Generative AI & Data Governance

Importance of Data Governance

Before diving into Generative AI GenAI), consider the importance of data governance. Imagine deploying GenAI without it—it’s like navigating without a compass. While companies will share foundational GenAI models, the differentiator will be their data and how they leverage it. Data strategy is essential for success in GenAI.

Data Governance: Beyond the Buzzword

Data governance may seem daunting, with its regulatory constraints and complex processes. However, the benefits are clear: a common vocabulary, increased data confidence, and efficient habits.

A Retailer’s Journey

A leading retailer implemented data governance before adopting advanced analytics, AI, and GenAI. Initially, they focused on data initiatives across businesses but soon realised the importance of data trustworthiness and quality for reliable insights and sound decision-making.

Benefits of Decentralised Data Governance

A decentralised data governance program empowers business lines through:

• Data acculturation programs to educate employees.
• Seamless access to data heritage for context.
• Learning from mistakes to prevent future errors.

Generative AI: The New Frontier

GenAI is promising, but what sets companies apart? The answer is data. Personalising and enriching models requires data mastery and governance. If humans struggle to detect data quality issues, how can algorithms judge data accurately? Techniques like fine-tuning and RAG rely on qualified, quality-controlled data.

Building a Data Governance and Generative AI Fortress

The key to combining GenAI and data governance is agility:

• Evaluate and monitor different models effectively.
• Enrich data to adapt to specific contexts and needs.

This transforms generic solutions into tailor-made tools. Data remains the fuel for GenAI, and measuring its quality becomes crucial with components like chunking and vectorisation.

The Master Asset: External Data Enrichment

For innovative companies, the future lies in integrating external data.

Retail Sector Example

Businesses can leverage external socio-demographic data to understand customers better and tailor offerings. Enriching product knowledge, from raw materials to transport, helps respond to demanding consumers who ask specific questions. This external enrichment strategy provides a competitive advantage, driving innovation.

Conclusion

In short, the intersection of generative AI and data governance is crucial. It builds confidence in the information and services delivered. As with any quest, tools extend our know-how. Our ability to govern, enrich, and value data will determine our success in this adventure.