Essential Data Warehouse Self‑Check Checklist: 200+ Items Across 6 Core Modules

Data Quality (≈50 items)

1. Data Completeness

Verify that all required fields contain no null or missing values (e.g., name, date).

Ensure primary‑key fields are unique and not null (e.g., order ID, user ID).

Confirm foreign‑key fields reference existing entities (e.g., department ID exists in the department table).

Require double‑verification of data entry (manual review or system validation).

Validate that data conforms to predefined formats (e.g., phone number, date format).

2. Data Accuracy

Cross‑check data against original sources (e.g., bank statements vs. invoices).

Identify outlier values (e.g., negative sales, unusually high figures).

Enforce business rules (e.g., age ≥ 0, amount ≥ 0).

Perform periodic calibration for sensor or measurement data.

Obtain third‑party audit or verification where applicable.

3. Data Consistency

Ensure the same business entity has consistent identifiers across systems (e.g., customer ID in CRM vs. ERP).

Validate cross‑system relationship integrity (e.g., order table matches inventory table).

Confirm multi‑source data is synchronized (e.g., operational DB and data warehouse versions match).

Standardize data dictionaries (e.g., gender field uses "Male/Female" instead of "M/F").

4. Data Compliance

Check adherence to industry regulations (e.g., financial data meets supervisory requirements).

Mask or redact sensitive fields (e.g., partial ID number, phone number masking).

Verify compliance with internal data‑classification policies.

Ensure processing complies with laws such as the Personal Information Protection Law.

5. Data Redundancy

Detect duplicate records of the same entity across systems (e.g., customer info duplicated in CRM and OA).

Remove redundant columns (e.g., duplicate "address" field).

Regularly clean backup data (e.g., delete expired logs).

6. Data Timeliness

Confirm critical data is refreshed within required windows (e.g., real‑time transaction updates).

Measure data latency against acceptable thresholds (e.g., report generation ≤ 24 hours).

Ensure scheduled ETL jobs complete on time.

7. Data Validity

Validate that data meets business requirements (e.g., sales data includes all required fields).

Flag or purge expired data (e.g., invalid coupons).

Require formal review and approval (e.g., financial data signed off by accounting supervisor).

Data Security (≈40 items)

1. Physical Security

Ensure servers and storage reside in controlled, secure areas (e.g., data‑center access control).

Verify surveillance coverage of critical zones (e.g., ATM rooms, data‑center entrances).

Install anti‑tamper mechanisms on equipment.

2. Access Control

Apply the principle of least privilege to user permissions.

Enforce password policies (complexity, rotation frequency).

Require dual‑approval for high‑risk operations (e.g., cash loading, data deletion).

Maintain comprehensive audit logs for all actions (login, data modifications).

3. Data Encryption

Encrypt data in transit (e.g., HTTPS, SSL/TLS).

Encrypt data at rest (e.g., encrypted database columns, file‑level encryption).

Implement secure key management, including sealed backup of encryption keys.

4. Backup & Recovery

Schedule regular backups (e.g., daily full backups).

Periodically test restore procedures to confirm recoverability.

Maintain off‑site or disaster‑recovery copies.

5. Monitoring & Auditing

Retain surveillance footage for a defined period (e.g., ≥ 3 months for ATM cameras).

Ensure system logs contain timestamps and operator identifiers.

Configure alerts for anomalous activities (e.g., unauthorized access attempts).

6. Third‑Party Access Management

Require confidentiality agreements for outsourced personnel.

Supervise third‑party activities (e.g., accompany equipment maintenance).

Promptly revoke temporary access rights after use.

Compliance & Legal (≈30 items)

1. Legal & Regulatory Compliance

Confirm compliance with the Data Security Law and Personal Information Protection Law.

Validate cross‑border data transfers against regulatory requirements (e.g., security assessment passed).

Maintain an up‑to‑date data‑breach response plan.

2. Internal Policy Execution

Enforce data classification and grading policies (e.g., label sensitive data).

Document data‑governance processes (operation manuals, standard procedures).

Conduct regular compliance training (at least annually).

3. Data Privacy Protection

Publish privacy policies (e.g., website privacy statement).

Apply anonymization or de‑identification where required (e.g., statistical reports).

Obtain explicit user consent before data collection.

4. Auditing & Certification

Achieve security certifications such as ISO 27001.

Update external audit reports on a regular schedule.

Remediate identified compliance gaps promptly.

Process & Management (≈30 items)

1. Data Lifecycle Management

Standardize the end‑to‑end flow from data acquisition to archiving.

Define archiving retention periods (e.g., retain for 5 years).

Apply secure destruction methods (physical shredding or encrypted erasure).

2. Change Management

Require approval for data‑model changes.

Perform impact analysis before system upgrades.

Prepare feasible rollback plans.

3. Documentation & Records

Maintain an up‑to‑date data dictionary with clear field definitions.

Ensure operation logs are complete (e.g., ATM cash‑loading records).

Submit fault or anomaly reports promptly.

4. Process Standardization

Enforce SOPs for data entry (e.g., dual verification for manual input).

Define cross‑department data‑sharing procedures.

Close data‑quality issues through root‑cause analysis.

Technology & Tools (≈20 items)

1. Data Storage & Processing

Regularly optimize databases (index maintenance, fragmentation reduction).

Support real‑time queries in the data warehouse.

Automate ETL pipelines to minimize manual intervention.

2. Metadata Management

Maintain a centralized metadata catalog.

Map metadata to business terminology (field ↔ business meaning).

3. Data Quality Monitoring

Deploy automated data‑quality monitoring tools (anomaly detection, rule‑based checks).

Generate regular data‑quality reports (e.g., monthly).

4. Automation Tools

Use automated cleaning utilities (e.g., missing‑value imputation).

Integrate AI models for governance assistance (e.g., anomaly detection).

Personnel & Training (≈20 items)

1. Responsibility Clarification

Establish a data‑governance team or committee.

Define roles and responsibilities (data steward, security officer, etc.).

2. Training Plan

Provide onboarding data‑governance training for new hires.

Conduct annual training covering all staff.

Include outsourced personnel in internal training programs.

3. Awareness Improvement

Incorporate data‑security awareness into performance evaluations.

Run case‑study sessions and examinations on data‑leak incidents.

Code example

扫码即可加入星球
👇全部获取