The recognition that data have value has been important in making it possible to collect data, process data, and manage with data ... but the downside of this has been that data and related analysis has been managed as intellectual property (IP) ... and this property then being exploited for its value to its owner rather than being used for public good.
The issue of the “public right to know” is not central to much debate ... and this has made it possible for public sector performance to be very low efficiency and nobody any the wiser. What a corporate organization tells the public is only a tiny amount of what the company knows ... and is carefully presented to send a message that is designed for the stakeholders, and not much related to the underlying data and knowledge.
The rule seems to be that only data that are required by law to be accessible to the pubic are going to be accessible ... everything else is going to be secret. More than anything else, this means that society will progress way more slowly than it would where data and analysis were being used to the optimum.
The argument that the value of IP produces an incentive to use data and innovate has some merit ... but so also does the argument that professionals and scientists are not only motivated by money, but also see value in discovery as a value beyond just its money value.
The TVM methodology is to have data and analysis as much as possible openly accessible. Data and analysis that might be useful for decision making are made openly accessible as rapidly as possible. The TVM approach that makes data and analysis easily accessible contrasts with the widespread practice of treating data about public matters as a proprietary private property. Data and analysis that might put people “at risk” are not openly accessible.
Open access is important, but so also is security. Security of data is a serious matter. In general data should be accessible for review and study, but not for people to change and manipulate in ways that will result in misinformation and incorrect analysis. As data becomes more important and more central to social performance and productivity, then the security of data takes on more and more importance.
Data security has many dimensions, all of which should be taken into consideration. These include (1) physical security; (2) disaster recovery; (3) hacking; (4) avoidance of misinformation; (5) theft of sensitive data; etc. Many people and organizations might seek to corrupt the data because good use of data will have the capacity to disrupt much profitable but inappropriate economic activity.
Sensitive information must be secure. Some information is quite sensitive, such as pay rates and benefit packages, and the like. Though they are sensitive, they are also important to understand since the cost of activities is very much a function of the cost of people and that is the cost of their remuneration and benefit packages.
The matter of privacy is complex, and no one answer is universally satisfactory.
At one level personal privacy is to be encouraged and respected … most people desire a high level of privacy in their private lives, and this is reasonable.
At the same time, people expect that society is safe and secure … and for this the “authorities” need to have access to information so that bad things are prevented from happening. Privacy gets in the way of efficient security operations.
People want convenience … but they do not want anyone and everyone to have access to open information.