About Data Economics

This site investigates the economics of data and the data economy.

Data is multifaceted, discoursivley volatile and hermeneutic in nature. Information derived from data is context dependent, subject to interpretation and negotiation.  Data fuels socio-economic ecosystems that are characterized by Circular Causal and Feedback Mechanisms among its agents. It requires and transforms institutional settings that process it, i.e. for economic purposes.

The good characteristics of data are hard to describe and even more difficult to protect. Data oscillates between extremes: from a singular digit to to collection thereof; from highly abstract to specifically concrete; from pervertly trivial to tremendously complex; from  ephemeral occurrences to hard boiled recordings.

Some thoughts and borrowed analogies about the good characteristics of data under constant progress …

Good characteristics examined: Volume

While it is intuitively appealing to think of data in aggregate, and to consider its volume in tera-, peta- or zettabytes, it pays to note that the value in such data is not a function of volume in any reliable sense. This is at odds with commodities such as iron ore, gold or crude oil, which can be measured directly in dollars per kilogram. [Source]

Good characteristics examined: Decay

Data has another unusual economic property: it remains. As an economic asset, it can be preserved at negligible cost, while other assets -premises, machines, staff, funds and lines of credit – may diminish in tough economic times. The potential value of the data is hard to measure as discussed above, but whatever it may be it grows relative to other shrinking assets. Thus, in hard times, the relative value of data grows, and the business case for Analytics, or Investing in Data grows. [Source]

Good characteristics examined: Scarcity

Allthough data is available on a massive scale, the level of scarcity increases with its specialization. Generating the sufficient amount of quality approved data to decrease the level of uncertainty and derive valid information from it can be a challenging, time- and cost-intensive task.

Good characteristics examined: Quality

According to IBM …

Data quality is an essential characteristic that determines the reliability of data for making decisions. High-quality data is:
– Complete: All relevant data —such as accounts, addresses and relationships for a given customer—is linked.
– Accurate: Common data problems like misspellings, typos, and random abbreviations have been cleaned up.
– Available: Required data is accessible on demand; users do not need to search manually for the information.
– Timely: Up-to-date information is readily available to support decisions. [Source]

Good characteristics examined: Extraction Method

Nor is the value extraction process homogeneous. While the way you extract value from one lump of iron or is no different than that applied to another, the same does not hold true for data. Further, the method of value extraction may well be unknown, and require further exploration. Even if a value extraction method has been identified, and indeed proven to work, there may well be additional value in the data. Finally, the value of data may well only prove itself in concert with other data. This is a synergistic, almost alchemical effect for which there is no good metaphor in the world of commodities. [Source]

Good characteristics examined: Analytics

Analytics, particularly in its “data mining” incarnation, is best described as investment in the extraction of value from this curious, heterogeneous, synergistic resource. The tools, skills, techniques and processes of Analytics ares all in the service of this investment enterprise. [Source]

Good characteristics examined: Skills

Data mining is very different, and the mining analogy fails spectacularly if it envisages a repetitive, well-defined activity, where large machines extract value in a predictable, reliable way, supported by interchangeable people performing repetitive tasks. Indeed, following the mining analogy, real data miners are less like miners and more like prospectors and geologists, performing difficult, ever-changing and highly skilled work to detect value when and how it may arise. [Source]