Big data industry innovation service media
——Focus on data·Changing business
In the era of digital economy, data has become a new asset class, leading market changes and business decisions. However, in this new world driven by data, a problem that cannot be ignored is quietly emerging: data poverty.
The so-called data poverty refers to: Small and medium-sized enterprises have disadvantages in data compared to large enterprises. In the context of data assets being included in the balance sheet, this disadvantage may be further amplified, allowing small and medium-sized enterprises to compete with large enterprises in the market. at a disadvantage. As a result, in the era of digital economy, small and medium-sized enterprises have become increasingly marginalized and "impoverished".
As the trend of data capitalization continues to strengthen, this disadvantage has not only not been alleviated, but has been further amplified in financial statements. This phenomenon is not only related to the survival and development of a single enterprise, but also affects the health and balance of the entire economic system. Therefore, understanding and dealing with data poverty has become a topic that we urgently need to pay attention to.
Why does data poverty occur?
As data capitalization and data-driven decision-making become key factors in corporate competitiveness, the challenges faced by small businesses and individuals are increasingly highlighted.
First, let’s look at the issue of data acquisition costs. In the digital age, data has become the new gold, but the cost of acquiring these valuable resources is not cheap. For many small and medium-sized businesses and individuals, access to high-quality, large-scale data often comes with an expensive price tag. This includes the direct cost of purchasing data, as well as the indirect cost of investing in data collection and processing technology. Storing large amounts of data also requires expensive hardware facilities or cloud storage services.
At the same time, large enterprises and institutions, with their economies of scale and resource advantages, can more easily bear these costs and therefore have access to richer data resources.
Next is the difference between data processing and analysis capabilities. High-quality data analysis requires the support of professional knowledge and advanced technology. Small businesses and individuals often lack the necessary expertise and technical tools, or cannot afford the relevant training and technology investments. In contrast, large enterprises often have the ability to hire professional data scientists and use advanced data analysis tools and algorithms, which allows them to dig deeper and exploit data resources.
In addition, the ability to apply data is also a key factor. In a data-driven business environment, companies and individuals who effectively utilize data can more accurately understand market trends, optimize operational efficiency and improve customer experience, which gives them a clear competitive advantage.
Small businesses and individuals who fail to make effective use of data may be at a disadvantage in understanding market trends, developing strategies and improving services, making it difficult to compete with data-rich competitors.
Let's look at a typical example.
A small manufacturing company named "X" has a stable customer base and reliable quality products, but with the advent of the digital wave, it begins to face increasingly severe challenges.
Company X hopes to optimize its product lines and production processes by analyzing market trends and customer feedback. However, they soon discovered that obtaining this data was costly. Large data vendors are demanding fees that far exceed their budgets, and even if they can pay these fees, they lack the in-house capabilities to turn this data into valuable insights.
The team at Company At the same time, their large competitors not only have dedicated data science teams but also use advanced analytical software to optimize production and market strategies, which puts Company X at a clear disadvantage in understanding customer needs and market changes.
Due to the inability to effectively utilize data, Company
Company This has led to a continued decline in profits, further limiting their investment in data capabilities, creating a vicious cycle.
Data assets are included in the table, further exacerbating the problem of data poverty
As data capitalization becomes an important trend in modern enterprise management, its impact on the market competition landscape is becoming increasingly significant. Especially for small businesses and individuals, the process of data capitalization may exacerbate the data poverty problem they face. Here’s an in-depth look at the issue:
Impact on asset value determination
In the process of data capitalization, data is officially recognized as an asset and its value is clearly assessed and recorded in financial statements. This change is a huge advantage for companies with large amounts of high-quality data. It not only increases the total asset value of these enterprises, but also enhances their market reputation and credibility.
However, for small businesses with fewer data resources, this trend means that their disadvantages in asset value and financial strength are further exacerbated. Not only does this limit their growth potential, it can also make them less attractive in the eyes of customers and suppliers.
Inequality in investment and financing advantages
Data capitalization brings new opportunities for companies in financing and investment. Large companies that can capitalize data are in an advantageous position when attracting investment and obtaining financing. This is because data assets increase the total asset value of these businesses, making them appear healthier and more attractive on financial reports.
On the contrary, small businesses often face greater challenges in financing and investment due to their disadvantages in data assets. They often struggle to demonstrate strong financial strength or attract potential investors, which limits their ability to grow and expand.
Differences in market competitiveness
After data is capitalized, enterprises can use this data to optimize business decisions, improve operational efficiency and innovation capabilities. This significantly enhances the competitiveness of large enterprises with rich data assets in the market. They can more effectively predict market trends, improve products and services, and even develop new business models.
At the same time, small businesses and individuals who cannot capitalize data cannot show corresponding asset advantages on their financial statements. This not only puts them at a disadvantage in financing and investment, but also limits their ability to use data to drive business growth. . Their competitiveness in the market is relatively weak and it is difficult for them to compete with large enterprises with rich data.
In a classic example, we can observe how data capitalization exacerbates the problem of data poverty. Suppose there are two companies: a large data analytics company "A" and a mid-sized market research company "B".
A has huge data resources and financial strength, and can incorporate its data resources into financial statements as the company's core assets. This move enhances their market appeal, attracts more investment, and further expands their market influence.
On the contrary, Company B is unable to conduct similar data capitalization due to limited resources. It encountered obstacles in raising capital and expanding its business, making it difficult to compete with A. Over time, Company B found itself increasingly at a disadvantage in the data-driven market competition, and customers began to turn to Company A, which provided more in-depth analysis.
It should be pointed out that data poverty not only affects individual small and medium-sized enterprises, but also has a profound impact on more important industries and markets.
Data poverty takes on diverse forms across different industries, each with its own unique challenges.
For example, data poverty in the retail industry is mainly reflected in the understanding of consumer behavior and market trends. Smaller retailers may not be able to afford advanced analytics tools, leaving them unable to compete with larger retailers on inventory management, personalized marketing and pricing strategies.
Data poverty in the manufacturing industry is mainly related to production efficiency and product innovation. Small manufacturers may lack the data analysis capabilities required to optimize the production process, making it difficult to achieve cost control and product quality improvement.
Data poverty in the medical industry may hinder progress in disease prevention and treatment in smaller medical institutions, which may not be able to obtain or process enough patient data to develop personalized medical solutions.
From a broader market perspective,Data capitalization may lead to polarization in market competition and intensify market monopoly. By leveraging their vast data resources, large enterprises can further optimize their products and services and accelerate the pace of innovation. This can lead to market concentration trends, where market share is increasingly concentrated in the hands of a few large players. Small businesses may find themselves increasingly struggling to survive in this environment, especially in industries that rely heavily on data.
This kind of market concentration may inhibit competition in some areas, affect the diversity of consumer choices, and is not conducive to the innovative development of the market.
How to solve data poverty problem?
Since data poverty will cause serious problems, how to solve this problem?
Addressing data poverty, especially among small businesses and individuals, is a multi-dimensional challenge involving data acquisition, improved analytical capabilities, and the creation of a more equitable data ecosystem. Here are some specific strategies:
1. Promote the open sharing of data and broaden the ways for small and medium-sized enterprises to obtain data.
Small businesses and individuals often face barriers to obtaining high-quality data. A key strategy to address this problem is to establish public or industry data sharing platforms. Such platforms can enable small businesses and individuals to obtain the data they need at low cost or even for free. . For example, governments can open up public data such as demographics and economic indicators, while industry organizations can provide industry-specific market data.
Additionally, promoting open data initiatives is crucial. Governments and large businesses should be encouraged to share non-sensitive data, which will not only help the growth of small businesses but also drive innovation and development across entire industries.
Cooperation between small businesses, as well as partnerships between small businesses, large enterprises, and academic institutions are effective ways to promote data sharing and resource utilization. By establishing data cooperation alliances and networks, enterprises of different sizes can share data, technology and market insights to jointly improve data utilization efficiency.
2. Provide financial support and training to enhance the data application capabilities of small and medium-sized enterprises.
Funding is one of the main obstacles for small businesses in data processing and analysis. To this end, governments and relevant agencies can provide dedicated financial support to small businesses and startups to help them invest in data collection and analysis technologies. At the same time, government subsidies or tax incentives can also incentivize these companies to invest in data assets.
In addition to financial support, it is also important to provide data analysis and management training. Training courses organized by the government or industry associations can help small businesses and individuals improve their data processing capabilities to better utilize existing data resources.
3. Improve the policy and regulatory framework to limit the data monopoly behavior of large enterprises
Improving policy and regulatory frameworks is critical to addressing data poverty. Governments need to develop fair data management policies to ensure fair access and use of data by all market participants, especially small businesses and individuals. This includes preventing large companies from stifling competition through data monopolies and ensuring that they are transparent and fair in how they acquire and use data.
Policymakers should encourage data transparency and accessibility, for exampleimplementing mandatory data-sharing regulations that require dominant companies to share a certain percentage of data resources. Such regulations will not only help break up data monopolies, but also stimulate innovation and growth throughout the industry.
Policies should also protect the data privacy and security of consumers and small businesses, ensuring that data sharing is promoted without infringing on the privacy rights of individuals and businesses. Through these measures, a more balanced and healthy data ecosystem can be created for all market participants.
4. Promote the democratization of technology and lower the threshold of data technology
Promoting technology democratization is one of the key strategies to solve the problem of data poverty. The core of technology democratization is to make data analysis and processing technology more accessible and easier to use for enterprises of all sizes and individual users. This involves lowering the barriers to use of advanced data technologies so that even small businesses and individuals with less technical backgrounds can make effective use of data.
In the process of democratizing technology, self-service data analysis tools and platforms play an important role. These tools simplify the data processing and analysis process, allowing users to gain insights without having to delve into complex data science concepts. For example, through a graphical interface and drag-and-drop operations, users can easily complete data visualization and basic analysis tasks.
The development of large models makes human-computer interaction more natural and is expected to further lower the threshold for using data platforms. This has become a very important development direction in the big data industry.A large number of BI companies are accelerating the transformation of technology product systems and rushing to launch conversational BI products. Typical examples include NetEase Shufan’s ChatBI , Alibaba's Quick BI, Sematic's Smartbi, Kyligence's Copilot AI and other products.
In the RPA field, access to large models is expected to change the original drag-and-drop human-computer interaction method into a human-machine natural language dialogue method, which will further lower the threshold for using RPA products. For example, Shishi Intelligence, Jinzhiwei, Hongji, etc. have introduced AI, especially large model technology, into the RPA field, which has achieved remarkable results in improving the ease of use and intelligence of RPA products.
In addition, there are many companies that combine various fields with large models to launch digital employees, patented products, etc., such as360 applies large models to launch digital employee products, smart products, etc. Ya transforms the patented big data system based on large models.
Data Monkey has been following relevant fields for a long time, interviewed a large number of companies in related fields, and written a series of analysis articles to systematically introduce the latest developments in related fields. For details, see:
Conversational BI
Conversation is data analysis, NetEase Shufan ChatBI does it
AI large model+RPA
ChatGPT+RPA=? ——Conversation with ALBERT Lan Zhenzhong & Really Intelligent Sun Linjun
Digital workforce and other industry applications
Large model technology + R&D information library, Smart Ya will create a R&D version of ChatGPT
It can be seen that the development of artificial intelligence, especially large model technology, has provided a strong impetus for the democratization of technology. These technologies can automate many data processing processes, thereby reducing the need for specialized knowledge. For example, AI-driven predictive analytics tools can help users identify market trends and consumer behavior patterns without requiring them to program complex algorithms.
However, promoting democratization of technology also faces challenges, especially ensuring that the adoption of these technologies does not come at the expense of their quality and safety. Therefore, technology providers and policymakers need to work together to ensure the provision of data technologies that are both easy to use and secure, while also improving users’ data literacy through education and training so that they can use these tools more effectively. Through these efforts, democratizing technology promises to be a key way to close the data poverty gap.
Taken together, as we enter a new era driven by data, it becomes critical to address data poverty. By improving access to data, enhancing data processing capabilities, improving policy and regulatory frameworks, and democratizing technology, we can not only help small businesses and individuals overcome data challenges, but also create a more equitable and inclusive data ecosystem for society as a whole.
Looking forward to the future, we can look forward to a more balanced market in which the value of data is fairly accessed and utilized by all participants. Innovation is no longer the exclusive preserve of large enterprises. Small businesses and individuals can also participate in the ocean of data. Ride the wind and waves. This will not only promote the diversified development of the economy, but also stimulate the innovation potential of the whole society and push us into a more intelligent, efficient and connected future.
Text: Yi Liao Yanyu / Data Monkey
