Data Governance for Generative AI: the time is now

Data Governance - Intellico

Index

In the era of digital transformation, data has emerged as the lifeblood of businesses, fueling innovation, driving decision-making, and shaping strategies for growth.
With the advent of Generative AI, companies are poised to unlock unprecedented opportunities for creativity, personalization, and efficiency. However, amidst this transformative potential lies a critical challenge: ensuring robust data governance frameworks to harness the power of Generative AI responsibly and effectively.

As we stand on the brink of this transformative era, a 2023 study conducted by Amazon Web Services and the MIT Chief Data Officer/Information Quality Symposium highlights a critical gap: the survey of 334 CDOs and Data Leaders shows that 80% recognize its transformative potential, but only 6% of companies have successfully implemented generative AI applications in the production environment. Furthermore, most organizations are not yet obtaining substantial economic value from the use of generative AI.

The biggest challenge? Data preparation.

That’s what reports a recent article in the Harvard Business Review.

Preparing a company’s data for generative AI, especially documents not conceived for training models, involves meticulous data governance practices to ensure the data’s quality, relevance, and ethical use, highlighting the strategic importance of robust data governance frameworks.

What’s data governance?

Data governance is a framework that ensures high-quality, secure data management across an organization.
Data governance in a business perspective focuses on maximizing data’s strategic value, enhancing decision-making, and ensuring regulatory compliance.

Meanwhile in an IT perspective, data governance involves defining policies, standards, and practices for data usage, storage, and access, while also implementing technologies to support data quality, privacy, and lifecycle management. This integrated approach facilitates better business outcomes and efficient IT operations.

More in detail, data governance includes three main business-driven tools.

Business Glossary plays a vital role in any organization’s data governance. Business Glossary is a collection of business terms that serve the purpose to align all the organization (including new employees) on the terminology that is used by the organization, and can be used for AI to be more accurate.

Data Catalog, instead, is a centralized repository or dataset that stores metadata and information about an organization’s data asset such as data type, description, data lineage, data quality, data ownership and security classification and can be used to build “Knowledge Graphs” describing the structure of information in the organization, thus providing guardrails to the GenAI engines.

Finally, data lineage ensures that changes in systems or data sources are documented and accounted for in model updates. It provides the tracking and visualization of the flow of data from its origin through various stages of processing to its final destination. It provides a comprehensive view of the data’s journey, including how it was transformed, where it came from, where it moved to, and how it was used. Data lineage is essential for understanding, managing, and maintaining data quality and integrity within an organization and all over time.

Why data-governance for Generative AI?

In retail, AI-generated content can be tailored to individual customer preferences, improving engagement and customer satisfaction.
For example, AI can create personalized email campaigns or product recommendations with the specific tone that aligns with the target, boosting conversion rates. On the other hand, it is important to track and know the last version of applicable catalogues or purchasing conditions in order to avoid spreading misleading and old information.

The same applies to manufacturing. AI can help technicians to query the technical documentation of equipment and devices. In order to be effective and scalable the knowledge base should be structured , definitions clearly stated so that the AI engine can recognize the latest updated documentation to take into account.

These are two examples of how the adoption of frameworks of data governance can also benefit the deployment of generative AI. In the context of Generative AI, data governance can accomplish several benefits:

  1. Data Quality Assurance: High-quality data is the cornerstone of successful AI applications. Data governance frameworks help companies maintain data quality by establishing standards for data collection, preprocessing, and validation. By ensuring that input data for Generative AI models is accurate, relevant, and representative, organizations can enhance the reliability and performance of their AI systems.
  1. Ethical and Regulatory Compliance: As AI technologies continue to evolve, concerns surrounding data privacy, bias, and fairness have come to the forefront. Data governance frameworks provide mechanisms for ensuring ethical and regulatory compliance in AI development and deployment. By integrating principles such as transparency, accountability, and fairness into their data governance policies, companies can mitigate risks and build trust with stakeholders.
  1. Risk Management and Security: The proliferation of data-driven technologies introduces new risks and vulnerabilities to organizations, ranging from cyber threats to data breaches. Data governance frameworks help companies identify, assess, and mitigate risks associated with Generative AI by implementing robust security controls, access management mechanisms, and data encryption techniques.
  1. Collaboration and Knowledge Sharing: Effective data governance promotes collaboration and knowledge sharing across different teams and departments within an organization by establishing clear roles, responsibilities, and processes for data management.

Conclusions

The integration of generative AI into business operations holds immense potential, but it must be underpinned by robust data governance.

Ensuring high data quality, ethical compliance, bias mitigation, and transparency not only enhances the effectiveness of AI models but also builds trust and accountability. As companies harness the power of generative AI, strong data governance frameworks will be essential in realizing its full benefits, driving innovation, and maintaining a competitive edge in the digital era.

Intellico Group embeds the technological view on AI from Intellico and the data governance skills of doDigital helping companies to implement end-to-end and explainable solutions.

If you want to learn more contact us…

Contributors:

Sara Uboldi, Head of solutions Intellico

Giulio Nicelli, Partner Data Governance doDigital

References:

  1. Is Your Company’s Data Ready for Generative AI? (hbr.org)

Do you need more information?

Fill out the dedicated form to be contacted by one of our experts.