Is the Data Governance we have the one we need?

It’s been a long time since organizations are talking about the importance of having a Data Governance Framework. Since digital transformation is about speed and accelerating time to market, one of the most prominent challenges organizations face is the ability to trust their data. Moreover, high-quality data has become a competitive advantage.

Through 2025, 80% of organizations seeking to scale digital business will fail because they do not take a modern approach to data and analytics governance.

Data governance is essential to ensure the quality, security, and usability of the data to improve decision-making, reduce risk, and comply with regulations. The Data Management Association (DAMA) defines data governance as “the exercise of authority and control over the management of data throughout its lifecycle” which includes the following:

  • Defining the data’s ownership and responsibilities.
  • Establishing data quality standards and ensuring compliance with those standards.
  • Protecting the confidentiality, integrity, and availability of data.
  • Ensuring that data is used in a consistent and ethical manner.
  • Providing a framework for decision-making about data

Is the traditional Data Governance approach appropriate for AI-driven organizations?

Gartner answered this question during Data & Analytics Summit in 2022: “Traditional” Data Management Is Too Slow, Too Structured and Too Rigid to AI Teams. Data Management Must Evolve to Meet AI Needs.

I encourage you to dive deep into Gartner’s article, which details why the one-size-fits-all model is no longer enough.

Data governance in the context of AI requires that organizations implement updated frameworks that have a different approach:

  • Holistic: Governing the entire data pipeline with strategies to address the reliability of data sources and algorithms and to monitor the output of these algorithms.
  • Dynamic: it’s not a one-time effort. Policies must monitor and evaluate algorithms and their outcomes. This can help determine the cadence for retraining models to ensure the insights they produce remain relevant.

Moreover, it’s not surprising that Info-Tech’s new 2023 trends report highlights Adaptive Data Governance as one of the nine leading-edge data trends that tech leaders must prepare for over the coming months.

Adaptive Data Governance advocates for a flexible approach, enabling organizations to utilize multiple data governance strategies based on evolving business situations. The goal is to establish governance and make it simpler and more efficient.

Some of the critical aspects of Adaptive Data Governance are:

  • The shift from manual and often sluggish data governance practices towards embracing proactive automation.
  • Faster decision-making and a more collaborative approach towards governance.
  • Processes Automation: Automated Data Governance simplifies the execution across the organization.
  • Agile Data Governance allows faster and more flexible decision-making.

Undoubtfully, Data Governance is a key enabler for AI adoption.


Nicole Halm
Lead Business Developer