Artificial Intelligence, specifically generative AI, is triggering seismic shifts in the realm of Data Governance, with no signs of slowing down.
Merely half a year has passed since the launch of ChatGPT, yet it seems as if an extensive review is overdue. This article aims to delve into how generative AI is revolutionizing data governance and gives a glimpse of its imminent trajectory. Let’s underscore ‘imminent,’ as the pace of development is brisk and can veer in countless directions. This is not a prophecy of data governance’s centennial journey; rather, it’s a pragmatic assessment of current alterations and those looming just around the corner.
Before we venture further, let’s briefly recap the fundamentals of data governance.
In layman’s terms, data governance can be defined as the guidelines or procedures that an organization adheres to, ensuring the reliability of their data. It primarily revolves around five crucial facets:
- Metadata and Documentation
- Search and Discovery
- Policies and Standards
- Data Privacy and Security
- Data Quality
In this discussion, we will examine how the integration of generative AI is poised to reconfigure each of these facets.
Let’s set the wheels in motion!
Significance of Metadata and Documentation
The foundation of data governance heavily relies on the effective handling of metadata and documentation. The advent of AI introduces a new landscape for generating data context, though it should be noted that human involvement remains critical, particularly in documentation.
Creating context or documenting data comprises two elements. The first one, accounting for roughly 70% of the task, involves documenting general information that is common across organizations. The other part pertains to capturing unique insights specific to an organization.
The fascinating part lies in AI’s capacity to shoulder a significant portion of the general documentation. Generative AI excels in managing general knowledge, which makes it a valuable tool.
However, when dealing with organization-specific knowledge, such as metrics, KPIs, and business definitions, human intervention is irreplaceable. The nuances and specificities of this knowledge are best understood and articulated by those who know the business intimately, the organization’s employees.
Creating a shared understanding of business concepts often involves the collaboration of domain teams. Definitions that best suit a company’s business model are derived from the rigorous discussions and consensus within these teams.
AI, at its current stage of development, lacks the capacity to contribute to these discussions and generate unique concepts. It cannot replace human intuition and insight when it comes to comprehending an organization’s unique business language.
Also, while generative AI can generate content on demand, it cannot completely replace the documentation process for two reasons. Firstly, as previously stated, AI cannot yet capture an organization’s unique aspects, necessitating human expertise. Secondly, the content produced by AI is not always accurate and requires human verification.
Evolution of Search and Discovery Methods
The influence of generative AI extends beyond data creation to consumption. We are witnessing a dramatic shift in search and discovery methodologies, where traditional approaches are becoming obsolete.
AI’s potential to function as a personal data assistant is a game-changer. With enriched data, AI can answer specific data inquiries and facilitate the distribution of context across the organization.
Further advancements may transform the data catalog from a passive entity to an active assistant. With AI, data catalogs could proactively assist users by providing insights and solutions. However, this advancement is contingent on the maintenance of the data catalog. For an AI assistant to provide reliable guidance, the underlying documentation must be trustworthy.
AI and data governance are indeed interdependent. Each component boosts the other, creating a virtuous cycle. However, they are not interchangeable.
Implementation of Data Policies and Standards
Formulating and implementing governance rules is a vital aspect of data governance. This task involves defining data ownership and domains within the organization, a challenge that AI is not currently equipped to handle.
This limitation stems from the fact that defining ownership and domains pertains to human politics, which AI cannot replace. AI may assist in executing rules or flagging infractions but lacks the ability to create the rules themselves.
Reinventing Data Privacy and Security
Generative AI is poised to transform the privacy segment of governance. Managing privacy rights has traditionally been complex and labor-intensive. But AI can automate much of this process, handling the architecture of permissions and managing Personally Identifiable Information (PII) more efficiently and accurately. However, despite these advancements, human oversight remains crucial for managing unforeseen circumstances and making necessary judgment calls.
Maintaining Data Quality
Data quality, an essential pillar of governance, ensures accuracy, consistency, and reliability of a company’s information. Generative AI is reshaping the way data quality is maintained by applying rules and identifying anomalies. Despite these advancements, maintaining data quality still requires a no-code approach to express data quality checks and human validation.
Conclusion
AI is paving the way for a paradigm shift in Data Governance. However, AI cannot fix a flawed system. For AI to revolutionize the search and discovery experience, organizations must maintain their documentation. Also, even if AI can generate the context around data, it cannot replace human involvement entirely. Therefore, the future of governance can be seen as a blend of AI-powered processes grounded in human discernment and cognition.