Data Collection-Governance-Analytics-Visualization · Compliance · Business Customization

Big Data Services are designed for enterprises "with massive data but unsure how to leverage it, needing to optimize business via data", covering e-commerce, manufacturing, retail, and finance. Unlike basic data processing, we align with global data privacy standards (GDPR/CCPA) to offer "end-to-end data services": from multi-source data collection (APPs, IoT devices, business systems) to cleansing & governance, then to advanced analytics (user profiling, sales forecasting, risk identification) and visualization (BI dashboards), delivering actionable business insights. Supporting global big data platforms (Apache Hadoop, AWS EMR, Google BigQuery), we deliver phase results in 3-8 weeks, solving pain points like "disorganized data, unfulfilled value, and high compliance risks".

1. Data Collection & Governance

• Multi-Source Data Integration: Connect enterprise-wide data (business systems like ERP/CRM, user behavior like APP clicks, IoT data like sensor temperature) supporting structured (MySQL), semi-structured (JSON), and unstructured (logs/images) data;
• Data Cleansing & Governance: Remove redundant/anomalous data (missing value imputation, duplicate removal), unify data formats/standards (e.g., unique user IDs), establish data lineage, and deliver Data Asset Catalog;
• Compliance Handling: Implement data classification (e.g., sensitive data encryption), conduct data anonymization (e.g., phone numbers as 138\*\*\*\*5678), ensuring alignment with GDPR/CCPA and industry regulations (e.g., financial data filing).

2. Data Modeling & Analytics

• Business Modeling: Build customized analytics models based on needs (e.g., e-commerce "user conversion model", manufacturing "equipment failure prediction model", retail "sales forecasting model");
• Advanced Analytics:

3. Data Visualization & Business Enablement

• Visualization Dashboards: Build enterprise BI dashboards (FineBI, PowerBI, Tableau) to display real-time KPIs (e.g., "real-time sales, user activity trends, equipment status") with drill-down analysis (e.g., from "national sales" to "store-level sales");
• Actionable Insights: Deliver Data Analytics Report with specific business optimization plans (e.g., "push beauty coupons to 25-30y female users", "adjust production line parameters to reduce failures");
• Long-Term Support: Provide dashboard maintenance & data updates (e.g., daily auto-sync of business data) and quarterly data reviews to iterate models for business changes.

4. Target Scenarios

• 1. E-Commerce User Operations: E.g., "Big data for an e-commerce platform" — build user profiles via behavior analysis (e.g., "price-sensitive users", "high-frequency repeat buyers") to enable personalized recommendations, boosting conversion by 15%;
• 2. Manufacturing Production Optimization: E.g., "Big data for an auto parts factory" — collect equipment data (speed, temperature) to build failure prediction models, reducing downtime by 30%;
• 3. Retail Inventory Management: E.g., "Big data for a grocery chain" — analyze historical sales, weather, and promotions to forecast store-level demand, cutting stockouts by 20% and inventory costs by 15%;
• 4. Financial Risk Control: E.g., "Big data for a consumer finance firm" — integrate credit and transaction data to build credit scoring models, lowering bad debt rates by 8%.