Data Cleansing

Data cleansing is a critical business function that involves identifying and correcting inaccurate, incomplete, or inconsistent data within databases. Traditionally, human data analysts perform this role by manually reviewing datasets, identifying errors, and applying corrections based on predefined rules and domain knowledge.

1. Healthcare: Standardising patient records across multiple hospitals to ensure consistent treatment protocols.
2. Finance: Reconciling transaction data from various sources to maintain accurate financial reporting.
3. E-commerce: Harmonising product catalogues from multiple suppliers to create a unified customer-facing database.
4. Manufacturing: Aligning inventory data across different production facilities to optimise supply chain management.
5. Education: Consolidating student information from various departments to create comprehensive academic profiles.

1. AI system connects to multiple data sources and systems
2. AI analyses data structures and content across all connected systems
3. AI identifies inconsistencies, duplicates, and errors based on predefined rules and machine learning algorithms
4. AI applies standardisation protocols to harmonise data formats (e.g., date formats, address structures)
5. AI performs automated corrections for clear-cut issues (e.g., obvious typos, standardising abbreviations)
6. AI flags complex issues requiring human review
7. Human data stewards review flagged items and approve or modify AI-suggested corrections
8. AI applies approved changes across all relevant systems
9. AI generates comprehensive reports on cleansing actions taken and remaining issues
10. AI continuously monitors data quality and learns from human interventions to improve future cleansing accuracy

If needed at any point:

• AI can revert changes if errors are detected
• Human operators can manually override AI decisions
• AI can prioritise critical data fields for urgent attention

Data privacy and security AI systems are designed with robust security measures and can be configured to comply with data protection regulations like GDPR. Sensitive data can be anonymised or pseudonymised before processing, and access controls ensure that only authorised personnel can view or modify critical information.

Accuracy of AI decisions While AI significantly reduces errors compared to manual processes, it's not infallible. That's why the system flags complex cases for human review and continuously learns from these interventions. Regular audits and quality checks ensure the AI maintains high accuracy levels.

Integration with legacy systems Modern AI data cleansing solutions are designed to work with a wide range of data formats and can interface with legacy systems through various APIs and connectors. In cases where direct integration is challenging, data can be exported, cleansed, and re-imported.

Loss of human expertise Rather than replacing human expertise, AI augments it. Data stewards and analysts can focus on complex cases and strategic data management instead of repetitive tasks. This often leads to more engaging work and opportunities for skill development in AI-assisted data management.

Handling of context-specific data While AI excels at applying consistent rules, it can be trained to recognise industry-specific contexts. For truly unique cases, the system flags these for human review, ensuring that critical context-dependent decisions are made by subject matter experts.