Data Spectrum Elaboration, Facilitating the Onboarding of Data Assets into Balance Sheets

In the article on the interpretation of data spectrum, we have gained an initial understanding of the definition and connotation of the data spectrum. The data spectrum is an innovative visualization metric proposed in the context of data interoperability research, which measures the difficulty of transforming data from various industries into accounting data—a visualization metric that can also gauge the ease or difficulty of converting data between any two industries.

As illustrated in the following diagram, the data spectrum consists of multiple concentric circles. The rings formed between these circles are referred to as layers. Points distributed within these concentric circles represent industrial data. The central point of the diagram represents accounting data, with points indicating various industrial data positioned according to their increasing degree of difficulty in converting to accounting data, ranging from the innermost circle to the outer ones.

Data Spectrum Diagram

The core principle of the Data Spectrum is rooted in initiating from the group standards of various industries, where the total number of existing group standards per industry is tallied. From these, standards within the industry's group standards thatdefine data specifications are selected and collectively termed as 'data standards', while the rest are categorized as 'general standards'. By comprehensively assessing the level of standardization in data organization, encoding, and storage within an industry—considering both data standards and general standards—the ease or difficulty in transforming data from one industry to another is measured.

International Institute For Advanced Data Management Study defines data standards as those related standards that establish a system comprised of management systems, control processes, and technical tools, collectively regulating aspects such as data coding formats, data exchange methods, communication protocols, storage formats, and data collection methods.Data can take forms including electronic records, paper records, object records, among others.

Dividing an industry's group standards into data standards and general standards, different weights are assigned to these two categories. The industry's standard score is calculated using a weighted sum approach (number * weight), named the Data Spectrum Score. This score undergoes algorithmic processing to transform it into a spectral score value within the range of 0-1. When plotting the Data Spectrum graph, ten layers are demarcated with intervals of 0.1. It is defined that accounting data corresponds to a Data Spectrum processed value of 1, situating it at the center of the Data Spectrum. Other industry representative points are then positioned in their respective layers according to their industry’s spectral score processing value.

Forestry Data Wave Diagram

Data resources utilized by enterprises, meeting specified criteria, can be classified as intangible assets, with amortization of profits and losses occurring post-service delivery and revenue recognized accordingly; data resources sold, fulfilling set requirements, can be deemed as inventory, with profits and losses realized and revenue acknowledged upon sale. On the balance sheet, data resources are categorized under intangible assets, inventory, and development expenditures, with disclosures made in the notes accompanying the financial statements.

The inclusion of data on the balance sheet signifies its transformation from a natural resource into an economic asset. As the premier production factor in the digital economy era, data is poised to become a vital pillar supporting government and enterprise financial reports, as well as a significant source of revenue for fiscal purposes.

Pricing of data assets, or how they are converted into accounting data, constitutes a pivotal challenge in incorporating data assets into the balance sheet. Against this backdrop, the advent of interoperable Data Spectrum provides a reference for assessing the cost of data conversion and the difficulty thereof. By integrating cost values with transformation values, it aids in establishing reasonable prices for industrial data assets, thereby facilitating their entry onto the balance sheet.

The Data Spectrum Research Project is dedicated to providing a reference tool for the field of data interoperability, aligningwith the trends of the data element industry era. It plays a pivotal role in offering crucial evidence for the valuation of data assets in the process of incorporating them into financial statements.

Looking ahead, the Supreme Digital Academy will continue to keep a keen eye on cutting-edge research, delving into the forefront of contemporary data topics, and sharing the latest findings in data research with all.