Semantic flaws fragment data commons. Failed provision and incentivization of data commons or inefficiently regulated privacy all delegitimize the commons. Vulnerable data commons expose privacy regulation to threats, as well as corruption. This is how the state of your data commons leaks your agenda.

Governed data is not necessarily more robust to failure than traded data is. Common data can be inefficiently utilized, incentivized, regulated, or provided. Because of these inefficiencies, fusing and integrating common data resource can fail. Whenever this happens, data governance fails, too.

Whereas markets succeed or fail for different reasons, markets for data assets succeed or fail because of information asymmetry. Regulation and other interventions can address market failure. However, such interventions can fail, too. What privacy regulation can secure efficiency of traded data assets?

Markets can price data assets. However, if a specific market is characterized by weak efficiency then that market cannot price proprietary information. Alternatively, data equity is a call option on real data assets with a strike price that is the price of debt associated with the assets. Should you leverage your data assets? How?

Efficient pricing causes you to be indifferent between data asset sale and acquisition. In fact, there may be no data asset to trade, because too efficient a market trumps your going concern for proprietary information. Alternatively, the market may fail you, because the trade is too costly. How then to put a price tag on your data asset?

Imagine a riddle. If you solve it, the price of your data is the cost of expended computing resources. If you can’t solve it, this price is the amount you would pay to learn the solution. This price can also be calculated as the present value of your discounted future earnings, caused by legitimately accessing the solution. With efficiency, the three values coincide.

Our capability rests on the relationship between counterfactuals and data assets. Counterfactuals impose trade-offs among causality, strategy, and confidentiality onto data assets. These notions also bound counterfactuals informationally. This relationship underpins our capability to efficiently transform, restructure, regulate, and secure data assets.

Our science foundation nurtures, stewards, and promotes open-source intelligence and operations security solutions for business and policy communities. Our current topics focus on semantic consensus and capital structures with secure and regulated data assets. Contact us for more!

Our services can help you to spur growth of your resource base. We can get everyone involved as much value as possible. We do this, by showing you how to consume, manage, pool, and fund your data resource. What binds our community together is giving fidelity to number!