Information ≠ data
Information is not equal to data. Information refers to knowledge or data that is useful to people and can guide people's behavior, while data refers to raw numbers or data points that have not been processed and interpreted. Although information and data are similar in some ways, there are some important differences between them.
First, information has a purpose; it is usually related to some kind of decision or action. For example, if you're looking for a restaurant, you might search for relevant information such as menu, price, location, and reviews to help you make a decision. This information is useful and can help you make informed decisions.
In contrast, data are usually raw data points or numbers that have not been processed. For example, if you have a list of all restaurant menus, that list is the data. It may contain various information, such as each restaurant's dish name, price, type, etc., but you need to analyze and interpret it to get useful information from it.
So, while information and data are both related to data, there are some important differences between them. Information is knowledge or data that has a purpose and can guide people's behavior, while data is raw numbers or data points that have not been processed and interpreted. It can be concluded that information and data are not equal. It still takes a long way to turn scattered, original, and worthless data into systematic, usable, and valuable data. The most important thing is to do it well. Data governance makes data orderly, commercialized and compliant.
The pain and difficulty of valuing data
There are certain pain points and difficulties in the current digitization of information, which mainly include:
**1. Information storage is too small and does not meet the basic amount of data governance: **In the process of data collection During the process, due to insufficient data sources or low data quality, the amount of information may be insufficient and valuable data cannot be formed. At this time, data collection and data integration are needed to increase the diversity and richness of the data, so as to better explore the value in the data.
**2. Information storage is not interoperable, and data islands are serious: **In the process of informatization, due to the inconsistent data formats and standards between various systems and platforms, it may make it difficult for data to communicate and interconnect, forming data islands. . At this time, it is necessary to strengthen the concept of full data, integrate data from various systems and platforms, and realize data sharing and circulation, so as to better explore the value of data.
**3. Information storage is not standardized, and data governance is too difficult and costly: **In the process of data management, due to insufficient normativeness and standardization of data storage, it may be difficult to effectively utilize the data. For example, the recording time can be recorded as [2023-1-1] or [2023/01/01]. Different recording methods will affect data connection. At this time, it is necessary to form a concept of available data, and improve the availability and operability of data by formulating standardized data storage standards and management processes, so as to better explore the value in the data.
**4. Data security laws and regulations: **In the process of informatization, due to insufficient data security and privacy, problems such as data leakage and infringement may occur. At this time, it is necessary to strengthen the formulation and implementation of data security laws and regulations to ensure the security and privacy of data, so as to better explore the value of data.
These pain points are mainly concentrated in aspects such as too little information storage, non-interoperable information storage, non-standard information storage, and data security laws and regulations. In order to solve these problems, Databao manages enterprise data in stages through three-level governance. The first stage is the confirmation and registration of data resources, the second stage is the production and processing of data elements, and the third stage is the circulation and transaction of data products. Through the data governance of these three stages, a full, tradable, standard, and Secure data products with application scenarios.
With the release of the "Interim Provisions on Accounting Treatment Related to Enterprise Data Resources" (hereinafter referred to as the "Interim Provisions"), when preparing a balance sheet, enterprises should add data resource items based on the principle of importance and combined with the actual situation of the enterprise. That means Enterprises should take inventory and manage their data.
Xiao Bin, rotating CEO of Databao, believes: "After the release of the data asset inclusion policy, it is a great benefit for enterprises. Enterprises need to quickly sort out and evaluate the data resources they have. How to manage and operate the data assets they have is the current issue. A must-do homework for enterprises.”
Like physical assets, data assets also need to be inventoried and the necessary information recorded. As a data asset value-added operation service provider, Databao puts forward the "data snowball" theory for the path of data assets into the table. Databao believes that the entry of data resources into the table is a stage node of enterprise data capitalization, not the end point; secondly, it is necessary to establish a sustainable data asset operation and management system, and the goal and core is to achieve the "snowball" type of continuous value-added ability of data assets. ability.
The first step of data snowball - data asset inventory
Data asset inventory is the first step to enter data assets into the table. It is to classify all kinds of disordered raw data through inventory into ① data resources that meet the definition of assets and meet the conditions for asset recognition, embodied as intangible assets or inventory; ② that meet the requirements of assets Definition, but data resources that do not meet the asset recognition conditions and have not yet been "entered into the table"; ③ Other data resources.
Government and enterprise data are scattered in various heterogeneous systems and even business personnel's computers. The data structure, data type, storage form, sensitivity level, and importance vary. The whole thing looks like a mess of threads. How to take inventory and manage it? It's not easy to figure it out.
What should be included in data asset inventory? The scope of data inventory generally ranges from organizational scope, business scope, system scope, etc.
Based on different data sources and different division strategies, the content of the inventory will focus on basic data, derived data, and external data. In addition, data standards, data definitions, data classification, data attribution, data asset catalogs, etc. need to be constructed. After the "Interim Provisions" are implemented, more and more companies will begin to inventory and analyze the company's information, data, assets, etc., to determine which ones can be used for their own use, which ones can be used as transactions, and which ones are calculated. into the cost. With the start of "table entry" of data assets, in the future, whoever owns a large amount of data will be the biggest beneficiary, because these data will become his assets. At the same time, this opportunity is the "Generalized System of Preferences", which means that all corporate merchants can share this "cake". As a developer in the big data industry, Databao will help companies eat this piece of cake and create profits together.