In general, there will be three Impacts coming from data:
1. It is sold, refined, and obtained in different ways and thus demands new approach from regulators
2. Legal battles will be fought over who controls data
3. It’s super lucrative
Types of data:
1. Classic administrative databases (age, sex, location, etc.)
2. Literally everything (is your toast burnt or not, what food do you eat for breakfast, streams of data from jet sensors, etc.). Sensors will be everywhere, and we will leave data behind in everything that we do.
Uses of data:
1. Personalized advertising
2. Improving machine learning such as personality assessor and facial recognition system. This can be sold as further products.
Uber, for its part, is best known for its cheap taxi rides. But if the firm is worth an estimated $68bn, it is in part because it owns the biggest pool of data about supply (drivers) and demand (passengers) for personal transportation. Similarly, for most people Tesla is a maker of fancy electric cars. But its latest models collect mountains of data, which allow the firm to optimize its self-driving algorithms and then update the software accordingly. By the end of last year, the firm had gathered 1.3bn miles-worth of driving data—orders of magnitude more than Waymo, Alphabet’s self-driving-car division.
Non-tech firms are trying to sink digital wells, too. GE, for instance, has developed an “operating system for the industrial internet”, called Predix, to help customers control their machinery. Predix is also a data-collection system: it pools data from devices it is connected to, mixes these with other data, and then trains algorithms that can help improve the operations of a power plant, when to maintain a jet engine before it breaks down and the like.
3. Data networking effect: More users of a service mean more data generated, which means better product, which means more users. Sometimes companies even uses users to train algorithm processing that data (ex: Facebook facial recognition)
Why do companies buy an entire data generating companies instead of trading their data in an open market?
1. Data is in constant streams of supply, each destroys the relevance of the others.
2. There are too many types of data it’s hard to determine pricing of it (an attempt to quantify it is called economics). Each stream of information is different, in terms of timeliness, for example, or how complete it may be. This lack of “fungibility”, in economic lingo, makes it difficult for buyers to find a specific set of data and to put a price on it: the value of each sort is hard to compare with other data. There is a disincentive to trade as each side will worry that it is getting the short end of the stick.
3. Data is non-rivalrous (can easily be copied) so it can be used for uses other than initially agreed. All sorts of “transaction costs” on markets—searching for information, negotiating deals, enforcing contracts and so on—make it simpler and more efficient simply to bring these activities in-house. Likewise, it is often more profitable to generate and use data inside a company than to buy and sell them on an open market.
Who owns data? Is it users, companies, or the government?
It’s legally and technically complicated. Users has learned helplessness. They don’t know how much their data is worth and are too lazy to find out since are already habituated with the current way of life. This further disincentivises firms from paying to other people’s data.
The three pillars of data regulation policies: Antitrust, privacy, and social equality. Ex: Facebook and WA merger suspected to happen because Facebook *knows*, via data, that WhatsApp will be their major rival in the future.
Gov policy: centralized pile of data; mandatory data sharing, increases privacy risks since it can leak.