Personal data conversations are all around us. Companies, governments, associations and non-profits, consumer advocacy groups, and many individuals have data on top of mind.

• What is it?
• How do we collect it?
• How do protect it?
• How do we hide it?
• What can we get out of it?
• How can we predict people's behavior with it?
• How do we manage it?
• How do we monetize it?
• How do we regulate it?

Frankly, the list of questions and answers regarding data is endless.

As I think about personal data I tend to start by organizing it into three categories:

1. Attribute data also referred to as "inherent data," or "consumer scores," are data and labels (e.g. scores) associated with an individual. Examples of attribute data include an individual's name, address, age, gender, credit score, political affiliation, sexual orientation, mental health status, probability of being in the market for a particular good or service, etc. According to some, leading data brokers have amassed thousands of attributes on hundreds of millions of people and have derived tens if not hundreds of behavioral and predictive scores for each one.
2. Activity data also referred to as "behavioral data" refers to data observed from an individual's behaviors, such as an individual's purchase history, financial transactions, social media posts and graphs, physical locations, browser history, search history, etc.
3. Capital data is data generated by the assets that people own, such as their fitness tracker, connected car, connected home, etc.

It is the idea of capital data that I think we need to work on, as the supplier of the data, the IoT device owner, is not being adequately compensated.