Summary

This case covers a competitive intelligence network system built for a research-oriented organization. The project was not a simple portal site or a conventional content management system. It combined public information collection, intelligent text processing, information presentation, back-office maintenance, expert-assisted quantitative analysis, and competitive strategy research support into one integrated platform.

The delivery approach focused on requirements modeling, subsystem decomposition, test-case validation, and training before full handover. Work that had previously depended on manual searching, manual整理, and manual analysis was translated into system capabilities: collectable data, processable content, searchable results, reusable models, and archived research outputs. The system delivered five major functional domains. Testing covered many functional points across collection, processing, presentation, administration, expert analysis, and strategy analysis, with the main items passing validation. Trial operation reported stable system performance and no major failures, providing a sound basis for acceptance and operational use.

Project Background

Before the project, the research workflow faced three practical constraints. Public information sources were dispersed, making manual collection expensive and inefficient. Structured data, documents, images, and research outputs lacked a unified organization model. Research projects also had difficulty reusing models, expert input, analysis workflows, and archived outputs across topics.

The project goal was to create a platform that connected data collection, information processing, analytical workflows, and research output publication. The scope can be summarized into five capability areas:

• Public information collection and intelligent processing: collecting information from websites, columns, search results, forums, blogs, e-commerce pages, and attachments, then applyingsummary generation, keyword extraction, classification, clustering, entity recognition, sentiment analysis, and full-text search.
• Information presentation and interactive service: publishing intelligence content, analysis results, statistical charts, and research reports, with support for full-text search, anonymous access, account login, and permission-based access control.
• Back-office business maintenance: supporting manual intelligence editing, industry data maintenance, statistical analysis, site column management, user permissions, and system configuration.
• Expert-assisted quantitative analysis: supporting online collaboration around AHP, Delphi, benchmarking, SWOT, fuzzy evaluation, and other qualitative-to-quantitative analysis methods.
• Strategic analysis workflow support: using workflows, models, indicator systems, report templates, and archiving mechanisms to support structured research outputs.

Main Challenges

1. The Requirements Were Abstract and Could Not Be Managed as a Feature List

The requirements included software functions such as information collection, text processing, data maintenance, and content presentation, but also business methods such as research workflows, analysis models, expert collaboration, and output archiving. If managed only as menus and pages, the project could have produced many functions without a coherent research workflow.

2. Multiple Data Types Required Early Data Modeling

The system had to handle structured data, unstructured documents, images, attachments, statistical charts, and research reports. These data types also needed to be connected through industry chains, institutions, enterprises, products, technologies, experts, and topics. If the data model was unstable, development and testing would have become repetitive and fragile.

3. Collection and Intelligent Processing Required End-to-End Testing

Information collection was not simply page access. The system had to support different site types, page structures, collection rules, and text-processing methods. Testing therefore had to cover the full chain: collection task configuration, page crawling, body text identification, duplicate removal, keyword extraction, summarization, classification, clustering, and full-text search.

4. Training Needed to Introduce Both Methods and System Operations

Users needed more than button-level system training. They also had to understand competitive intelligence methods, analytical models, expert collaboration processes, and how these methods were reflected in the platform. Without method training, the system would have remained a tool rather than a research capability.

Management Approach: Translate Research Methods into Delivery Conditions

1. Break Complex Requirements into Capability Domains

The project team first divided the requirements into five capability domains: information collection and processing, information service and presentation, back-office maintenance, expert-assisted analysis, and strategic research support. Each domain was then mapped to functions, data objects, test cases, and training content.

This changed the management focus from “how many pages are finished” to “which research capabilities are ready.” The final deliverable was not only a set of interfaces, but a toolchain supporting information collection, analysis, publication, and knowledge accumulation.

2. Stabilize the Data Model Before Driving Feature Development

The project included many data objects: enterprises and institutions, products and services, experts and talent, technical literature, classification dimensions, indicator systems, and research outputs. Data fields, classification standards, relationships, and permission boundaries had to be controlled early.

This allowed collected data, manually maintained data, analytical data, and published outputs to flow through one platform, reducing the risk of separate front-end presentation, back-office maintenance, and analytical models drifting apart.

3. Use Test Cases to Validate the Real Work Chain

Testing covered public information collection, intelligent processing, information presentation, back-office maintenance, expert-assisted analysis, and strategic research support. The test report listed many functional points, and the main test items passed validation.

The value of testing was not to prove that individual pages opened. It was to validate the work chain: information could be collected, body text could be identified, results could be classified and searched, data could be maintained, expert input could feed into models, and analysis results could be turned into reports and archived outputs.

4. Use Trial Operation to Verify Stability and User Fit

Trial operation materials reported that the system operated normally, had no major failures, and met the design requirements. User feedback also indicated that the system content met contract and usage expectations, and that adjustments were made during implementation based on user needs.

This shows that launch was not treated as the endpoint. Trial operation was used to confirm stability, business fit, and user acceptance before formal use.

5. Combine Method Training with System Training

Training was divided into method-oriented training and system operation training. Method training helped users understand competitive intelligence concepts, research workflows, and analytical tools. System training focused on information collection, back-office management, front-end presentation, model analysis, and hands-on operation.

This addressed a central adoption challenge: the platform was not an isolated IT tool, but a carrier for research methods. Users needed to understand the logic behind the methods before the system functions could become practical research capability.

Reusable Lessons

1. Abstract Projects Should Be Managed Around Methods Before Features

When a project involves research workflows, analytical models, and expert collaboration, the feature list is only the surface. Project management must first clarify how the business method should work, then break it into data structures, workflows, permissions, models, and report outputs.

2. The Data Model Is the Backbone of an Analytical System

Analytical systems are vulnerable to fragmented data and inconsistent definitions. Data objects, classification dimensions, indicator systems, and relationships should be stabilized early, otherwise collection, maintenance, search, statistical analysis, and report generation will all be affected.

3. Testing Should Cover the Collection-Processing-Analysis-Publishing-Archiving Chain

Testing should not stop at pages and buttons. A stronger approach is to test the actual work chain: collection tasks run, texts are processed, data enters the repository, models calculate, results are presented, and research outputs are archived.

4. Trial Operation Should Validate Business Fit, Not Just System Stability

A stable system is not enough. Trial operation should also verify whether the system fits the users’ research habits and working process. User feedback, requirement adjustments, and stable operation records all help determine whether the project is ready for formal use.

5. Training Should Connect Methodology and Operation

If users can operate the system but do not understand the analytical method, the platform’s value will be limited. Combining method training, case explanation, and hands-on system practice helps the handover move from tool deployment to capability adoption.

Conclusion

The management value of this competitive intelligence platform project was its ability to organize scattered information collection, intelligent processing, data maintenance, expert collaboration, and research workflows into a tested and trainable analytical capability. The central lesson is that analytical information systems are not difficult only because of technology. The deeper challenge is translating a research method into system capability. When data models, analytical workflows, test chains, and training are managed together, the system can become a reliable foundation for routine research work rather than just another software platform.