HaxiTAG: Data Monetization

By leveraging Artificial Intelligence (AI) and Large Language Models (LLMs) on the foundation of data assetization and centralized storage, enterprises can achieve intelligent decision-making, automated business processes, and data-driven innovation. This enables them to build unique competitive advantages in the era of intelligence. The following discussion delves into how data integrates with AI and LLMs, core application scenarios, intelligent decision-making approaches, business automation, innovation pathways, and key challenges.

Integration of Data, AI, and Large Models

With centralized data storage, enterprises can utilize AI to extract deeper insights, conduct analysis, and make predictions to support the development of intelligent applications. Key integration methods include:

Intelligent Data Analysis
- Utilize Machine Learning (ML) and Deep Learning (DL) models to unlock data value, enhancing predictive and decision-making capabilities.
- Apply large models (such as GPT, BERT, Llama, etc.) for Natural Language Processing (NLP) to enable applications like intelligent customer service, smart search, and knowledge management.
Enhancing Large Model Capabilities with Data
- Enterprise-Specific Knowledge Base Construction: Fine-tune large models using historical enterprise data and industry insights to embed domain-specific expertise.
- Real-Time Data Integration: Combine large models with real-time data (e.g., market trends, user behavior, supply chain data) to improve forecasting accuracy.
Data-Driven Intelligent Application Development
- Convert structured and unstructured data (text, images, voice, video, etc.) into actionable insights via AI models to support enterprise-level intelligent application development.

Core Application Scenarios of AI and Large Models

Enterprises can leverage Data + AI + LLMs to build intelligent applications in the following scenarios:

(1) Intelligent Decision Support

Real-Time Data Analysis and Insights: Utilize large models to automatically analyze enterprise data and generate actionable business insights.
Intelligent Reporting and Forecasting: AI-powered data visualization reports, predicting trends such as sales forecasts and supply chain dynamics based on historical data.
Automated Strategy Optimization: Employ reinforcement learning and A/B testing to continuously refine pricing, inventory management, and resource allocation strategies.

(2) Smart Marketing and Customer Intelligence

Precision Marketing and Personalized Recommendations: Predict user needs with AI to deliver highly personalized marketing strategies, increasing conversion rates.
Intelligent Customer Service and Chatbots: AI-driven customer service systems provide 24/7 intelligent responses based on enterprise knowledge bases, reducing labor costs.
User Sentiment Analysis: NLP-based customer feedback analysis to detect emotions and enhance product and service experiences.

(3) Intelligent Supply Chain Management

Demand Forecasting and Inventory Optimization: AI combines market trends and historical data to predict product demand, optimizing inventory and reducing waste.
Logistics and Transportation Optimization: AI-driven route planning enhances logistics efficiency while minimizing costs.
Supply Chain Risk Management: AI-powered risk analysis improves supply chain security and reliability while reducing operational costs.

(4) Enterprise Automation

RPA (Robotic Process Automation) + AI: Automate repetitive tasks such as financial reporting, contract review, and order processing to improve efficiency.
Intelligent Financial Analysis: AI-driven financial data analysis automatically detects anomalies and predicts cash flow risks.

(5) Data-Driven Product Innovation

AI-Assisted Product Development: Analyze market data to predict product trends and optimize design.
Intelligent Content Generation: AI-powered generation of high-quality marketing content, including product descriptions, ad copy, and social media promotions.

How AI and Large Models Empower Enterprise Decision-Making

(1) Data-Driven Intelligent Recommendations

AI learns from historical data to automatically recommend optimal actions, such as refining marketing strategies or adjusting inventory.

(2) Large Models Enhancing Business Intelligence (BI)

Traditional BI tools often require complex data modeling and SQL queries. With AI and LLMs, users can query data using natural language, for example:
- Business and financial queries: "How did sales perform last quarter?"
- AI-generated analysis reports: "Sales increased by 10% last quarter, with a 15% growth in North America. Key driving factors include..."

(3) Intelligent Risk Management and Prediction

AI identifies patterns in historical data to predict risks such as credit defaults, financial fraud, and supply chain disruptions.

Business Automation and Intelligence

Enterprises can leverage AI and LLMs to construct intelligent business workflows, enabling:

End-to-End Process Optimization: Automate the entire workflow from data collection to decision execution, such as automated approval systems and intelligent contract management.
AI-Driven Knowledge Management: Transform internal documentation and historical insights into an intelligent knowledge base for efficient information retrieval.

How Data, AI, and Large Models Drive Enterprise Innovation

Enterprises can establish data intelligence-driven innovation capabilities through:

Building AI Experimentation Platforms
- Enable collaboration among data scientists, business analysts, and engineers for AI experimentation.
Developing Industry-Specific Large Models
- Train proprietary large models tailored to industry needs, such as AI assistants for finance, healthcare, and e-commerce.
Creating AI + Data Ecosystems
- Share AI capabilities with external partners via open APIs to facilitate data monetization.

Challenges and Risks

(1) Data Security and Privacy Compliance

AI models require access to vast datasets, necessitating strict compliance with regulations such as China’s Cybersecurity Law, Personal Information Protection Law, GDPR, and CCPA.
Implement techniques like data anonymization, federated learning, and access control to mitigate privacy risks.

(2) Data Quality and Model Bias

AI models rely on high-quality data; biased or erroneous data can lead to flawed decisions.
Enterprises must establish data quality management frameworks and continuously refine models.

(3) Technical Complexity and Implementation Barriers

AI and large model applications require substantial computational resources, leading to high infrastructure costs.
Enterprises must develop AI talent or collaborate with external AI service providers to lower the technical threshold.

Conclusion

Centralized data storage lays the foundation for AI and large model applications, enabling enterprises to build competitive advantages through data-driven decision-making, business automation, and product innovation. In the AI-powered future, enterprises can achieve greater efficiency in marketing, supply chain optimization, and automated operations while exploring new data monetization and AI ecosystem opportunities. However, successful implementation requires addressing challenges such as data security, model bias, and computational costs. A well-crafted AI strategy will be essential for maximizing business value from AI technologies.

The Rise and Opportunities of Data Businesses

Nearly two centuries ago, during the rapid expansion of American commerce, Lewis Tappan and John M. Bradstreet pioneered the concept of commercial credit reporting. In an era of limited information, they established firms dedicated to collecting, analyzing, and selling business data, laying the foundation for modern credit bureaus and risk assessment practices. Their innovative approach filled a critical gap in the burgeoning economy, enabling more informed lending and investment decisions.

Lewis Tappan and John M. Bradstreet demonstrated the potential of transforming data into profitable products. They established companies dedicated to collecting, analyzing, and selling data, filling a critical gap in the business world that urgently needed reliable credit assessment methods. Today, with the rapid advancement of technology, the opportunities for data businesses are even more extensive. According to McKinsey's latest survey, approximately 40% of business leaders expect to create data, analytics, and AI-based businesses within the next five years - the highest proportion among all new business categories.

Why is Now the Best Time to Build a Data Business?

Technological advancements have created favorable conditions for the rapid and cost-effective development of data businesses:

Enhanced Data Management Efficiency: Advanced data tools and technologies enable businesses to process, manage, and access real-time data more efficiently.
The Rise of Generative AI: Generative AI has significantly reduced the cost of processing unstructured data (such as text, images, and videos), making it easier to analyze and utilize.
The Proliferation of the Internet of Things (IoT): The decreasing cost of IoT technology allows businesses to collect and access real-world data faster and more economically.
Widespread Use of Internal Data Products: Leading enterprises increasingly treat data as internal products, laying the foundation for data monetization.

Evaluating Opportunities and Formulating the Right Strategy

The foundation of building a data business lies in having unique data of sufficient scale or possessing a distinctive method for processing data and extracting commercial value from it. Businesses can consider the following three broad strategies:

Creating Industry Standards: As Moody's, Standard & Poor's, and Fitch have done in the credit rating field. This strategy typically begins with large-scale aggregation of unique data and may eventually become an industry standard as network effects expand.
Leveraging Insights from Active User Groups: Transforming data collected from active user groups into valuable insights for advertisers, suppliers, partners, and users.
Converting Organizational Knowledge into Products: For example, evolving tools that solve internal business problems into profitable external products.

Key Considerations for Building a Sustainable Data Business

Defining a Strong Customer Value Proposition:
- Consider the type of "intelligence" provided by data products (from raw data to information, knowledge, and wisdom)
- Choose an appropriate product delivery model (data platform, insight platform, or intelligent application)
Adjusting the Operating Model:
- Incentivize growth potential rather than short-term profits
- Adopt new sales and pricing models
- Invest in specialized technical skills
Modernizing Data Technologies:
- Establish a robust data infrastructure
- Invest in core and advanced technical capabilities based on data types and delivery methods
Managing Data Security, Privacy, and Intellectual Property:
- Clarify data rights
- Develop consistent data privacy principles
- Pay attention to and comply with local laws
- Prioritize data governance and security

Building a data business requires not only unique datasets but also the right capabilities to scale products. First movers often gain significant advantages in capturing untapped market opportunities. However, successful data businesses can not only create scalable and profitable models but also potentially establish lasting brands. By following the guidelines provided in this article, businesses can better navigate the complexities of data businesses, transform raw data into actual profits, and secure advantageous positions in the digital economy era.

Menu

HaxiTAG

Your Trusted Partner for Intelligent Transformation and AI Industry Solutions

Get GenAI guide

Thursday, March 13, 2025

Integrating Data with AI and Large Models to Build Enterprise Intelligence