The role of AI tools to support data quality

Petrus Keyter, Data Governance Consultant at PBT Group

Over the past 12 months, artificial intelligence (AI) tools have emerged as a game changer when it comes to data quality. These play an important role in supporting data quality by automating, enhancing, and streamlining various data quality processes and functions.

Below are three of the key areas in which AI tools can improve data quality:

• Anomaly detection: AI tools can identify outliers or unusual patterns in data that may indicate errors or fraud. Machine learning models can adapt to evolving data trends and improve their detection capabilities over time.
• Predictive data quality: AI can predict potential data quality issues before they occur by analysing trends and patterns in the data.
• Data cleansing: AI tools can automatically identify and correct errors. These include duplicates, inconsistencies, and missing values, to improve the overall accuracy of the data. Furthermore, machine learning algorithms can learn from past corrections to enhance future data cleansing efforts.

AI can be seen as an essential part of data quality tools for the future. Let me explain further. The continuous self-improvement ability of AI-driven data quality tools can reduce costs dramatically. AI tools can learn from data quality issues and improve their processes over time, becoming more effective at maintaining and enhancing data quality without extensive human intervention. Such tools will also result in more reliable, accurate, and timely data for businesses to make more informed decisions and improve their operational efficiencies.

AI advantages in data quality

There are a myriad of benefits when it comes to integrating AI into data quality management. The ones that stand out include:

• Improved data governance: AI can help enforce data governance policies by automatically applying rules and standards across the organisation. Data quality is therefore maintained and in line with regulatory requirements and internal policies.
• Real-time data quality monitoring: AI tools can monitor data quality in real-time, providing immediate feedback and alerts on potential issues. This ensures that data remains reliable and actionable.
• Proactive issue resolution: AI can predict potential data quality problems before they occur, allowing organisations to address issues proactively rather than reactively.
• Faster time to value: Because AI tools can process and cleanse data much faster than traditional methods, the tools can reduce the time it takes to make data ready for use.
• Self-learning and adaptability: AI systems can learn from past data quality issues and continuously improve their performance. This adaptability ensures that the system becomes more effective over time, even as data sources and types evolve.

By leveraging these benefits, organisations can ensure that their data remains a valuable asset, driving more accurate insights, improving operational efficiency, and supporting better informed decision-making.

Key considerations

Successfully implementing AI in data quality management requires careful consideration of various factors to ensure that the benefits are fully realised while avoiding potential pitfalls. Some of the key considerations include the following:

1. Clear objectives and goals.

The business must define the specific data quality issues it aims to address with AI. Whether it is improving accuracy, reducing duplicates, or automating cleansing processes, having clear objectives helps in selecting the right AI tools and approaches.

2. Data quality framework.

Establish a robust data quality framework that includes standards, metrics, and governance practices. AI tools should operate within this framework to ensure consistency and alignment with organisational goals.

3. Data governance and compliance.

Ensure that AI-driven data quality processes comply with relevant regulations and standards. Furthermore, strong data governance policies should be in place to manage AI’s role in maintaining data quality.

4. Data privacy and security.

AI tools may need to access sensitive or personal information. It is therefore essential for the company to implement strong data privacy and security measures to protect this data from unauthorised access or breaches.

5. Quality of training data.

AI models rely on training data to learn and make predictions. The quality of this data is critical. Poor quality training data can lead to inaccurate or biased AI outputs.

6. Transparency and explainability.

It is important to ensure that the AI processes are transparent and explainable. This is especially the case when decisions or corrections are being made to data. An explainable decision-making process builds trust and allows for better governance.

7. Human oversight and intervention.

While AI can automate many data quality tasks, human oversight is still necessary. Data quality teams should monitor AI outputs and be ready to intervene when necessary.

8. Scalability and flexibility.

AI tools must be able to scale with the organisation’s data needs and adapt to changing requirements. Flexibility is key as data environments and quality standards may evolve over time.

9. Integration with existing systems.

AI tools should be able to integrate with the company’s current data management systems and workflows. This includes ensuring compatibility with existing databases, ETL processes, and data governance tools.

10. Cost and resource allocation.

Implementing AI for data quality can require significant investment in terms of technology, infrastructure, and talent. Carefully consider the costs and allocate resources appropriately, ensuring that the expected benefits justify the investment.

11. Continuous learning and improvement.

AI models should be regularly updated and retrained as new data becomes available and as data quality issues evolve. Continuous learning ensures that the AI remains effective and relevant over time.

12. User training and adoption.

Ensure that the data quality team and other stakeholders understand how to use AI tools effectively. Provide training and support to encourage adoption and maximise the tools’ impact.

13. Managing expectations

It is important for the business to set realistic expectations about what AI can achieve in terms of data quality. AI should not be seen as a cure-all. Rather, it must form part of a broader data management strategy.

14. Monitoring and feedback loops.

A company should implement monitoring systems to track the performance of AI-driven data quality processes. Feedback loops can be used to continually refine AI models and processes based on real-world results.

15. Ethical considerations

AI applications in data quality should be guided by ethical principles, particularly in areas like bias detection and fairness. Decision-makers need to ensure that AI-driven decisions do not inadvertently introduce or perpetuate biases.

By carefully considering these factors, organisations can effectively leverage AI to enhance data quality while minimising risks and maximising the value of their data assets.