Leveraging Data, AI, and Large Models to Build Enterprise Intelligent Decision-Making and Applications

On the foundation of data assetization and centralized storage, enterprises can further integrate Artificial Intelligence (AI) and Large Language Models (LLM) to achieve intelligent decision-making, automated business processes, and data-driven innovation—thus establishing a unique competitive advantage in the era of intelligence. This article explores how data integrates with AI and large models, core application scenarios, intelligent decision-making methods, business automation, innovation pathways, and potential challenges in depth.

Integrating Data, AI, and Large Models

Once data is centrally stored, enterprises can leverage AI to conduct deep mining, analysis, and predictions, supporting the development of intelligent applications. The key approaches include:

1. Intelligent Data Analysis

• Using machine learning (ML) and deep learning (DL) models to extract data value, enhance predictive and decision-making capabilities.
• Applying large models (such as GPT, BERT, and Llama) in Natural Language Processing (NLP) to enable applications like intelligent customer service, smart search, and knowledge management.

2. Enhancing Large Models with Data

• Building enterprise-specific knowledge bases: Fine-tuning large models with historical enterprise data and industry insights to incorporate domain-specific expertise.
• Real-time data integration: Merging large models with real-time data (such as market trends, user behavior, and supply chain data) to enhance predictive capabilities.

3. Developing Data-Driven Intelligent Applications

• Transforming structured and unstructured data (text, images, voice, video) into actionable insights through AI models to support enterprise-level intelligent applications.

Core Application Scenarios of AI and Large Models

1. Intelligent Decision Support

• Real-time Data Analysis & Insights: AI models automatically analyze business data and generate actionable business decisions.
• Automated Reports & Forecasting: AI generates data visualization reports and forecasts future trends, such as sales projections and supply chain fluctuations.
• Automated Strategy Optimization: AI continuously refines pricing strategies, inventory management, and resource allocation through reinforcement learning and A/B testing.

2. Smart Marketing & Customer Intelligence

• Precision Marketing & Personalized Recommendations: AI predicts user needs, creating highly personalized marketing strategies to enhance conversion rates.
• AI-Powered Customer Service: Large model-driven chatbots and virtual assistants provide 24/7 intelligent Q&A based on enterprise knowledge bases, reducing manual workload.
• Sentiment Analysis: NLP technology analyzes customer feedback, identifying emotions to improve product and service experiences.

3. Intelligent Supply Chain Management

• Demand Forecasting & Inventory Optimization: AI integrates market trends and historical data to predict product demand, reducing waste.
• Smart Logistics & Transportation Scheduling: AI optimizes delivery routes to enhance logistics efficiency and reduce costs.
• Supply Chain Risk Management: AI assists in background checks, risk monitoring, and data analysis, improving supply chain security and resilience.

4. Enterprise Process Automation

• AI + RPA (Robotic Process Automation): AI automates repetitive tasks such as financial reporting, contract review, and order processing, enhancing business automation.
• Smart Financial Analytics: AI detects abnormal transactions and predicts cash flow risks through financial data analysis.

5. Data-Driven Product Innovation

• AI-Assisted Product Development: AI analyzes market data to forecast product trends and optimize product design.
• Intelligent Content Generation: AI generates high-quality marketing content, such as product descriptions, advertising copy, and social media content.

How AI and Large Models Enable Intelligent Decision-Making

1. Data-Driven Intelligent Recommendations

• AI learns from historical data to automatically suggest optimal actions to decision-makers, such as marketing strategy adjustments and inventory optimization.

2. Enhancing Business Intelligence (BI) with Large Models

• Traditional BI tools require complex data modeling and SQL queries. With AI, users can query data using natural language, such as:
  • Business and Financial Queries: "What was the sales performance last quarter?"
  • AI-Generated Reports: "Sales grew by 10% last quarter, with North America experiencing a 15% increase. The key drivers were..."

3. AI-Driven Risk Management & Forecasting

• AI detects patterns in historical data to predict credit risk, financial fraud, and supply chain disruptions.

Business Automation & Intelligence

AI and large models help enterprises automate business processes and optimize decision-making:

• End-to-End Intelligent Process Optimization: Automating everything from data collection to execution, such as automated approval systems and smart contract management.
• AI-Driven Knowledge Management: Transforming enterprise documents and historical knowledge into intelligent knowledge bases, allowing employees to access critical information efficiently.

How AI, Data, and Large Models Drive Enterprise Innovation

1. Establishing AI Experimentation Platforms

• Creating collaborative AI labs where data scientists, business analysts, and engineers can develop and test AI solutions.

2. Industry-Specific Large Models

• Training customized AI models tailored to specific industries (e.g., finance, healthcare, and e-commerce).

3. Building AI + Data Ecosystems

• Developing open APIs to share AI capabilities with external partners, enabling data commercialization.

Challenges and Risks

1. Data Security & Privacy Compliance

• AI models require access to large datasets, necessitating compliance with data protection regulations such as GDPR, CCPA, and China’s Cybersecurity Law.
• Implementing data masking, federated learning, and access controls to minimize privacy risks.

2. Data Quality & Model Bias

• AI models rely on high-quality data; biased or erroneous data may lead to incorrect decisions.
• Establishing data governance frameworks and continuously refining AI models is essential.

3. Technical Complexity & Deployment Challenges

• AI and large model applications demand significant computational power, posing high cost barriers.
• Enterprises must cultivate AI talent or collaborate with AI service providers to lower technical barriers.

Conclusion

Centralized data storage lays the foundation for AI and large model applications, allowing enterprises to leverage data-driven intelligent decision-making, business automation, and product innovation to gain a competitive edge. With AI enablement, enterprises can achieve efficient smart marketing, supply chain optimization, and automated operations, while also exploring data monetization and AI ecosystem development. However, businesses must carefully navigate challenges such as data security, model bias, and infrastructure costs, formulating a well-defined AI strategy to maximize the commercial value of AI.

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2025 AI Security Analysis and Insights

 The Evolution of AI Security Trends

With the widespread adoption of artificial intelligence, enterprises are facing increasingly prominent security risks, particularly those associated with DeepSeek. Research conducted by the HaxiTAG team indicates that the speed of AI adoption continues to accelerate, largely driven by advancements in technologies such as DeepSeek R1. While managed AI services are favored for their ease of deployment, the growing demand for data privacy and lifecycle control has led to a significant rise in enterprises opting for self-hosted AI models.

Key Security Challenges in Enterprise AI Adoption

Enterprises must focus on three critical areas when implementing AI solutions:

1. Data Security and Control

• As the core asset for AI training, data integrity and privacy are paramount.
• Organizations should implement stringent data encryption, access control, and compliance checks before AI deployment to prevent data breaches and unauthorized usage.

2. Proactive AI Security Governance

• Enterprises should establish AI asset discovery and cataloging systems to ensure that AI models, data, and their usage can be effectively tracked and monitored.
• Key governance measures include data provenance tracking, transparent reporting mechanisms, and clear accountability structures for AI usage.

3. AI Runtime Security

• The runtime phase presents a crucial opportunity for AI protection. While traditional cybersecurity measures can mitigate some risks, significant vulnerabilities remain in addressing AI-specific security threats.
• Threats such as model poisoning, adversarial attacks, and data exfiltration require specialized security architectures to counteract.

Current Market Landscape and Security Solutions

HaxiTAG's research categorizes existing AI security solutions into two primary groups:

1. Ensuring Secure AI Usage for Employees and Agents

• This category focuses on internal AI applications within enterprises, addressing risks related to data leakage, misuse, and regulatory compliance.
• Representative solutions include AI Identity and Access Management (AI IAM), AI usage auditing, and secure AI sandbox testing.

2. Safeguarding AI Product and Model Lifecycle Security

• These solutions prioritize AI supply chain security, as well as protection mechanisms for the training and inference phases of AI models.
• Core technologies in this domain include privacy-preserving computing, secure federated learning, model watermarking, and AI threat detection.

Industry Insights and Future Trends

1. AI Security Will Become a Core Pillar of Enterprise Digital Transformation

• In the future, AI adoption strategies will be deeply integrated with security frameworks, with Zero Trust AI security architectures likely to emerge as industry standards.

2. Acceleration of Autonomous and Controllable AI Ecosystems

• Rising concerns over data sovereignty and AI model autonomy will drive more enterprises toward privatized AI solutions and stricter data security management frameworks.

3. Growing Demand for Generative AI Security Governance

• As AIGC (AI-Generated Content) becomes more prevalent, addressing misinformation, bias, and misuse in AI-generated content will be a critical aspect of AI security governance.

AI security has become a fundamental pillar of enterprise AI adoption. From data security to runtime protection, enterprises must establish comprehensive AI security governance frameworks to ensure the integrity, transparency, and compliance of AI assets. HaxiTAG’s research further highlights the emergence of specialized AI security solutions, indicating that future industry developments will focus on closed-loop AI security management, enabling AI to create greater value within a trusted and secure environment.

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The Evolution of Enterprise AI Applications: Organizational Restructuring and Value Realization

— An In-Depth Analysis Based on McKinsey’s The State of AI: How Organizations Are Rewiring to Capture Value (March 12, 2025) and HaxiTAG’s Industry Applications

The Structural Shift in Enterprise AI Applications

By 2025, artificial intelligence (AI) has entered a phase of systemic integration within enterprises. Organizations are moving beyond isolated innovations and instead restructuring their operations to unlock AI’s full-scale value. McKinsey’s The State of AI report provides a comprehensive analysis of how companies are reshaping governance structures, optimizing workflows, and mitigating AI-related risks to maximize the potential of generative AI (Gen AI). HaxiTAG’s extensive work in enterprise decision intelligence, knowledge computation, and ESG (Environmental, Social, and Governance) intelligence reinforces a clear trend: AI’s true value lies not only in technological breakthroughs but in the reinvention of organizational intelligence.

From AI Algorithms and Technological Breakthroughs to Enterprise Value Realization

The report highlights that the fundamental challenge in enterprise AI adoption is not the technology itself, but how organizations can transform their structures to capture AI-driven profitability. HaxiTAG’s industry experience confirms this insight—delivering substantial Gen AI value requires strategic action across several key dimensions:

1. The Core Logic of AI Governance: Shifting from Technical Decision-Making to Executive Leadership

• McKinsey’s Insights: Research shows that enterprises where the CEO directly oversees AI governance report the highest impact of AI on EBIT (Earnings Before Interest and Taxes). This underscores the need to position AI as a top-level strategic imperative, rather than an isolated initiative within technical departments.
• HaxiTAG’s Practice: In deploying the ESGtank ESG Intelligence Platform and YueLi Knowledge Computation Engine, HaxiTAG has adopted a joint governance model involving the CIO, business executives, and AI experts to ensure that AI is seamlessly embedded into business operations, enabling large-scale industry intelligence.

2. Workflow Redesign: How Gen AI Reshapes Enterprise Operations

• McKinsey’s Data: 21% of enterprises have fundamentally restructured certain workflows, indicating that Gen AI is not just a tool upgrade—it is a disruptor of business models.
• HaxiTAG’s Cases:
  • Intelligent Knowledge Management: In the EiKM Enterprise Knowledge Management System, HaxiTAG has developed an automated knowledge flow framework powered by Gen AI, allowing organizations to build real-time knowledge repositories from multi-source data, thereby enhancing market research and compliance analysis.
  • AI-Optimized Supply Chain Finance: HaxiTAG’s intelligent credit assessment engine, leveraging multimodal AI analysis, enables dynamic risk evaluation and financing optimization, significantly improving enterprises’ capital turnover efficiency.

3. AI Talent and Capability Building: Addressing the Skills Gap

• McKinsey’s Observations: Over the next three years, enterprises will intensify efforts to train AI-related talent, particularly data scientists, AI ethics and compliance specialists, and AI product managers.
• HaxiTAG’s Initiatives:
  • Implementing an embedded AI learning model, where the YueLi Knowledge Computation Engine features an intelligent training system that enables employees to acquire AI skills in real business contexts.
  • Combining AI-driven mentoring with expert knowledge graphs, ensuring seamless integration of enterprise knowledge and AI competencies, facilitating the transition from skill gaps to AI empowerment.

Risk Governance and Trustworthy AI Frameworks in AI Applications

1. Trustworthiness and Risk Control in Generative AI

• McKinsey’s Data: The top concerns surrounding Gen AI adoption include inaccuracy, intellectual property infringement, data security, and decision-making transparency.
• HaxiTAG’s Response:
  • Deploying a multi-tiered knowledge computation and causal inference model to enhance explainability and accuracy of AI-generated content.
  • Integrating YueLi Knowledge Computation Engine (KGM) to combine symbolic logic with deep learning, reducing AI hallucinations and improving factual consistency.
  • Establishing a "Trustworthy AI + ESG Compliance Framework" in ESGtank’s ESG data analytics solutions to ensure regulatory compliance in sustainability assessments.

2. AI Governance Architectures: Centralized vs. Decentralized Models

• McKinsey’s Data: Key AI governance elements, such as risk management and data governance, are predominantly centralized, while AI talent and operational deployment follow a hybrid model.
• HaxiTAG’s Implementation:
  • ESGtank adopts a centralized AI ethics governance model (establishing an AI Ethics Committee) while embedding decentralized AI capability units within enterprises, allowing independent innovation while ensuring alignment with overarching compliance frameworks.
  • The HaxiTAG AI Middleware uses an API + microservices architecture, ensuring that various enterprise modules can efficiently utilize AI capabilities without falling into fragmented, siloed deployments.

AI-Driven Business Model Transformation

1. AI-Driven Revenue Growth: Unlocking Monetization Opportunities

• McKinsey’s Data: 47% of enterprises reported direct revenue growth from AI adoption in marketing and sales.
• HaxiTAG’s Cases:
  • Gen AI-Powered Smart Marketing: HaxiTAG has developed an A/B testing and multimodal content generation system, optimizing advertising performance and maximizing marketing ROI.
  • AI-Driven Financial Risk Solutions: In supply chain finance, HaxiTAG’s intelligent risk control models have increased SME financing success rates by 30%.

2. AI-Enabled Cost Reduction and Automation

• McKinsey’s Insights: In the second half of 2024, most enterprises reduced costs in IT, knowledge management, and HR through AI.
• HaxiTAG’s Implementations:
  • In AI-powered customer service, the AI knowledge management + human-AI collaboration model has reduced operational costs by 30% while enhancing customer satisfaction.
  • In ESG compliance, automated regulatory interpretation and report generation have cut compliance costs while improving audit quality.

Future Outlook: AI-Enabled Enterprise Transformation

1. AI Agents (Agentic AI): The Next Frontier of AI Innovation

McKinsey predicts that AI agents (Agentic AI) will emerge as the next major breakthrough in enterprise AI adoption by 2025. HaxiTAG’s strategic initiatives in this area include:

• Intelligent Knowledge Agents: The YueLi Knowledge Computation Engine is embedding AI agents leveraging LLMs + knowledge graphs to dynamically optimize enterprise knowledge assets.
• Automated Intelligent Decision-Making Systems: In supply chain finance and ESG analytics, AI agents autonomously analyze, infer, and execute complex tasks, advancing enterprises toward fully automated operations.
• HaxiTAG Bot Factory: A low-code editing platform for creating and running intelligent agent collaboration for enterprises based on private data and models, significantly reducing the threshold for enterprises' intelligent transformation.

2. The Ultimate Form of Industrial Intelligence

The ultimate goal of enterprise intelligence is not merely AI technology adoption, but the deep integration of AI as a cognitive engine that transforms organizational structures and decision-making processes. In the future, AI will evolve from being a mere execution tool to becoming a strategic partner, intelligent decision-maker, and value creator.

AI Inside: The Organizational Reinvention of the Era

McKinsey’s report emphasizes that AI’s true value lies in "rewiring organizations, not merely replacing human labor." HaxiTAG’s experience further validates this by highlighting four key enablers for AI-driven enterprise transformation:

1. Executive leadership in AI governance, ensuring AI is integral to corporate strategy.
2. Workflow reengineering, embedding AI deeply into operational frameworks.
3. Risk governance and trustworthy AI, securing AI’s reliability and regulatory compliance.
4. Business model innovation, leveraging AI to drive revenue growth and cost optimization.

In this era of digital transformation, only organizations that undertake comprehensive structural reinvention will unlock AI’s full potential.

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Challenges and Future of AI Search: Reliability Issues in Information Retrieval with LLM-Generated Search

 

Case Overview and Innovations

In recent years, AI-powered search (GenAI search) has emerged as a major innovation in information retrieval. Large language models (LLMs) integrate data and knowledge to facilitate Q&A and decision-making, representing a significant upgrade for search engines. However, challenges such as hallucinations and controllability modulation hinder their widespread reliable application. Tech giants like Google are actively exploring generative AI search to enhance competitiveness against products from OpenAI, Perplexity, and others.

A study conducted by the Tow Center for Digital Journalism at Columbia University analyzed the accuracy and consistency of eight GenAI search tools in news information retrieval. The results revealed that current systems still face severe issues in source citation, accurate responses, and the avoidance of erroneous content generation.

Application Scenarios and Performance Analysis

GenAI Search Application Scenarios

1. News Information Retrieval: Users seek AI-powered search tools to quickly access news reports, original article links, and key insights.

2. Decision Support: Businesses and individuals utilize LLMs for market research, industry trend analysis, and forecasting.

3. Knowledge-Based Q&A Systems: AI-driven solutions support specialized domains such as medicine, law, and engineering by providing intelligent responses based on extensive training data.

4. Customized general artificial intelligence experience: Improve the reliability and security of any generated artificial intelligence application by providing the most relevant paragraphs from unified enterprise content sources.

5. Chatbot & Virtual Assistant: Improve the relevance of your chatbot and virtual assistant answers, and make your user experience personalized and content-rich dialogue.

6. Internal knowledge management: Empower employees through personalized and accurate answers based on enterprise knowledge, reduce search time and improve productivity.

7. Customer-oriented support and case transfer: Provide accurate self-help answers based on support knowledge to minimize upgrades, reduce support costs and improve customer satisfaction.

Performance and Existing Challenges

• Inability to Reject Incorrect Answers: Research indicates that AI chatbots tend to provide speculative or incorrect responses rather than outright refusing to answer.

• Fabricated Citations and Invalid Links: LLM-generated URLs may be non-existent or even fabricated, making it difficult for users to verify information authenticity.

• Unstable Accuracy: According to the Tow Center's study, a test involving 1,600 news-based queries found high error rates. For instance, Perplexity had an error rate of 37%, while Grok 3's error rate reached a staggering 94%.

• Lack of Content Licensing Optimization: Even with licensing agreements between AI providers and news organizations, the issue of inaccurate AI-generated information persists.

The Future of AI Search: Enhancing Reliability and Intelligence

To address the challenges LLMs face in information retrieval, AI search reliability can be improved through the following approaches:

1. Enhancing Fact-Checking and Source Tracing Mechanisms: Leveraging knowledge graphs and trusted databases to improve AI search capabilities in accurately retrieving information from credible sources.

2. Introducing Explainability and Refusal Mechanisms: Implementing transparent models that enable LLMs to reject uncertain queries rather than generating misleading responses.

3. Optimizing Generative Search Citation Management: Refining LLM strategies for URL and citation generation to prevent invalid links and fabricated content, improving traceability.

4. Integrating Traditional Search Engine Strengths: Combining GenAI search with traditional index-based search to harness LLMs' natural language processing advantages while maintaining the precision of conventional search methods.

5. Domain-Specific Model Training: Fine-tuning AI models for specialized industries such as healthcare, law, and finance to mitigate hallucination issues and enhance application value in professional settings.

6. Improving Enterprise-Grade Reliability: In business environments, GenAI search must meet higher reliability and confidence thresholds. Following best practices from HaxiTAG, enterprises can adopt private deployment strategies, integrating domain-specific knowledge bases and trusted data sources to enhance AI search precision and controllability. Additionally, establishing AI evaluation and monitoring mechanisms ensures continuous system optimization and the timely correction of misinformation.

Conclusion

While GenAI search enhances information retrieval efficiency, it also exposes issues such as hallucinations, citation errors, and lack of controllability. By optimizing data source management, strengthening refusal mechanisms, integrating traditional search technologies, and implementing domain-specific training, AI search can significantly improve in reliability and intelligence. Moving forward, AI search development should focus on "trustworthiness, traceability, and precision" to achieve truly efficient and secure intelligent information retrieval.

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Integrating Data with AI and Large Models to Build Enterprise Intelligence

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:

1. 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.

2. 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.

3. 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:

1. Building AI Experimentation Platforms

   • Enable collaboration among data scientists, business analysts, and engineers for AI experimentation.

2. Developing Industry-Specific Large Models

   • Train proprietary large models tailored to industry needs, such as AI assistants for finance, healthcare, and e-commerce.

3. 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.

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Comprehensive Analysis of Data Assetization and Enterprise Data Asset Construction

Data has become one of the most critical assets for enterprises. Data assetization and centralized data storage are key pathways for digital transformation. Drawing on HaxiTAG’s enterprise services and practical experience in Data Intelligence solutions, this analysis explores the objectives, concepts, necessity, implementation methods and pathways, value and utility, as well as potential issues and risks associated with data assetization and centralized storage.

Objectives of Data Assetization and Centralized Data Storage

(1) Enhancing Data Value: Transforming "Burden" into "Asset"

• The core goal of data assetization is to ensure data is manageable, computable, and monetizable, enabling enterprises to leverage data for decision-making, business process optimization, and new value creation.

• Historically, data was often perceived as an operational burden due to high costs of storage, organization, and analysis, leading to inefficient data utilization. Data assetization transforms data into a core competitive advantage.

(2) Eliminating Data Silos and Achieving Unified Management

• Traditional enterprises often rely on decentralized data storage, where different departments manage data independently, leading to redundancy, inconsistent standards, and limited cross-departmental collaboration.

• Through centralized data storage, enterprises can construct a unified data view, ensuring data consistency and integrity to support precise decision-making.

(3) Strengthening Data-Driven Decision-Making

• Data assetization enables enterprises to achieve data-driven intelligence in areas such as precision marketing, intelligent recommendations, customer behavior analysis, and supply chain optimization, thereby enhancing business agility and competitiveness.

Concepts of Data Assetization and Centralized Data Storage

(1) Data as an Asset

• Data, like capital and labor, is a core production factor. Enterprises must manage data as they do financial assets, encompassing collection, cleansing, storage, analysis, operation, and monetization.

(2) Data Lifecycle Management

• The key to data assetization is lifecycle management, including:

  • Data Collection (standardized input, IoT data integration)

  • Data Governance (cleansing, standardization, compliance management)

  • Data Storage (structured and unstructured data management)

  • Data Computation (real-time processing, batch analysis)

  • Data Application (BI reporting, AI modeling, business strategy)

  • Data Monetization (internal value creation, data sharing, and trading)

(3) Centralized vs. Distributed Storage

• Centralized data storage does not imply all data resides in a single physical location but rather that:

  • Data lakes or data warehouses are used for unified management.

  • Data remains logically centralized while being physically distributed, leveraging cloud and edge computing for efficient data flow.

Necessity of Data Assetization and Centralized Data Storage

(1) Supporting Enterprise Data Governance

• Centralized storage allows enterprises to establish standardized data models, enhance governance, improve data quality, and reduce inconsistencies and redundancy.

(2) Enhancing Data Analysis and Application Capabilities

• Centralized storage provides a solid foundation for big data analytics, AI, and machine learning, accelerating enterprise intelligence.

(3) Strengthening Security and Compliance

• Dispersed data storage increases the risk of data breaches and compliance violations. Centralized storage facilitates access control, encrypted storage, and compliance auditing.

(4) Improving Data Sharing and Business Collaboration

• Centralized storage breaks down data silos between business departments and branches, enhancing efficiency. For example:

  • Marketing teams can access real-time user behavior data to improve precision marketing.

  • Supply chain management can optimize inventory in real time, reducing waste.

  • Customer service can leverage unified data views for better customer experiences.

Implementation Methods and Pathways for Data Assetization and Centralized Data Storage

(1) Establishing Data Standards and Governance Frameworks

• Define a data management architecture (e.g., Data Backbone, Data Lake, Data Warehouse).

• Set data standards (format specifications, metadata management, quality rules).

• Implement data access control mechanisms to ensure compliant data usage.

(2) Adopting Modern Data Storage Architectures

• Data Warehouses (DWH): Suitable for structured data analysis, such as business reports and financial data management (e.g., Snowflake, BigQuery).

• Data Lakes: Designed for storing structured, semi-structured, and unstructured data, supporting machine learning and big data analytics (e.g., Amazon S3, Databricks).

• Hybrid Storage Architectures: Combining data lakes and warehouses for both real-time processing and historical data analysis.

(3) Data Collection and Integration

• Utilize ETL (Extract, Transform, Load) or ELT (Extract, Load, Transform) tools for efficient data pipelines.

• Integrate multiple data sources, including CRM, ERP, IoT, and third-party data, to form a comprehensive data asset.

(4) Data-Driven Applications

• Precision Marketing: Personalized recommendations and ad targeting based on customer profiles.

• Intelligent Operations: IoT-driven equipment monitoring to enhance maintenance efficiency.

• Supply Chain Optimization: Real-time inventory and order tracking for procurement decision-making.

Value and Utility of Data Assetization and Centralized Data Storage

(1) Improving Data Utilization Efficiency

• Standardization and sharing reduce redundant storage and computations, optimizing data usage efficiency.

(2) Enhancing Enterprise Data Insights

• Advanced analytics and machine learning reveal hidden patterns, such as:

  • Customer churn prediction

  • Optimized product pricing

  • Market strategy adjustments

(3) Boosting Operational Efficiency and Automation

• Automated data workflows and intelligent analytics reduce manual data handling and improve operational efficiency.

(4) Enabling Data Monetization

• Enterprises can monetize data through data sharing, open APIs, and data trading, such as:

  • Banks leveraging user data for optimized financial product recommendations.

  • Retailers enhancing supply chain efficiency through data partnerships.

Enterprise Intelligence: The Integration of Data Assetization, Centralized Storage, and AI

Data assetization and centralized storage serve as the foundation for enterprise digitalization, eliminating data silos and enabling data-driven decision-making. By establishing data lakes and warehouses, enterprises can achieve efficient data management, analysis, and sharing, paving the way for intelligent applications.

With the integration of AI and Large Language Models (LLM), enterprises can unlock deeper data insights and drive business innovation. AI facilitates precision marketing, intelligent customer service, supply chain optimization, and financial analysis, enhancing automation and operational efficiency. LLMs, combined with real-time data, elevate decision-making capabilities, supporting automated BI analytics, intelligent risk control, and personalized recommendations.

However, enterprises must address data security, compliance, data quality, and technological costs to ensure AI applications are reliable. The future lies in building an ecosystem where AI and data converge, enabling intelligent decision-making, automated operations, and data-driven innovation, securing a competitive edge in the intelligent era.

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Unlocking the Full Potential of Data: HaxiTAG Data Intelligence Drives Enterprise Value Transformation

In an era where data-driven decision-making reigns supreme, enterprises are increasingly seeking more efficient ways to extract valuable insights from their vast data assets. According to IDC forecasts, by 2024, unstructured data—such as PDFs, emails, and large datasets—will account for 93% of all enterprise data. This trend underscores the critical importance of data management and intelligence, while the advent of Generative AI further accelerates the unlocking of data’s inherent value.

However, the true potential of data is often constrained by challenges such as data fragmentation, inconsistent quality, data silos, and inadequate governance. As Ritika Gunnar, General Manager of Data and AI at IBM, aptly stated: “Enterprises must first untangle the chaos of data.” To address these challenges, leading technology companies like Salesforce and IBM are intensifying efforts to develop advanced data intelligence solutions, empowering enterprises to achieve transformative, data-driven outcomes.

Data Intelligence: From Chaos to Value

Data intelligence serves as the foundation for modern enterprises to effectively manage and leverage data. It encompasses the entire process—from data cataloging, quality assurance, governance, and lineage tracking to data sharing. By establishing a unified intelligent data framework, enterprises can unlock the following benefits:

• Efficient Data Discovery and Organization: Automated cataloging and classification enable enterprises to quickly locate, understand, and utilize data.
• Improved Data Quality: Intelligent cleansing and validation mechanisms ensure data accuracy and consistency.
• Robust Data Governance and Compliance: Transparent lineage tracking and access controls ensure compliant data usage.
• Enhanced Data Sharing and Collaboration: Breaking down data silos fosters seamless cross-departmental collaboration, strengthening the data value chain.

HaxiTAG Data Intelligence Solution

As a dedicated innovator in the field of data intelligence, HaxiTAG is committed to building intelligent data pipelines that transform raw data into strategic assets capable of guiding business decisions. HaxiTAG Data Intelligence is a comprehensive suite of smart data tools focused on data management, operations, and standardization, designed to handle unstructured and semi-structured data with enterprise-grade governance and optimization.

What sets HaxiTAG apart is its seamless integration with AI, Large Language Models (LLMs), and business processes through a series of intelligent adapters. These adapters enable flexible, on-demand connections between data, AI capabilities, and business workflows, ensuring enterprises can fully harness their data potential in real time.

Key Advantages

• Full Lifecycle Data Management: Encompasses the entire closed-loop process of data collection, storage, processing, analysis, and visualization.
• Intelligent Processing of Unstructured Data: Offers advanced capabilities for parsing, structural transformation, and knowledge extraction from complex data types (e.g., PDFs and emails).
• Enhanced Search and Insight Generation: Leverages intelligent indexing and semantic analysis technologies for precise data retrieval and deep analytical insights.
• Scalable Enterprise-Grade Architecture: Compatible with mainstream cloud platforms and on-premises deployments, supporting high-concurrency and high-availability data computing needs.
• AI and LLM Integration via Adapters: Seamlessly connects data with AI and LLM functionalities to automate insights, enhance decision-making, and streamline business processes.

As data intelligence technology continues to evolve, enterprises stand at the forefront of overcoming long-standing barriers to data utilization, achieving a transformative leap from mere data accumulation to intelligence-driven value creation. HaxiTAG empowers enterprises with its cutting-edge data intelligence tools, helping partners unlock the full potential of their data and transform data assets into a true competitive advantage.

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