Read More《从实物资产到数据资产：数字化如何重新定义新时代企业价值》
Positive Comments: Digital Reconstruction of Enterprise Value: A Paradigm Shift from Physical Assets to Data Assets
In the industrial era, enterprise value was directly defined by physical assets such as steel, factories, and equipment. In the digital age, however, intangible assets like data, algorithms, and digital ecosystems are becoming the new core of value. This transformation is not only an upgrade of technological tools but also a fundamental reconstruction of the logic of enterprise value creation. Its positive significance is reflected in the following four dimensions.
I. The Economic Logic of Intangible Assets Taking Effect: A Leap from “Controlling Resources” to “Coordination Ability”
As mentioned in the news, the success of enterprises like Uber, Airbnb, and Netflix is essentially a practice of “software becoming the factory and data becoming the supply chain.” Traditional enterprises rely on the “possession” of physical assets (such as factories, fleets, and stores) to build competitive barriers, while digital enterprises create value by “coordinating” resources (such as connecting drivers with passengers, landlords with tenants, and content with users). The advantages of this model are as follows: Firstly, asset – light operation significantly reduces initial investment and fixed costs, allowing startups to expand rapidly in a short period (as mentioned in the news, “a fintech startup can develop an application in three weeks and reach one million users in ten days”). Secondly, the data – driven dynamic matching ability (such as Uber’s real – time order dispatch algorithm and Netflix’s content recommendation model) significantly improves resource utilization efficiency, and the “friction costs” in traditional industries (such as empty driving rates, hotel room vacancy rates, and DVD inventory backlogs) are greatly compressed. Thirdly, the network effect of the digital ecosystem (the more users, the richer the data, and the more accurate the service) forms a “positive flywheel,” shifting the moat of leading enterprises from “physical asset scale” to “data asset thickness.”
II. Intelligent Upgrade Driven by Digital Twin: An Evolution from “Experience – Based Decision – Making” to “Real – Time Optimization”
The popularization of digital twin technology (such as digital models that map the physical world in real – time) has shifted the definition of an enterprise’s “ability” from “skilled labor” to “data intelligence.” For example, in the manufacturing industry, sensors are used to collect data on equipment vibration and temperature, and combined with machine learning models to achieve predictive maintenance, avoiding the high costs of “repair after failure” in the traditional model. In the retail industry, smart shelves adjust replenishment strategies in real – time by detecting product consumption data, reducing inventory backlogs. The core of this transformation is to “transform the operating laws of the physical world into computable digital models,” enabling enterprises to preview the effects of decisions in a “simulated environment” and then feed the optimized solutions back into actual operations. The “digital thread” (an end – to – end traceable environment from concept to implementation) mentioned in the news is precisely an embodiment of this intelligent upgrade – enterprises no longer rely on managers’ experience and judgment but continuously optimize through data feedback loops, systematically improving the organization’s “cognitive ability.”
III. Organizational Cognitive Ability Built by Feedback Loops: A Qualitative Change from “Linear Processes” to “Organic Growth”
The deep – seated value of digitalization lies in transforming enterprises from “mechanical systems” to “organic systems.” The processes of traditional enterprises are linear (such as production → sales → after – sales), and there are delays in information transmission between different links, often resulting in decision – making lagging behind market changes. Digital enterprises, on the other hand, connect systems such as CRM, SCM, and finance through technologies such as APIs and microservices, forming a closed – loop of “perception – decision – action – feedback.” For example, an online consultation from a customer will trigger the recording of customer service data, which is then synchronized in real – time to the product department to optimize functions and drive the marketing department to adjust promotion strategies. This immediate linkage of the “nerve endings – central system – execution terminals” enables enterprises to “perceive environmental changes and respond quickly” like living organisms. The emphasis in the news on “delay is risk, and redundancy is flexibility” precisely reflects this logic – the ability to respond quickly to market demands has become the core competitiveness of digital enterprises.
IV. Adaptive Advantages of Dynamic Governance: A Breakthrough from “Controlling Management” to “Flexible Empowerment”
Facing the four forces of “novelty, volatility, disruption, and scope” brought about by digitalization, the governance models of forward – looking enterprises have shifted from “command and control” to “support and observation.” For example, through the “policy as code” technology, enterprises can automatically enforce security and compliance requirements without affecting development efficiency. Through the “federated governance” model, global branches can retain local innovation space while adhering to unified principles. Through the “organizational digital twin (DTO),” enterprises can preview the actual impact of strategic decisions in a virtual environment, reducing the cost of trial and error. The essence of this governance model is to “build a flexible architecture,” which not only avoids the rigidity of the traditional hierarchical system but also prevents the risks of disorderly innovation, enabling enterprises to “maintain direction and adjust flexibly” in a rapidly changing environment.
Negative Comments: Hidden Concerns in Digital Transformation: Challenges of Volatility, Dependence, and Fairness
Although digitalization has brought revolutionary opportunities for enterprise value creation, the potential risks behind it cannot be ignored. From technological dependence to organizational adaptation, from data security to the resource gap, the “dark side” of digital transformation needs to be rationally examined.
I. Dilemma of Organizational Adaptation under Rapid Technological Iteration: Cultural Lag and Competency Gap
As mentioned in the news, “the speed of change in the technological environment exceeds the speed of organizational culture adaptation,” and this contradiction is particularly prominent in practice. For example, after traditional manufacturing enterprises introduce industrial Internet platforms, front – line workers need to shift from “operating machines” to “analyzing data,” but most employees lack data thinking and skills. Managers are used to “experience – based decision – making” and are skeptical of new models such as algorithm – based dynamic pricing and intelligent production scheduling. This “cultural lag” may turn digital tools into “window – dressing” – a large amount of data is collected by the system but remains idle because the organization cannot digest it; AI models provide optimization suggestions, but they are ignored because managers do not trust them. More seriously, the “novelty” of technology requires enterprises to innovate continuously, but the traditional KPI assessment system (such as quarterly profit) conflicts with long – term technological investment, which may force enterprises to sacrifice digital strategies for short – term performance.
II. Decision – Making Risks of Data Dependence and Algorithm Black Boxes: Alienation from “Enhanced Intelligence” to “Substituted Judgment”
The core of digitalization is that “data becomes capital,” but over – dependence on data and algorithms may lead to decision – making biases. For example, if a retail enterprise’s dynamic pricing model is only based on historical sales data, it may ignore consumers’ perception of “price fairness,” causing user dissatisfaction. If the training data of a credit assessment algorithm in a fintech application is biased (such as gender or regional discrimination), it may magnify social unfairness. More importantly, the “black – box nature” of algorithms (such as the decision – making logic of deep – learning models is difficult to explain) may cause enterprises to lose control of their core capabilities – when algorithms become the “main actors in decision – making,” managers may gradually lose their understanding of the essence of the business and ultimately become “executors of algorithms” rather than “strategic planners.” Although the news mentions that “digital systems can show insights that humans never expected” is exciting, we also need to be vigilant about the ethical risks that “machine – dominated decision – making” may bring.
III. Conflict Costs between Legacy Systems and Digital Disruption: A Backlash from “Path Dependence” to “Transformation Shackles”
Taking Kodak and Blockbuster as examples, the news points out that “being excellent at things that the market no longer needs” is the greatest risk in the digital age. However, the “legacy systems” of traditional enterprises (including physical assets, organizational structures, and business processes) often become “invisible shackles” for transformation. For example, a manufacturing enterprise with a century – long history may have invested hundreds of millions of dollars in building an advanced production line. However, digital transformation requires connecting it to an industrial Internet platform, which means modifying the sensor interfaces of existing equipment,重构 production processes, and even adjusting the supply – chain cooperation model. This “conflict between old and new systems” not only incurs high transformation costs but may also be stalled due to the resistance of stakeholders (such as suppliers and distributors). Ironically, the more successful an enterprise has been in the past (such as having a large offline channel and a stable customer base), the greater the resistance to transformation may be – vested interests may oppose changes for fear of losing their existing benefits.
IV. Resource Gap for SMEs in Transformation: Aggravation from “Technological Inclusiveness” to the “Matthew Effect”
Although digitalization is regarded as a “weapon for disruptors” (such as startups can quickly enter the market through SaaS platforms), SMEs still face resource bottlenecks in transformation. As mentioned in the news, “a SaaS startup can disrupt an industry without real – estate,” but such enterprises often rely on venture capital and have technical teams and data accumulation. Most SMEs (such as traditional wholesale and retail and regional manufacturing) lack the funds to purchase digital tools (such as customized ERP systems and AI model training) and have difficulty attracting data talents (such as algorithm engineers and data analysts). More seriously, the “digital ecosystems” built by large enterprises based on their data advantages (such as user behavior data on e – commerce platforms and logistics data on supply – chain platforms) may form new monopolies. If SMEs want to access these ecosystems, they need to share their core business data with the platforms and may ultimately become “ecosystem appendages,” losing their independent pricing power and customer reach ability.
Suggestions for Entrepreneurs: Striking a Balance between “Breaking” and “Establishing” in the Digital Wave
Facing the opportunities and challenges brought by digitalization, entrepreneurs need to respond with “strategic determination + flexible adaptation.” The following are specific suggestions:
I. Prioritize Building “Data Asset” Capabilities Instead of Blindly Pursuing Technological Stacking
Data is the “oil” in the digital age, but the value of data lies in being “usable” rather than “large in quantity.” Entrepreneurs should focus on core business scenarios (such as customer demand insight and supply – chain optimization), clarify “what data is needed,” “how to collect it,” and “how to analyze it,” and avoid digitalization for the sake of digitalization (such as blindly deploying sensors without data application scenarios). For example, a catering startup can first collect data such as “user ordering preferences, peak – hour customer traffic, and ingredient consumption rates,” and optimize the food preparation volume and menu design through a simple data analysis model, rather than investing in an AI prediction system from the start.
II. Design a “Flexible Technological Architecture” to Cope with a Rapidly Changing Environment
Volatility is the norm in the digital age, and the technological architecture needs to have the characteristics of “scalability and replaceability.” It is recommended to adopt the “cloud – native + microservices” model: cloud services can scale computing resources on demand (such as automatically expanding during a surge in traffic during promotions), reducing fixed costs; microservices modularize business functions (such as splitting user management and order processing into independent services), avoiding the impact of a single module upgrade on the overall system. In addition, reserve “technology interfaces” (such as an open API platform) to facilitate the access of new technologies (such as large AI models and IoT devices) in the future and avoid being “locked in” by a single technology stack.
III. Promote “Organizational Culture Change” to Avoid “Advanced Technology but Backward Culture”
The key to digital transformation is the transformation of “people.” Entrepreneurs need to promote cultural adaptation in the following ways:
– Training and Empowerment: Provide employees with training on data thinking and the use of digital tools (such as Excel data analysis and basic BI tool operation) instead of relying solely on external technical teams.
– Pilot Projects in Small Steps: Select one or two business scenarios (such as customer service and inventory management) for digital pilot projects, and convince employees to accept the change with actual results (such as a 30% increase in efficiency and a 20% reduction in costs).
– Adjust the Assessment Mechanism: Incorporate “data application ability” into employees’ KPIs (such as sales teams need to formulate promotion strategies based on user behavior data), and incorporate “cross – departmental data collaboration” into departmental assessments to break down organizational silos.
IV. Be Vigilant about “Algorithm Dependence” and Retain the Core Position of “Human Judgment”
Data and algorithms are “decision – making tools,” not “decision – making substitutes.” Entrepreneurs need to establish a “human – machine collaboration” mechanism:
– Define Algorithm Boundaries: In decisions involving ethics, user privacy, and major strategies (such as pricing strategies and customer classification), retain the manual review process.
– Enhance Algorithm Transparency: Choose interpretable algorithm models (such as decision trees and logistic regression), or use “feature importance analysis” tools (such as SHAP values) to explain the algorithm’s decision – making basis to the team.
– Establish an “Anti – Factual Testing” Mechanism: Regularly verify the effectiveness of algorithms with historical data (such as “what would the result be if we made decisions based on manual experience?”), and avoid algorithm errors caused by data biases.
V. Explore “Lightweight Digital Paths” to Avoid the Resource Gap
SMEs can reduce transformation costs by “leveraging external resources”:
– Use SaaS Tools: Choose SaaS platforms that charge on a pay – as – you – go basis (such as collaboration tools on DingTalk and Feishu, and financial cloud services on Kingdee and UFIDA) to avoid large – scale one – time investment in customized systems.
– Access Industry Ecosystems: Join vertical digital platforms (such as Rootcloud in the manufacturing industry and Youzan in the retail industry) to share the platform’s technical capabilities (such as AI customer service and intelligent product selection), and pay attention to protecting core data (such as user contact information and supply – chain bottom prices).
– Collaborate with External Resources: Cooperate with universities and research institutions (such as entrusting data laboratories to analyze business data), or obtain technical support through government subsidy projects (such as support policies for digital transformation of SMEs).
In the digital wave, the definition of enterprise value has shifted from “how many physical assets are owned” to “how much intelligence can be created.” Entrepreneurs need to take “data as the foundation, flexibility as the principle, and people as the core” to seize opportunities while avoiding risks and gain an edge in this value reconstruction.
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