Data Pricing and the Economics of Information Markets

They say that knowledge is power.  But is it free?  The Internet has allowed not only the mass dissemination of data, but also the mass collection of data…including data that helps companies make revenue.  While a lot of general information is available for free on the Internet to consumers, ranging from daily news to YouTube videos to Wikipedia articles, information that has business value is often only available at a price.  

The Value of Data

Profit-seeking firms can use data to improve their processes and avoid going through trial-and-error, or waiting for potentially slow market forces, to maximize efficiency.  Because data can help lower production costs (by improving production efficiency) and/or increase revenue (by improving marketing and customer retention), it has significant value to firms.  Valuable data can be purchased from other companies that perform market research or production efficiency research.  Companies can also hire consulting firms to collect specific data that is tailored to their individual needs and specific industry profile.

Substitute and Complement Mastery

In addition to using data to improve its own products and marketing of them, firms can seek data on substitutes and complements in the market to improve their own product demand.  For example, a firm may be able to use marketing data on rivals’ substitutes to determine whether it should compete directly or shift its production elsewhere.  Is a rival’s substitute good favored by the same demographics?  Could advertising money be spent with a focus on other demographics to boost sales there?  In oligopoly markets, data on substitute goods can be crucial in helping use game theory to determine whether to compete directly in a market segment.

For complementary goods or services, a company can use data to determine whether the situation is ripe for an offer to bundle services with a complementary firm or attempt to purchase a complementary firm and offer both goods or services under one company.  Data may indicate that the situation is suitable for a merger between two firms so that they can combine production and offer bundles that will likely be in high demand.  Or, data could reveal that a proposed merger or bundle is likely to be inefficient or result in few additional sales, leading to the firm avoiding that decision and saving lots of money.

Data as an Excludable Good

For there to be substantial value for anything in a market, supply must be limited.  Data may be copied endlessly, especially in the computer era, but can be intentionally kept excludable to restrict its supply.  The excludability from data typically comes from it only being published and released selectively to paying customers.  To prevent this data from being shared to non-payers, creating the free rider problem, owners of the data are likely to only release it in specific formats tailored to the needs of the individual buyer.  Collectors and owners of data can also use various security methods to prevent unauthorized distribution of the data, such as making it time-sensitive or limit the number of allowed downloads.  If a buyer attempts to distribute the data to a third party, their account may be frozen and/or they may face legal ramifications.

Economic Challenges of Data Markets

Clearly, data has real value to profit-seeking firms.  But there are many challenges in terms of valuing data, which is not a physical factor of production and relies on being used properly.  In unskilled hands, additional data may do little.

Pricing Challenges of Intangible Assets

Unlike adding a new piece of factory equipment on a production floor, the change in production caused by utilizing a new data set can be difficult to quantify.  Similarly, it may be difficult to determine how much of an increase in sales is due to adjusting marketing techniques based on a data set, especially over a long period of time.  Are changes in revenue due to an overall increase in market demand, perhaps caused by a period of macroeconomic expansion?  Did the improvement in production efficiency come from using the new data sets, or did it come from some fortunate new hires?

While some firms may be willing to do deep data dives and determine how best to utilize data sets for which they have paid substantial sums, others may view purchasing data from consulting firms as a waste of funds.

Marginal Costs of Data and Consulting

Is purchasing a data set a singular event, or do firms need to subscribe to regular data updates?  Again, it can be difficult for some firms to compare the marginal costs and marginal productivity of incorporating a new data set.  Due to diminishing marginal returns, it may be difficult to justify subscribing to expensive consulting services, for which the improvements will progressively shrink over time.  Therefore, it can be difficult for consulting firms and other data analysis companies to remain in business: subscribers may be hard to come by, making revenue unpredictable.

Property Rights in Data Ownership

While demographic data and consumer data may largely be seen as free for the collecting, things become more complicated when data is collected on private companies.  When data is gathered on the operations of a firm, who owns that data - the collector or the firm?  What are the limits on being able to collect such data?  At some point, collecting data on private companies can cross into economic espionage, which is a crime.  Fears of economic espionage may lead firms to resist revealing any operational or production data.  It may also make firms suspicious about the activities of corporate consultants and potentially not trust offers of data analysis.

Barriers to Entry due to Asymmetric Information

In order to ensure that their data is protected, firms may focus on hiring data analysts and consultants who will accept non-disclosure agreements (NDAs).  This may weaken the market for information and instead lead to increased demand for experts who know how to analyze internal information.  If these experts are prevented by NDAs from working for other companies, it may create a barrier to entry for new firms in the industry.  Due to economic espionage fears, new firms may not be able to purchase data that can help them operate efficiently.  And, due to NDAs, consultants may not legally be available for hire by start-ups, limiting the ability of these firms to analyze their own data.