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Data Management Platform
Fuel System — Building Enterprise-Grade "Data Asset Factory", Determining the Ceiling of AI Capabilities
There is a classic saying in the AI field: "Garbage in, garbage out." No matter how advanced the model or how powerful the computing resources, if the input data is chaotic, incomplete, or biased, the AI results will inevitably suffer significantly. Many enterprises invest heavily in AI projects but achieve minimal results; the root cause is often not poor algorithms but poor data: data scattered across various business systems with inconsistent formats, uneven quality, high labeling costs, and chaotic version management.
The Magicsoft Data Management Platform was created specifically to solve this "foundation problem." It is the "fuel system" of the AI ecosystem, responsible for transforming enterprise raw data into high-quality, structured, traceable AI-ready data, supporting the full lifecycle of model training, fine-tuning, and inference. We are building not just a data management tool, but an enterprise-grade data asset factory — transforming data from a "cost center" to a "value center."
■ Deep Product Positioning
Building Enterprise-Grade "Data Asset Factory", Achieving Data Standardization, Structuring, and Valuation
🎯 Value Proposition in One Sentence:
Refine enterprise data from "chaotic crude oil" into "high-purity AI fuel", making every training session worthwhile.
The Data Management Platform is neither a database nor a data middle platform (which focuses on BI analysis and reporting). It specializes in serving AI scenarios: supporting annotation, vectorization, and version control of unstructured data (text, images, audio/video), seamlessly integrating with model training pipelines. A mature AI team spends 60%~80% of their time on data processing. Magicsoft Data Management Platform aims to reduce this ratio to below 30%, allowing algorithm engineers to focus their energy on model innovation.
■ Core Module Breakdown
The Magicsoft Data Management Platform covers the entire process from "raw state" to "model-ready", consisting of five core modules.
Multi-Source Access → Cleaning & Governance → Labeling & Processing → Storage Management → Feature Engineering
↓ ↓ ↓ ↓ ↓
Collection Refinement Value-Add Storage Modeling① Multi-Source Data Access System
Module Description:
Enterprise data is scattered across various heterogeneous systems: business databases, log files, object storage, third-party APIs... The Data Management Platform provides rich connectors, supporting one-click access to multiple data sources and unified aggregation into the data lake.
Supported Data Source Types:
| Data Source Category | Specific Sources | Access Method |
|---|---|---|
| Business Systems | MySQL, PostgreSQL, Oracle, SQL Server | JDBC connection, supports incremental sync |
| Data Warehouse/Lake | Hive, Iceberg, Hudi, Delta Lake | Metadata mounting |
| Object Storage | AWS S3, Alibaba Cloud OSS, MinIO | Bucket mounting + directory monitoring |
| Message Queues | Kafka, Pulsar, RocketMQ | Real-time subscription consumption |
| Log Files | Server logs, application logs (JSON/CSV/Text) | Filebeat + automatic parsing |
| External APIs | Third-party data services, crawler data | HTTP polling or Webhook |
| Local Files | Excel, CSV, images, audio/video | Web upload or CLI tool |
Data Access Process Diagram:
Select data source type (e.g., MySQL)
↓
Configure connection info (host/port/account/database name)
↓
Select sync mode (Full / Incremental / Real-time CDC)
↓
Preview data samples, configure field mapping
↓
Create sync task, schedule execution (one-time/periodic)
↓
Data written to unified data lake storage (Iceberg format)👉 Problems Solved:
- Data Silos → Break system barriers, centralize all data management
- Low Access Efficiency → Visual configuration, no coding required, minute-level completion
A large retail enterprise has 20+ business systems with varying data formats. Previously, an AI project required two weeks just for data extraction and integration. After using the Magicsoft Data Management Platform, they configured sync tasks for all data sources through the interface, with data automatically aggregating into a unified data lake. New projects can query directly at launch, reducing data preparation time from two weeks to half a day. More importantly, the platform supports real-time CDC (Change Data Capture), synchronizing business data changes to the data lake within seconds, enabling models to train on the latest data for more timely results.
② Data Cleaning and Governance
Module Description:
Raw data often has various quality issues: duplicates, missing values, anomalies, inconsistent formats... The Data Cleaning and Governance module uses automated rules + manual review to "clean" the data, ensuring that data entering the model is clean and reliable.
Data Quality Issues Classification and Processing Strategies:
| Issue Type | Example | Automatic Processing Strategy |
|---|---|---|
| Duplicate Data | Same order record appears twice | Deduplication (based on primary key or similarity) |
| Missing Values | User age field is empty | Imputation (mean/median/mode/model prediction) or deletion |
| Outliers | Age=200 | Removal based on statistics (3σ) or business rules (0-120) |
| Inconsistent Format | Dates like 2023-01-01, 2023/01/01, 01/01/2023 | Unified conversion to ISO standard format |
| Erroneous Data | Phone number missing one digit | Regex validation, marked for manual correction |
| Irrelevant Data | Dirty data from test environment | Filtering based on source identifier or keywords |
Data Cleaning Workflow:
Raw Data → Quality Assessment Report (dirty data ratio, issue distribution)
↓
Configure cleaning rules (deduplication, imputation, format conversion, outlier removal)
↓
Run cleaning tasks (Spark distributed processing)
↓
Output cleaned data + cleaning logs (records which data was deleted and why)
↓
Data quality score (proceeds to next step after reaching threshold)Data Quality Assessment System:
| Quality Dimension | Metric | Target Value |
|---|---|---|
| Completeness | Non-null field ratio | ≥ 95% |
| Uniqueness | Duplicate record ratio | ≤ 1% |
| Validity | Ratio meeting format/range requirements | ≥ 99% |
| Consistency | Ratio of consistent values for same entity across systems | ≥ 98% |
| Timeliness | Data latency (time from generation to storage) | ≤ 1 hour |
👉 Problems Solved:
- Dirty Data → Automated cleaning, efficiency improved by 10x+
- Unassured Quality → Quantified assessment, data availability at a glance
A logistics company wanted to train an "estimated delivery time" model. The raw data had numerous anomalies: some orders had empty delivery times, some timestamps had chaotic formats, and there were duplicate order records. Training directly with this data would result in extremely biased model predictions. Using the Magicsoft Data Management Platform, they configured missing value imputation (filled with average time for the same route), format unification (timestamps converted to Unix milliseconds), and deduplication (based on order ID). Within half an hour, they obtained a clean dataset. The data quality score improved from 63 to 97, and model training convergence speed also significantly accelerated.
③ Data Labeling and Processing
Module Description:
Supervised learning requires labeled data. The data labeling module supports both manual labeling + AI-assisted labeling, covering various data types such as text, images, and audio, helping enterprises build training sets at low cost and high quality.
Supported Labeling Types:
| Data Type | Labeling Task Examples | Labeling Tool Format |
|---|---|---|
| Text | Classification (sentiment, intent), Named Entity Recognition (NER), relation extraction, Q&A pair construction | Web labeling interface, supports pre-labeling |
| Image | Classification, object detection (bounding boxes), semantic segmentation (pixel-level), keypoint annotation | Rectangle/polygon/point cloud tools |
| Audio | Speech transcription (ASR labeling), sentiment labeling, speaker separation | Waveform + timeline annotation |
| Video | Action recognition, object tracking, shot segmentation | Frame-by-frame annotation + interpolation |
Labeling Process Diagram:
Import unlabeled data → Select labeling template (text classification/bounding box/transcription...)
↓
Assign labeling tasks to labelers (internal team or crowdsourcing platform)
↓
(Optional) AI pre-labeling: model automatically generates labels first, manual correction
↓
Labelers annotate online → Submit labeled data
↓
QA auditor samples for review → Pass to storage; fail returns for re-labeling
↓
Export to training format (JSONL/COCO/CSV)AI-Assisted Labeling (Active Learning):
| Strategy | Description | Effect |
|---|---|---|
| Pre-labeling | Model predicts first, humans only correct errors | Labeling efficiency improved by 3-5x |
| Hard Examples First | Samples the model is uncertain about are prioritized for human labeling | Improve model effect with minimal labeling volume |
| Semi-Automatic Labeling | Image segmentation: click a few times, AI automatically generates edges | Labeling time reduced from minutes to seconds |
👉 Problems Solved:
- High Labeling Costs → AI assistance reduces 70% of manual workload
- Unstable Labeling Quality → QA process + consistency checks, ensuring label accuracy ≥95%
An autonomous driving company needed to label 1 million road images (vehicles, pedestrians, lane lines). If done purely manually, at 30 seconds per image, it would require approximately 8,300 person-days and cost millions. Using Magicsoft Data Management Platform's AI pre-labeling feature, an initial model first automatically labeled the images, and humans only needed to correct errors (about 20% of images needed correction), reducing the workload to 1/5 of the original. Meanwhile, the platform's built-in labeling consistency check randomly samples 10% of samples for labeling by different labelers, calculating the Kappa coefficient. When below threshold, it triggers re-review, ensuring labeling quality.
④ Data Storage System
Module Description:
Data volume in AI scenarios is massive (PB-level) and requires support for high-concurrency read/write operations. The Data Management Platform adopts a distributed storage architecture combined with data tiering strategies to ensure performance while controlling costs.
Storage Architecture:
┌──────────────────────────────────────────────────┐
│ Data Lake │
│ Raw Data → Cleaned Data → Feature Data │
└──────────────────────────────────────────────────┘
↓
┌──────────────────────────────────────────────────┐
│ Vector Database │
│ Text Embedding / Image Embedding │
└──────────────────────────────────────────────────┘
↓
┌──────────────────────────────────────────────────┐
│ Feature Store │
│ Offline Features + Online Features │
└──────────────────────────────────────────────────┘Data Tiering Strategy (Hot-Cold Separation):
| Tier | Storage Medium | Access Frequency | Cost | Retention Period |
|---|---|---|---|---|
| Hot Data | NVMe SSD + Memory Cache | High (daily training) | High | 1~3 months |
| Warm Data | SATA SSD / Standard HDD | Medium (weekly review) | Medium | 3~12 months |
| Cold Data | Object Storage (S3/OSS) | Low (audit, archive) | Low | 1+ years |
Key Performance Metrics:
| Metric | Target Value | Description |
|---|---|---|
| Write Throughput | ≥ 1 GB/s | Supports real-time data ingestion |
| Read Throughput | ≥ 2 GB/s | Supports multi-GPU parallel reading for training |
| Random Read Latency | < 10ms | Online feature query |
| Vector Search Latency | < 100ms (million-level vectors) | RAG scenarios |
👉 Problems Solved:
- Large Data Volume → Distributed scaling, supports PB-level storage
- Read/Write Performance → Tiering + caching, data doesn't become a bottleneck during training
- Cost Out of Control → Hot-cold separation, hot data on SSD, cold data on object storage, cost reduced by 70%
A social media company generates dozens of TBs of user behavior logs daily, requiring 90 days of storage for model training. If all stored on SSD, monthly storage costs would exceed $100,000. The Magicsoft Data Management Platform adopts intelligent tiering: hot data from the last 7 days on SSD (for daily incremental training), warm data from days 8-30 on HDD (for weekly review), and cold data from days 31-90 automatically archived to object storage (for compliance auditing). Storage costs dropped from $100,000 to $30,000, while hot data training performance remained unaffected.
⑤ Feature Engineering and Data Modeling
Module Description:
After raw data is cleaned and labeled, it needs to be transformed into features that models can learn from. The Feature Engineering module provides rich capabilities for feature extraction, transformation, and combination, seamlessly integrating with model training pipelines.
Feature Engineering Capabilities Overview:
| Capability | Description | Example |
|---|---|---|
| Numerical Features | Normalization, binning, missing value imputation | Age: 25 → (25-mean)/std |
| Categorical Features | One-hot, Label Encoding, Embedding | City: Beijing→[1,0,0] |
| Text Features | TF-IDF, Word2Vec, BERT Embedding | User review → 768-dim vector |
| Time Features | Extract year/month/day/hour/week, difference calculation | Order time → "Is Weekend" |
| Cross Features | Combine multiple features | Age×Income Level |
| Feature Selection | Based on variance, mutual information, model importance | Auto-filter Top-K features |
Feature Store:
Offline Features (batch processing computation, for training)
↓
Write to Feature Store (supports time travel, Point-in-time correct)
↓
Online Features (real-time computation, for inference)
↓
Feature Service API (for model calling)Feature Engineering and Model Training Integration:
Raw Data → Feature Engineering (define feature logic) → Training/Validation Set
↓
Model Training (auto-pull features)
↓
Model Inference (online feature service)👉 Problems Solved:
- Duplicate Feature Development → Feature Store allows features to be defined once and shared by training and inference
- Online-Offline Inconsistency → Unified feature logic ensures consistent feature computation between training and inference
- Difficult Feature Backtracking → Time Travel supports feature snapshots at any point in time
In traditional ML workflows, feature engineering is often the most error-prone area. A typical "training-inference inconsistency" issue: during training, user click counts from the previous day are used as features, but during inference, only data up to the current moment can be obtained, resulting in different distributions. Magicsoft Feature Store solves this problem: it saves historical snapshots of features, allowing training to pull features at the same time point as inference, ensuring online-offline consistency. Additionally, feature reuse allows Team A's defined user profile features to be directly used by Team B without recalculation, significantly improving efficiency.
■ Advanced Capabilities (Differentiators)
① Enterprise Knowledge Base Construction (RAG System)
Capability Description:
Retrieval-Augmented Generation (RAG) is currently the mainstream paradigm for LLM implementation. The Data Management Platform includes a built-in knowledge base construction pipeline, helping enterprises transform internal documents, FAQs, product manuals, and other unstructured data into LLM-retrievable knowledge bases.
Knowledge Base Construction Process:
Enterprise Documents (PDF/Word/HTML/Database)
↓
Document Parsing + Text Chunking
↓
Embedding Vectorization (calling Embedding models)
↓
Store in Vector Database (Milvus/PGVector/Qdrant)
↓
Provide Retrieval API (input question, output relevant snippets)Supported Document Formats:
| Format | Parsing Method |
|---|---|
| OCR + Layout Analysis | |
| Word/Excel/PPT | Embedded Parser |
| HTML/Markdown | Tag Stripping |
| Database | SQL Query to Text |
👉 Value:
- Let LLMs "Understand" the Enterprise: RAG enables LLMs to answer questions based on enterprise private knowledge
- Knowledge Accumulation: Enterprise knowledge transforms from scattered documents into structured, retrievable assets
A medical technology company has tens of thousands of pages of drug instructions, clinical guidelines, and internal operation procedures. In the past, doctors had to sift through numerous documents to check drug contraindications. Using the Magicsoft Data Management Platform, they automatically parsed, chunked, and vectorized these documents to build a medical knowledge base. Combined with LLMs, doctors simply ask "Can drug XX and drug YY be taken together?" and the system retrieves relevant snippets and generates accurate answers, significantly improving clinical efficiency.
② Vector Database Support (Embedding)
Capability Description:
Vector databases are core components of RAG and semantic search. The Data Management Platform includes or integrates mainstream vector databases, supporting massive vector storage and efficient similarity retrieval.
Vector Database Comparison:
| Database | Characteristics | Applicable Scenarios |
|---|---|---|
| Milvus | Distributed, GPU-accelerated, most comprehensive features | Large-scale production environments |
| Qdrant | Written in Rust, high performance, cloud-native | Scenarios with high latency requirements |
| PGVector | PostgreSQL extension, simple and easy to use | Small-scale, don't want to introduce new components |
| Elasticsearch | Supports both full-text and vector | Hybrid retrieval requirements |
Performance Metrics:
| Scale | Recall Rate (@10) | Latency (P99) |
|---|---|---|
| 1M entries (768-dim) | ≥ 95% | < 50ms |
| 10M entries | ≥ 92% | < 200ms |
| 100M entries | ≥ 90% | < 1s (requires GPU acceleration) |
👉 Value:
- Semantic Search: No longer relies on keywords, understands user intent
- RAG Foundation: Provides enterprise knowledge context for LLMs
An e-commerce platform built a product semantic search system using vector databases. When users search for "lightweight jackets suitable for summer," traditional keyword search can only match titles containing these words with unsatisfactory results. Vector search converts both user queries and product descriptions into Embeddings, retrieving semantically similar products with recommendations that better match user expectations. Search result click-through rates improved by 25%.
③ Data Version Control (Data Versioning)
Capability Description:
Model training requires reproducibility. Data version control allows enterprises to manage data like code: each dataset change generates a version, supporting rollback, comparison, and rollback.
Version Control Capabilities:
| Capability | Description |
|---|---|
| Snapshot | Tag the complete state of a dataset at a specific point in time |
| Incremental Version | Only record changed parts, saving storage |
| Version Comparison | Compare data distribution differences between two versions (PSI) |
| Rollback | Restore dataset to a previous version |
| Lineage Tracking | Trace data sources and processing workflows |
Version Management Diagram:
dataset v1.0 (2025-01-01): 100k raw logs
↓ Cleaning
dataset v1.1 (2025-01-02): 95k entries, deduplication + outlier removal
↓ Labeling
dataset v2.0 (2025-01-15): 80k labeled data
↓ Add new data
dataset v2.1 (2025-02-01): 120k entries (merged 40k newly labeled)👉 Value:
- Reproducibility: Models trained with v2.0 can be reproduced at any time
- Experiment Comparison: Dataset v2.0 vs v2.1, which trains better models
- Compliance Audit: Know where model training data comes from and what processing it underwent
A fintech company was required by regulators to prove that training data used for each risk control model was compliant and traceable. Magicsoft Data Management Platform's data version control records dataset version numbers during each training session and saves complete data lineage (data sources, cleaning rules, labeling personnel). During audits, simply providing the version number and lineage diagram is sufficient to pass review. This was almost impossible in the era without version control.
■ Core Business Value
| Value Dimension | Traditional Model | Magicsoft Data Management Platform |
|---|---|---|
| Data Preparation Time | 2-4 weeks | 1-3 days |
| Data Quality | Relies on manual inspection, high omission rate | Automated quality assessment + cleaning, quality score ≥95% |
| Labeling Cost | Purely manual, expensive | AI-assisted labeling, cost reduced by 60%~80% |
| Feature Development Efficiency | Duplicate development, online-offline inconsistency | Feature Store, define once, reuse everywhere |
| Model Reproducibility | Difficult, no record of data changes | Data version control, fully reproducible |
| Knowledge Accumulation | Data discarded after use, no accumulation | Data asset factory, continuous accumulation and appreciation |
Value Summary:
- Improve AI model training effectiveness (high-quality data + rich features)
- Reduce data processing costs (automation + AI assistance)
- Achieve data asset accumulation (versioning + knowledge base)
- Support long-term AI capability upgrades (data becomes more valuable with accumulation)
The core business value of the Data Management Platform can be expressed in one formula: AI Effectiveness = (Data Quality × Data Scale) / Data Processing Cost. Magicsoft simultaneously improves quality and scale while reducing costs through automated, intelligent, and systematic data management, maximizing enterprise data ROI. More importantly, once data assets are formed, they become a competitive moat — competitors can buy the same models, but they cannot buy the high-quality labeled data and knowledge base accumulated by enterprises over years.
■ Customer Case Study (Example)
A Certain Internet Finance Company:
Pain Points: Risk control models needed to integrate multiple data sources (transactions, credit, device fingerprint); data access and processing took 2 weeks; limited labeled samples, poor model performance.
Solution: Deployed Magicsoft Data Management Platform, unified access to 6 data sources, automated cleaning and feature engineering; used active learning to assist labeling, rapidly expanding the training set.
Results: Data preparation time reduced from 2 weeks to 2 days, labeling costs decreased by 70%, risk control model AUC improved from 0.82 to 0.89, bad debt rate decreased by 15%.
■ Next Steps (CTA)
📌 If your enterprise:
- ✅ Has data scattered across various systems with difficult access
- ✅ Has poor data quality and unsatisfactory model training results
- ✅ Has high labeling costs and slow progress
- ✅ Has duplicate feature development with online-offline inconsistency
- ✅ Wants to build a RAG knowledge base but doesn't know where to start
👉 Contact Magicsoft Data Management experts to receive:
- ✅ Data Maturity Assessment (evaluate your enterprise's current data quality status)
- ✅ Industry Data Processing Best Practices White Paper
- ✅ Free PoC (access one data source, complete cleaning + labeling + feature engineering)
Let the Data Management Platform become the "foundation" and "fuel" for your AI strategy.