that collects it, or should users have some say in how their information is processed, bartered, and monetized?

Because companies such as Google are so reliant on user data, they spend millions lobbying to assert their claim to it. But increasingly, lawmakers and regulators have called their ownership into question.

In the European Union, for instance, the General Data Protection Regulation asserts that web users have a right to be forgotten, meaning that tech companies must be prepared to delete all data pertaining to a given user upon request.

Many businesses have also raised anti-trust concerns regarding proprietary data, especially when it comes to platform businesses such as Amazon.

In 2020, the Wall Street Journal found that Amazon had used their storefront data to undercut these third-party sellers with its own in-house products. In a recent deal with the EU, Amazon promised not to leverage this proprietary data to compete with third-party sellers going forward. Amazon’s proprietary data gave them a real competitive advantage here – but the scale of their platform made that leveraging that advantage unfair to their third-party sellers.