Artificial Intelligence has become, to the disgruntlement of many, a buzzword these days. Regardless of how you feel about the trend and (mis)use of the words, almost everyone will agree that AI will infiltrate every industry in the years to come.
As many know, data is the fuel that drives most AI systems, and the capabilities of AI algorithms are only as effective as the data they are trained on. In addition, the data volume in organizations is expected to increase exponentially in the coming years. Therefore, the adage "rubbish in, rubbish out" holds more truth than ever before for AI.
If left unchecked, bad data and bad data governance practices will be the silent assassin of many AI projects across industries. In the below, I’ve listed some high-level, practical industry examples on why Data Governance and AI are inseparable when looking at a bright, AI-driven future. It’s a non-exhaustive list, but it hopefully sparks the conversation.
1. Data Quality Determines AI Performance
AI systems are hungry for data, but not just any data. For instance, in healthcare, if medical imaging AI algorithms are trained on incomplete, biased, or unstructured datasets, they could perpetuate or even exacerbate existing biases, leading to incorrect diagnoses or treatment recommendations.
2. Managing Bias and Fairness
One notable real-world example involves AI recruitment tools. Flawed data and inadequate governance can embed biases into the system, leading to discriminatory hiring practices that favor or exclude certain demographics, contravening fairness principles.
3. Enhanced Accuracy and Reliability
Consider a finance company leveraging AI for fraud detection. Without proper data governance, inaccurate or outdated data could result in the failure to detect fraudulent patterns, leading to substantial financial losses and compromised customer trust.
4. Compliance and Ethical Considerations
Industries like insurance will heavily rely on AI for risk assessment. Insufficient data governance may lead to AI models utilizing personal data in non-compliant ways, resulting in legal repercussions and reputational damage.
5. Effective Decision-Making and Innovation
In the retail sector, AI-powered recommendation systems can significantly boost sales. However, without robust metadata management, these systems might recommend irrelevant products to customers, diminishing user experience and potentially causing revenue loss.
Final Thoughts
In a world where AI will increasingly shape our decisions and experiences, the value of quality data underpinned by effective data governance cannot be overstated. The success and trust in AI applications heavily rely on the quality of the data they are built upon. Thus, prioritizing data governance is not just a choice but a necessity for AI to realize its full potential while maintaining ethical and accurate operations.
Erisna is an intuitive, single-view data governance platform that enables organizations to work with high-quality data. With tools for Data Discovery, Cataloging and Validation that can be understood across the business, Erisna enables data teams to drive efficiency and cost savings when working with data.
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