DataService

As the quality control hub within the research data value chain, the data processing team is dedicated to converting raw, heterogeneous and fragmented research data into highly usable, highly credible structured analytical assets. Centered on three core workflows—data cleansing, real-time monitoring and collaborative management—we have built a professional data processing system tailored to research scenarios.

Fine-grained Data Cleansing

From Raw Noise to Analysis-Grade Assets

Data cleansing lays the foundation for research work. We have established a rigorous three-stage, nine-step cleansing standard to guarantee 100% credible and usable data input for analytical models.

Logical Verification & Outlier Elimination：

For questionnaire data and database extracted data, we adopt a dual mechanism combining box plot analysis and logical skip verification. The system automatically identifies and flags contradictory responses (e.g., a respondent aged 20 with 30 years of working experience), extreme value interference, and incomplete invalid samples, eliminating junk data at the source.

Intelligent Missing Value Processing：

We abandon the crude one-size-fits-all deletion method. By adopting multiple imputation and dynamic mean/median replacement models, we implement differentiated filling strategies for various variable types (continuous variables / categorical variables), which preserves sample size while restoring the characteristics of data distribution to the greatest extent.

Structured Cleansing of Text Data：

For open-ended questions and unstructured text captured via web crawlers, we deploy customized regular expression matching and NLP word segmentation tools. The system efficiently filters meaningless modal particles, garbled promotional content and repetitive spam texts, extracts core keywords and labels texts with sentiment polarity tags.

Full-Process KPI Monitoring

Metric-Based Measurement of Data Health & Business Outcomes

Data processing itself requires digitalized evaluation, and the data we deliver can also support clients’ KPI tracking systems. Accordingly, we have built a two-dimensional KPI monitoring framework. It enables us to autonomously control data quality, while allowing clients to view their own KPI metrics at a glance.

Data Metric KPI Monitoring：

We can configure core data metrics for real-time monitoring in accordance with clients’ requirements, including valid sample retention rate, outlier proportion, logical verification pass rate and field completeness. Once any of these metrics hits the preset warning threshold, we will launch in-depth troubleshooting to guarantee all delivered data stays within the KPI standard range.

Business-Oriented KPI Delivery：

We can aggregate metrics such as the improvement of Net Promoter Score (NPS), changes in unaided brand recall rate, and scores of key drivers of user satisfaction into KPI data packages ready for client use. Free drill-down analysis by dimensions including time, region and demographic groups is supported to align KPI indicators with clients’ business demands.

Senior Data Team

Human-Machine Collaborative Intelligence Guarantee

No sophisticated algorithm can replace human experience and insight. Our team integrates technical thinking and research thinking in depth. What we deliver to you is not merely a set of codes, but interpretable and trustworthy solutions.

Compound Professional Backgrounds：

All team members come from interdisciplinary fields including statistics, computer science, marketing and psychology, with an average of over five years of data processing experience in the market research industry.

Rigorous Delivery Management：

We assign a dedicated Data Project Manager (DPM) to every data processing project, who delivers end-to-end oversight covering pre-project data assessment, cleansing plan confirmation, KPI dashboard setup and final delivery acceptance. This dedicated-single-person accountability model ensures consistent data quality throughout complex, long-cycle projects.