AI is only as good as the data it learns from; poor data leads to poor predictions.
Here are some ways where we can improve our Data quality.
1- Data governance framework: –
- Define clear rules for how data is entered, stored, and updated.
- Assign ownership for critical fields like customer info, inventory, or financial records.
- Enforce standards across departments to avoid duplication or conflicting formats.
2- Automated Data cleaning: –
- Use AI/ML models to detect anomalies.
- Automate corrections where possible.
- Flag suspicious records for human review instead of letting errors propagate.
3- Validation at Entry: –
- Configure ERP forms with real‑time validation (e.g., mandatory fields, dropdowns instead of free text).
- Integrate external APIs to verify data as it’s entered.
4- Master Data management: –
- Create a single source of truth for core data (customers, suppliers, products).
- Sync across modules so finance, sales, and supply chain all reference the same records.
- Prevent “data silos” where each department maintains its own inconsistent version.
5- Periodic Audits: –
- Schedule regular data quality audits (monthly/quarterly).
- Use dashboards to track KPIs like duplicate rate, missing values, error frequency.
- Encourage accountability by tying audit results to process improvements.
6- Continuous monitoring: –
- Use anomaly detection to flag irregular transactions or inconsistent codes in real time.
- Integrate dashboards to track KPIs such as duplicate rate, error frequency, and data completeness.