What AI Governance Means For CDOs In 2024
Why AI governance challenges demand proactive leadership from CDOs in 2024.
In 2024, CDOs face mounting pressures as AI reshapes data governance. Helena Schwenk, VP at Exasol, explores the imperative of AI governance, its challenges, and strategic solutions for CDOs.
Effective data governance is top of mind for many executives in our current data-driven business landscape. Why? Organizations have exploding volumes of data at their fingertips. Still, they must properly manage that data’s availability, usability, integrity, and security to ensure accurate and reliable data-driven insights and remain competitive overall.
Unfortunately, many of today’s businesses already struggle to keep pace with some of the most basic data governance needs, lacking the necessary time and resources while facing reduced IT budgets. Succumbing to poor data governance practices can be devastating to these organizations in a number of ways.
Basing decisions on data considered “poor quality” can be damaging, costing organizations up to $12.9 million annually. Inadequate data governance also increases the risk of compromising data security and hinders regulatory compliance with many data privacy and protection laws. This can lead to large fines, data loss, reputational damage, and customer loss.
Effective data governance is more vital than ever, especially with the mass adoption of newer technologies like artificial intelligence (AI).
Adding AI to the Governance Equation
The rapid growth of AI experimentation and adoption has added another layer of complexity to the data governance situation. While successful AI implementation has advanced, experimentation will still be a key theme for many organizations in the years to come.
Those involved with AI experimentation often lead with a “try fast, fail fast” mindset, raising more questions about whether proper governance practices are followed. On top of this, as AI continues to be paired and merged with other technologies like generative AI, we’re witnessing new and more complex models emerge.
As a result, in 2024, we’ll likely see the practice of AI governance become a C-level imperative as businesses seek to leverage the game-changing opportunities it presents while balancing responsible and compliant use. This also means more pressure will be placed on chief data officers (CDOs), often responsible for successful AI implementation across organizations.
See More: How to Navigate Human Error to Ensure Ethical AI Practices
Mounting Pressures For CDOs
AI governance is a collective effort, demanding collaboration across functions – including the CFO, CISO, and CDO – to address AI’s ethical, legal, social, and operational implications. But, ultimately, for CDOs, the responsibility rests squarely on their shoulders, which includes:
- Overseeing ethical and compliant use of AI technologies
- Assessing and understanding the evolving regulatory landscape
- Navigating an increasingly complex technology landscape
Successful AI implementation by CDOs typically involves three steps: 1) exploration and experimentation, 2) development, testing, and integration, and 3) rolling out and governing. Step three is often the most problematic phase because more experience and resources are needed. This responsibility and pressure, combined with the impending introduction of new AI regulations and a quickly evolving regulatory environment, will push some CDOs to their breaking point.
For others, it will underscore the importance of establishing a fully resourced AI governance practice coupled with C-level oversight. This can only be accomplished by securing a commitment from leadership and establishing a team with cross-functional representation, including experts in data science, law, compliance, ethics, and IT. From there, teams must conduct a risk assessment to identify potential risks associated with AI applications and develop AI governance policies covering data privacy, transparency, fairness, accountability, and security. Following this, organizations can implement a structured model lifecycle management process and maintain regular audits and assessments of AI applications while fostering a culture of ethical AI.
This strategic approach driven by CDOs addresses immediate challenges and strengthens the overall case for proactive and well-supported AI governance going forward.
At the end of the day, organizations face many challenges in today’s rapidly changing tech market. It’s no easy task to be a data-driven company that remains fully optimized, effectively governed, and compliant. But the boom in AI has turned many data processes on their heads – including data governance – and will be an important trend to keep an eye on in the years to come. The time is now for businesses to invest in the right tools to stay agile, boost performance, and accelerate insights while keeping governance and compliance in check.
How are CDOs mastering AI governance challenges? Let us know on Facebook, X, and LinkedIn. We’d love to hear from you!
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