Cryptoracle Data Analysis Team

Date: September 22- September 28, 2025

Data source coverage

Core indicators

Total Records Ingested: 192,216,615

Total Groups Crawled: 5429 (Operational Effective Group 5429)

Effective data rate: 100%

New Groups Crawled in the Last 7 Days: 626

New field

Inspection Time: September 26, 2025

Inspection Object: Data

Random Sampling (Sample Size: 200)

Accuracy Rate: 45%

Main Issues:

Low accuracy rate in English

Misidentification of semantic types as advertisements

Normal chat

Notification or forwarded notifications

Explanatory content

Omission Rate: 0%

Iteration Goal: Ensure indicator stability across version updates and minimize data drift.

Current Progress: Sentiment-related indicators have been validated as effective; activity-related indicators still require further refinement.

Data Flow: Raw values → smoothed/processed data → enhanced explanatory data (the final explanatory layer is not delivered to clients).

Delay Risk Mitigation: Prepare contingency plans in case of delivery delays.

On-Time Delivery: Requires team-wide alignment and consensus.

1. Data Crawling:

Improve the efficiency of acquiring new signal sources.

Enhance the precision of data ingestion into the database.

2. ODS (Operational Data Store) Ingestion:

Cross-check and align legacy and new data prior to iteration.

Implement two-stage deduplication (ODS layer and indicator computation layer) to ensure no duplicate records, with clearly defined deduplication logic.

Conduct timely checks to identify and address missing or failed ingestion tasks.

3. Indicator Computation:

Proactively gather and understand data requirements, with proper documentation and technical preparation in advance.

Maintain clear communication and alignment with stakeholders, ensuring all requirements are fully documented in writing.

4. Data Analysis:

Development team should respond quickly to requests from the analytics team, providing the required datasets and supporting analyses.

Analytics team should propose timely handling strategies for the next iteration.

Expand tagging capabilities for community segmentation.

Define data objectives for each iteration and synchronize them with all team members beforehand.

5. Testing:

Allocate dedicated testing time during each iteration cycle.

Focus testing efforts on data completeness and timeliness of processing.