Economics of Data

As the digital age we’re living in advances, the weight of data as an asset becomes heavier and as a whole it’s turning out to be increasingly profitable for businesses to utilize correctly.

As an asset, data is exceptionally ambiguous and abstract, which makes its valuation both difficult and subjective. This complexity also extends to differently perceived usefulness of divergent data types, due to the variation in their quality and characteristics.

Is Data the New Oil?

There are many obvious distinctions between data and oil. The main difference is that data isn’t scarce whereas oil has to be extracted from the physical ground and is therefore a finite resource. However, the similarity between the two is the need for refinement before economic use becomes feasible.

Both data and oil need to be extracted before the process of refinement and usage. Oil must be physically drilled from the ground, whereas data must be derived from something else, such as human behavior. Oil can be turned into products such as fuel and plastics, whereas data can be turned into personalized user marketing or perhaps analytics to predict the future outcomes in the stock market. This potential for transformation is of note: neither of the resources have innately high value without the necessary technologies used for refinement.

Valuation of Data and Oil

The fiery race for black gold started in the 1800s as economies first started industrializing. The rush for digital gold in terms of data bloomed in the late 1990s as the global economy started transitioning from physical goods into more intangible products.

Data, when created, collected, stored, and analyzed, can be turned into economically relevant information –  comparable to the process of mining and refining crude oil. Significant investments are made to find new reserves of both crude oil and useful data. The patterns regarding investments in these assets can also be quite similar: one cannot predict where exactly new oil patches can be found nor know where economically useful data may be available. Hence investments in both can be highly uncertain in nature.

Data lacks the tangible and standardized quality of oil. Each dataset and observation is unique, influenced by the context and the time of its collection. The quality of a data source relies on its relevance, size, accuracy, and applicability, making it challenging to assign a fixed price per unit. In contrast, the consistent physical properties of oil and other similar natural resources produce a more straightforward valuation.

The distinct characteristics of data enhances its intricate role in modern economies, where its abstract nature requires innovative approaches to assess and leverage its worth.

The immateriality of data is what fundamentally differentiates it from oil. Oil exists as a physical asset with real substance. One barrel of crude oil has an exact value that’s constantly defined by the market, whereas data doesn’t have one globally established rate of exchange.

Demographic Factors Influencing Data Valuation

The utility of data in business can be divided into two key parts: predictive analytics and direct marketing, both of which can be either beneficial or detrimental for the economic success of the collector. Good data might sky-rocket the sales of a product whereas wrongly interpreted data might make it crash. It’s only to be expected that –  as the value of data isn’t fixed and its usability differs depending on the need – the price may also vary depending on whose data is at stake.

Let’s discuss marketing. Some of the main factors affecting the value of one’s personal data are age, gender, ethnicity and household income. One demographic radically standing out with higher data prices is found to be people aged 18-24. Data for individuals within this age bracket sells approximately triple the price when comparing against the ages 25-44. The lowest valuation is found to be for people aged 55 and higher, which correlates with potentially declining income and upcoming retirement.

Another interesting economic feature worth noting is how scarcity and supply affects data. When it comes to race, white individuals’ per person data are priced far lower than for black populations. By contrast, the total prices per population are higher for white individuals as their proportions of populations are larger and thus there simply exists more data and also potential customers for which this data can be used to better target.

Individual interests also play a role in the valuation of data. Someone interested in luxury-clothing is automatically seen as a bigger potential spender for most companies than the average user, who might spend more of their financial capacity on things such as interest payments on their loans. These types of spending habits also explain why the data of young people (19-24) has a relatively high price per person when compared to elders; they simply have fewer financial responsibilities and thus tend to be more reckless in their spending habits.

Takeaway

The awareness and quality of public discussion on data is crucial as its power and importance are constantly growing to be more significant for our society as a whole. By having ethically critical discourse on the subject we can make its collection, valuation, and usage reflect our ambitions and attitudes as well as possible.