ENTERPRISE AI ANALYSIS
Research on Evaluation and Optimization Path of Enterprise Digital Transformation Capabilities Driven by Big Data
Muge Zhang and Xinjuan Wang
Executive Impact Summary
This research develops a big data-driven model for evaluating and optimizing enterprise digital transformation capabilities. It integrates multi-source big data, uses an XGBoost ensemble learning model for accurate capability assessment across five dimensions (strategy, technology, data, organization, business), and leverages SHAP for interpretable attribution. The model identifies shortcomings and generates prioritized optimization paths, demonstrating superior accuracy (MSE 8.720, R2 0.871), diagnosis (0.884), and path coverage (0.852) compared to traditional methods.
8.720
Model MSE
0.871
Model R²
4.11%
Model MAPE
0.884
Diagnosis Accuracy
0.852
Path Coverage
Deep Analysis & Enterprise Applications
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Introduction & Context
Methodology Overview
Key Findings & Results
Practical Implications
Enterprise digital transformation is crucial for competitiveness, but existing assessment methods often lack real-world relevance and interpretability, hindering effective strategy formulation. This paper addresses these gaps by proposing a big data-driven assessment and optimization model.
The core methodology involves constructing a structured indicator system across five dimensions (strategy, technology, data, organization, business) and mapping them to quantifiable big data features. A high-dimensional dynamic feature matrix is generated, and a lightweight XGBoost model predicts capabilities. SHAP attribution identifies shortcomings, which are then matched with a knowledge base to generate prioritized optimization paths.
Experimental results demonstrate the model's superior performance with an MSE of 0.872, a digital transformation shortcoming diagnosis accuracy of 0.884, and a path coverage of 0.852. It significantly outperforms LightGBM, DNN, and AHP-Entropy models in accuracy, stability, and interpretability, providing scientific decision-making support for enterprises.
The proposed model enhances the scientific nature and interpretability of digital transformation assessments. By providing data-driven, actionable optimization paths, it enables enterprises to efficiently allocate resources, formulate targeted strategies, and achieve a closed-loop linkage between assessment and improvement. This method offers significant guiding value for real-world transformation practices.
8.720
Lowest MSE among compared models, indicating superior prediction accuracy.
Enterprise Digital Transformation Capability Assessment Process
Data Collection & Preprocessing
→
Feature Engineering (Lagged, Rolling, PCA)
→
XGBoost Model Training & Prediction
→
SHAP Attribution & Shortcoming Identification
→
Optimization Path Generation
| Model | MSE | R² | MAPE (%) | Key Strengths |
|---|---|---|---|---|
| This Article's Model | 8.720 | 0.871 | 4.11 |
|
| LightGBM | 12.353 | 0.845 | 5.62 |
|
| DNN | 18.471 | 0.782 | 7.35 |
|
| AHP-Entropy | 34.122 | 0.619 | 11.28 |
|
Real-world Application: Chinese A-share Listed Manufacturing Companies
The model was validated using public data from Chinese A-share listed manufacturing companies (2018-2023), encompassing 127 companies and 762 annual observations. The dataset integrated operational, environmental, and digital footprint data across five core dimensions. The XGBoost model, with hyperparameters tuned via 5-fold cross-validation and Bayesian optimization, demonstrated superior performance in capturing complex nonlinear relationships and generating actionable optimization paths, significantly outperforming traditional and other machine learning benchmarks in assessment accuracy and practical operability.
Companies Analyzed: 127
Data Span: 2018-2023
Observations: 762
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Your Personalized AI Implementation Roadmap
A structured approach to integrating AI into your digital transformation journey, ensuring measurable progress and sustainable growth.
Phase 1: Data Integration & Baseline Assessment
Gathering and integrating multi-source enterprise data, establishing baseline digital transformation capability scores across all five dimensions. Initial gap analysis.
Phase 2: Model Deployment & Iterative Refinement
Implementing the XGBoost-SHAP model, refining feature engineering, and validating early predictions. Setting up dynamic feedback loops.
Phase 3: Optimization Path Generation & Execution
Leveraging SHAP insights to identify key shortcomings and automatically generate prioritized, actionable optimization paths. Begin executing high-priority initiatives.
Phase 4: Continuous Monitoring & Strategic Adjustment
Ongoing monitoring of capability metrics, re-evaluation of optimization paths based on new data and feedback, and adapting strategies to evolving business needs.
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