Data valuation
Data valuation is a discipline in the field of accounting and information economics. It is concerned with methods to calculate the value of data collected, stored, analyzed and traded by organizations. History. In the 21st century, exponential increases in computing power and data storage capabilities (in line with Moore's law) have led to a proliferation of big data, machine learning and other data analysis techniques. Businesses increasingly adopt these techniques and technologies to pursue data-driven strategies and business models. Traditional accounting techniques used to value organizations were developed in an era before high volume data capture  and analysis became widespread, and focus on tangible assets (machinery, equipment, capital, property, materials etc.), ignoring data assets. As a result, accounting calculations often ignore data and leave its value off organizations' balance sheets. Notably, in the wake of the 9/11 attacks on the World Trade Center in 2001, a number of businesses lost significant amounts of data. They filed claims with their insurance companies for the value of information that was destroyed, but the insurance companies denied the claims, arguing that information did not count as property and therefore was not covered by their policies. Noticing this, a number  of organizations and individuals began to think and publish on the topic of data valuation. Doug Laney, vice president and distinguished analyst at Gartner, conducted research on Wall Street valued companies which found that companies which had become information-centric, treating data as an asset, often had market to book values two to three times higher than the norm. On the topic, Laney has commented: "Even as we are in the midst of the Information Age, information simply is not valued by those in the valuation business. However, we believe that, over the next several years, those in the business  of valuing corporate investments, including equity analysts, will be compelled to consider a company's wealth of information in properly valuing the company itself." In the latter part of the 2010s, the list of most valuable firms in the world (a list traditionally dominated by oil and energy companies) was dominated by data firms – Microsoft, Alphabet, Apple, Amazon and Facebook. Towards the end of the decade, data valuation has received widespread recognition by accounting firms. Trends driving this include organizations starting to understand the value of being data driven, organizations getting requests from others to buy or use their data , organizations looking for new revenue options from digital business models and internet technologies, technologies improving and costs dropping, and organizations starting to fear disruption from major data-driven competitors. Characteristics of data as an asset. A 2020 study by the Nuffield Institute at Cambridge University, UK divided the characteristics of data into two categories, economic characteristics and informational characteristics. Data value drivers. A number of drivers affect the extent to which future economic benefits can be derived from data. Some drivers relate to data quality, while others may either render the data valueless or create unique and valuable competitive advantages  for data owners. The process of realizing value from data can be subdivided into a number of key stages: data assessment, where the current states and uses of data are mapped; data valuation, where data value is measured; data investment, where capital is spent to improve processes, governance and technologies underlying data; data utilization, where data is used in business initiatives; and data reflection, where the previous stages are reviewed and new ideas and improvements are suggested. Methods for valuing data. Due to the wide range of potential datasets and use cases, as well as the relative infancy of data  valuation, there are no simple or universally agreed upon methods. High option value and externalities mean data value may fluctuate unpredictably, and seemingly worthless data may suddenly become extremely valuable at an unspecified future date. Nonetheless, a number of methods have been proposed for calculating or estimating data value. Infonomics valuation models. Doug Laney identifies six approaches for valuing data, dividing these into two categories: foundational models and financial models. Foundational models assign a relative, informational value to data, where financial models assign an absolute, economic value. Bennett institute valuations. Research by the Bennett Institute divides approaches for estimating the  value of data into market-based valuations and non market-based valuations.