Ignoring Data Quality in Enterprise AI: A Risk You Can’t Afford
Authored by EncompaaS - Jan 29, 2025
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Once a futuristic concept, Artificial Intelligence (AI) has cemented itself as an operational necessity for enterprises aiming to remain competitive in today’s data-driven economy.
However, the success of AI initiatives is far from guaranteed. A critical determinant of success is the quality of the data driving these systems.
Without a robust data quality framework, organisations risk financial losses, compliance breaches, and stifled innovation.
The hidden cost of neglecting data quality
The importance of data quality cannot be overstated. Poor data quality and governance introduces risks that impact every corner of an organisation, including:
- Data breaches and compliance penalties: In 2024, the global average cost of a data breach reached $4.88 million, illustrating how sensitive data mishandling can lead to financial and reputational harm. Regulatory violations, such as GDPR infractions, carry penalties starting at €10 million or 2% of annual revenue—whichever is higher.
- Operational inefficiencies: Research from Gartner shows that poor data quality costs organisations an average of $12.9 million annually, encompassing flawed decision-making and resource wastage.
- AI project failures: Gartner projects that by 2025, 30% of Generative AI initiatives will fail due to poor data quality governance and risk controls. These failures not only delay return on investment (ROI) but also erode competitive advantages.
Increased vulnerability in highly regulated industries
In highly regulated sectors such as finance, healthcare, and government, the importance of data quality cannot be overstated, due to stringent compliance requirements and the sensitive nature of the data involved. Poor data quality in these industries can lead to significant financial penalties, operational inefficiencies, and compromised decision-making.
Finance
Financial institutions worldwide must adhere to regulations that enforce robust financial data quality management. For example, the Basel Committee’s BCBS 239 mandates risk data aggregation standards, while in the US, the Dodd-Frank Act requires accurate reporting to prevent financial crises.
Despite these requirements, many banks continue to face significant challenges in financial data quality management, hindering full compliance.
Healthcare
Healthcare organisations must comply with a range of regulations to ensure secure data management and data quality.
For example:
- Australia: Privacy Act 1988 and My Health Records Act 2012 mandate the secure handling of personal health data.
- United Kingdom: The Data Protection Act 2018 and NHS Digital standards regulate data sharing and quality.
- United States: The Health Insurance Portability and Accountability Act (HIPAA) sets strict standards for safeguarding health information.
Government
Governments manage vast amounts of sensitive data, making regulatory compliance essential.
In Australia, the Privacy Act 1988 and Data Availability and Transparency Act 2022 govern data handling and sharing. The United Kingdom enforces the General Data Protection Regulation (GDPR) and the Freedom of Information Act 2000 to ensure data privacy and accessibility. In the United States, the Federal Information Security Management Act (FISMA) establishes strict standards for securing data across federal agencies.
Globally, governments are increasingly adopting data quality governance frameworks to standardise practices and improve data quality checks, ensuring better data security and reliability.
In these industries, using advanced data quality and governance solutions is critical to reduce risks and meet compliance requirements.
Manual vs. automated data quality management
The debate between manual and automated data quality management often centres on efficiency, accuracy, and scalability. Here’s a comparison:
Metric | Manual processes | Automated data quality management |
Efficiency | Time-intensive, labour-heavy | Rapid and scalable |
Accuracy | Prone to human error | Consistent and reliable |
Scalability | Limited by resources | Easily handles large datasets at scale |
Compliance | Reactive, with higher risk of non-compliance | Proactive, with automated policy enforcement |
Cost | Higher long-term costs due to inefficiencies | Lower operational costs with better ROI |
Manual processes are labour-intensive, prone to human error, and limited in scalability, often resulting in higher long-term costs and a reactive approach to compliance, increasing the risk of violations.
Automation is faster, more accurate, and easily handles large datasets. It proactively enforces governance policies and offers lower operational costs with better ROI, making it a more competitive and efficient choice.
EncompaaS: Empowering enterprises through data quality
EncompaaS delivers comprehensive solutions to address data quality and governance challenges using our proprietary DUGU information management methodology:
- Discover: Identify and catalogue all data assets, including hidden “dark data” across systems.
- Understand: Extract metadata to create a normalised, unified view of diverse data sources.
- Govern: Implement automated policies to ensure compliance and mitigate risks.
- Use: Leverage enriched data to power AI projects for faster and safer outcomes.
The EncompaaS platform allows organisations to improve data quality and use it as a strategic asset for driving AI-driven innovation.
Real-world impacts: Revolutionising data management in pharmaceuticals
A leading US-based pharmaceutical manufacturer partnered with EncompaaS to resolve challenges in rebate management. By replacing highly manual processes with EncompaaS’ advanced information management solution, the organisation achieved remarkable results:
- 10,000+ contracts analysed
- 140,000 data points extracted
- 95%+ data accuracy rate
- 4,000 hours saved per quarter
- 150% faster data retrieval
- 90% improvement in risk detection
This case demonstrates how robust data quality software can revolutionise operational efficiency in highly regulated industries.
Streamlining data preparation for AI
Preparing data for AI doesn’t have to be a complex, time-consuming process. The EncompaaS platform provides a clear, structured approach to enterprise information management.
From AI data preparation to data analytics and reporting, EncompaaS provides the capabilities needed to ensure robust data management for AI success. By automating data quality and governance, organisations can reduce risks, save time, and achieve better outcomes.
If your organisation is looking to pursue enterprise AI adoption, EncompaaS can help you meet the challenges of AI preparation with confidence.
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