In the digital age, data has become an important asset, and businesses and individuals alike want to monetize it. But not all data is an asset, and how to become an asset is a concern for enterprises today. Let's take a look at the three key steps of enterprise data assetization: data resourceization, resource productization, and product value.
First of all, there is a consensus in the industry to define data assets, that is, "data assets refer to the identifiable forms owned or controlled by enterprises, which are expected to bring sustainable economic benefits to enterprises, and have data as the main content and services". "Owned or controlled by the enterprise" means that the enterprise must have three rights to the data assets. "It is expected to bring sustainable economic benefits to the enterprise", in fact, it will be divided into internal value and external benefits, the internal value is self-produced and sold for self-use, and the external value is self-produced and sold to others. "The identifiable form with data as the main content and service" means that the data product should be confirmed as the form of the data asset, so the data product has become a core element of the data asset confirmation.
Therefore, the data generated by the enterprise itself has the right to hold data resources, and the data products that can be used continuously are listed as data assetsData that is purchased, shared, crawled, or authorized by an enterprise has the right to be processed and used, and a data product that is used sustainably is listed as an asset, but if it is not a data product that can be used, it cannot be listed as a data asset.
Technically, the generation path of data element value can be divided into three key stages: data resourceization, resource product and product value.
A three-step path to the implementation of enterprise data assets.
A data resource refers to a dataset from different sources that is physically aggregated according to a certain logic and reaches a "certain scale", and has a reusable, applicable, and obtainable data collection.
What is data recycling: the process in which an enterprise or institution processes and sorts, collects, and stores the raw data directly or indirectly obtained or collected to form data resources.
Enterprise data resource stage: Under the guidance of the enterprise data strategy, it is necessary to build its data capability system and establish an enterprise data governance system, so as to form talents, technologies, organizational arrangements and systems that are compatible with the data-driven business model within the enterprise.
For example, Shanghai Pudong Development Bank is a joint-stock bank headquartered in Shanghai, and it urgently needs to turn a lot of messy data into data resourcesThe first thing to do is to put forward a digital technology plan in the 14th Five-Year Plan, with the overall goal of improving the bank's comprehensive capabilities, and data assetization is only one of its comprehensive capabilities. Based on DCMM's more specific and practical data strategic planning in 2021, the next step is to carry out data recycling.
First of all, it is necessary to sort out the data**, and the Shanghai Pudong Development Bank first divides the data** into internal data and external data. Internal data includes nearly 2,000 systems established by SPD Bank to collect raw data, which can then be analyzed and classified to form a data catalog. Industrial and commercial data, credit investigation data, court data, etc. imported from external data. After sorting out the data, we sorted out and collected the data, and then built a data center according to customer needs and management needs, forming data resources. In the end, the scale of data resources of the whole bank has reached 2757 PB, including structured data, unstructured data, and external data.
Resource productization refers to the process in which the data resource holder organizes itself or effectively authorizes external institutions, guided by the needs of data users, to make substantial labor input and creation of data resources, and forms an identifiable service form with data as the main content that can serve internal and external users. That is, the data resources with a certain scale and a certain value are developed according to some needs and goals of specific situations to form data products.
There are three major points in the productization process of data resources: it is necessary to analyze customer needs and scenarios, and it is also necessary to find a customer to jointly develop according to clear demand scenarios, and then in the development process, it is also necessary to make some service terminals.
1.Features of data products:
For any enterprise to make good use of data, it must develop data products. In popular understanding, data products are roughly equal to data resources + data algorithm models + service terminals. What are the characteristics of data products, which can be covered in five words:
First, content, data products need to contain the developed data resources, and these resources and content are truly usable and clearly belonged.
Second: Delivery, just like other products, data products are essentially a product, in order to sell it, it is necessary to provide a certain service terminal for his needs.
Third: demand, data products should be produced with clear application scenarios to meet the data needs of users.
Fourth: supply, the purpose of data products is to supply data or knowledge, not a one-time transaction. The sale of data products is not to sell the ownership of a data product, not to let consumers or enterprises directly buy out the data products, but to provide consumers with a steady stream of sustainable services.
Fifth: use, data products are ultimately used, and it needs to participate in production and business activities to play value.
2.The form of the data product.
We can make a matrix division of the form of data products according to the demand characteristics and service characteristics.
Requirements characteristics include both modular and non-modular requirements. If consumers want to use data products to improve their own models and algorithms, it is called model-based demand. Consumers just want to get specific information about data products, for example, to check whether the target cooperative enterprise has dishonest personnel, lawsuits, etc., and only need to focus on conclusions and decision-making information, which is called non-model demand.
There are also two types of service methods for product providers, interface and non-interface methods.
Interface class: An interface class is usually a type of software that contains a collection of instructions, data, or programs that are used to perform a specific task. The interface for active operation of users mainly includes query interface, software, SaaS and other applications.
Non-interface: the use of the functions of a program (such as an operating system, library), and the way in which programs interact with each other, such as APIs, file delivery, controlled sandboxes, federated Xi, etc.
Through the demand characteristics and service methods, the entire product form can be divided into three forms, including data set, data information service, and data application.
3.Billing method of the data product.
Data products are ultimately to be sold, and the billing method for data products is as follows:
According to the data assetization strategy, data products can be circulated in the form of self-use, sharing, openness, and external transactions, among which the value of tradable data products can be reflected through trading contracts. The valorization of data products is the process of continuously serving the business decisions of internal and external users, so as to bring sustainable economic benefits to the enterprise.
The process by which a data product exerts value is called a prophetic model seer model
The first step to value capture:
In order to support the daily production and operation decision-making of enterprises, a series of applications (self-usage) have been developed, such as supporting precision marketing, potential customer discovery, product recommendation, inventory optimization, process path optimization and other machine Xi models or optimization models based on data products, supporting enterprises to improve operational efficiency, decision-making level or form new digital businesses, and empower enterprises to digitally transform.
The second step of value capture:
Once an enterprise operates based on data-driven, it will find that its own data is insufficient, and it will need to seek suitable external data and obtain data products provided by external agencies through purchase or sharing. After the internal and external data products are integrated, new data products are developed according to the needs of internal production and operation to achieve data engagement.
The third step of value capture:
Enterprises open or share data products with ecological partners (ecologicalization), and open or share them with partners for further mining and acquisition of data value.
The fourth step of value capture:
Enterprises further develop data products and innovate their business to form tradable data products (reinvention), sell data products through a data element trading market, and form a data business for external services, so as to obtain reasonable sales revenue.
Data resourceization: It means that the enterprise will process and process the data of different ** through the necessary processing, integration and processing, and physically collect it according to a certain logic to reach a "certain scale" to form a reusable, applitable, and obtainable data collection. This stage requires a data strategy planning method, a data capability system, and an enterprise data governance system.
Data productization: Enterprises conduct research and development of data products guided by the needs of data users through their own organization or effective authorization to external institutions. This stage includes establishing a possible application value map of data resources, analyzing the data requirements and application scenarios of target customers, selecting appropriate test customers, and jointly organizing data products and their technology development.
Product value: Data products can be bought and sold in the data element trading market, which can bring continuous benefits. At this stage, it is necessary to establish a data asset strategy, build a data asset management system, and realize the operation and management of data assets.
Finally, the value monetization capitalization reflects that after the data becomes an asset, there are some asset games like other assets, such as pledge financing, IPO asset evaluation, mergers and acquisitions, and entry into the table.
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