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Mastering Data Governance in a Dynamic AI Landscape

Experts suggest that the global AI market will reach an astonishing value of $190.61 billion by 2025. Every day, AI brings new innovations, along with the need for new standards, regulations, and quality controls. So how do we navigate these challenges? Is it even possible to keep up with the changing landscape?

Here’s how AI is reshaping industries and redefining our approach to data governance.


AI in the Corporate World

AI can do a lot, from being a personal assistant to making real-time decisions. So it’s no wonder that it fits perfectly into the corporate world.

Let’s talk numbers: 92% of large companies have seen great returns on their AI investments and will continue to boost their investments in AI in the coming years.

This is not just about keeping up with the trends. Companies are using AI in several aspects of their business, from coding to data governance. Leveraging AI can transform efficiency and precision in ways that cannot be matched by traditional methods.


Role of AI in Data Governance

Data governance is the process of establishing an organization-wide framework for managing data. It makes sure the organization’s data is managed well. It also keeps it secure, reliable, and accessible. 

With the arrival of AI, there has been a sea of change in data management and governance. AI is now being used extensively to improve various data-related efforts, such as:

  • Data tagging: AI is highly effective in data classification and labeling. This is extremely useful in environments where manual categorization can be time-consuming and unfeasible.
  • Data cleanup: AI can spot errors, outliers, duplicates, and data discrepancies much easier than traditional methods. This helps in keeping data accurate and polished, thus improving the quality of the data.
  • Analytics: AI can sift through mountains of data and spot patterns and insights that we mere humans simply cannot find. This helps businesses foresee trends and take action accordingly.
  • Decision-making: AI can provide deep insights and predictive analytics, improving decision-making quality. It can create data models of hypothetical situations, which can provide empirical evidence to make informed decisions.
  • Automation: AI is most extensively used for automating tasks like report generation and compliance checks, thus freeing our time for more strategic work. This not only improves our productivity but also reduces the possibility of error.
  • In a way, AI can be considered a brilliant colleague who makes your tasks easier for you.


Challenges in Data Governance

While AI revolutionizes the way we deal with data and data governance, it isn’t without its hurdles.

  • Security is a major cause of concern. Security flaws can make AI/ML systems vulnerable to attacks and malfunctions.
  • Also, maintaining high-quality, meaningful data is of utmost importance. Making sense of bad data may lead to a lack of context and poor judgment.
  • When the training data is contaminated, it can affect the output of an AI system. Biased training data can lead to unfair results.

Let’s consider the case of Microsoft’s AI Chatbot, Tay. This was a cautionary tale of an AI learning from the wrong sources, leading to an absolute PR nightmare. This bot was developed by Microsoft to engage with people on Twitter. In less than a day, the bot trained itself to write offensive tweets, trained by thousands of misogynistic and racist tweets it received from trolls.

The onset of the 2020 pandemic was also the time when AI innovation was at its peak. 

AI-enabled medical teams across the globe strived to use AI to understand and forecast the spread of the emergent disease. One such algorithm, used by the US healthcare system, incorrectly concluded that Black patients are healthier than equally sick White patients by correlating historical health-related expenses to health needs. As a result, Black patients received less medical care, despite having the same risk as White patients.


Ensuring the Efficiency of AI Systems

With such inaccuracies and high risks, can we even rely on AI?

Yes. I do believe that it is possible. With some conditions, of course.

  • First and foremost, data quality is paramount.
  • Next, we should maintain and manage this high-quality data to ensure easy accessibility while prioritizing data security. 
  • Finally, we need to strike a balance between accessibility, privacy, and regulatory compliance.

Periodic output checks paired with quick response teams to fix issues can ensure the high efficiency of AI-based systems.

Proper implementation of AI-based systems brings excellent results

The Rizzoli Orthopedic Institute uses advanced analytics to gain a more granular understanding of clinical variations of symptoms within families. With clean, well-defined data, they were able to gather insights that led to reduced annual hospitalizations and imaging tests.


The Road Ahead

Despite AI’s advancements, the human element remains irreplaceable. Even after all these years, we can’t fully rely on AI. It is our responsibility to ensure that the systems are functioning as intended while adhering to ethical standards.

The GDPR mandates human intervention in all instances of automated decision-making. Other regulatory bodies are also following suit.

So, while we can let AI do the brunt of the work, the onus will still lie on us to act on the results.

As we adopt AI more and more, data governance for AI is no longer an option but a necessity. As leaders in data, we must smoothly and responsibly guide this integration. We should continuously inspect, reflect, and evolve. 

If you are eager to delve deeper into data governance in the realm of AI, I invite you to join the Data Leaders Network.

This community is an invaluable resource for data and AI leaders to…

  • Discuss, learn, and network with peers and experts
  • Gain insights into the latest tools, trends, and best practices
  • Connect with regulatory bodies and organizations that deal with governance laws
  • Attend roundtables and events on advancements in the data sphere

Presently, the Data Leaders Network is free to join, so make the most of this opportunity. We have a lot planned for the network, and I truly believe that this can be a foundation for exciting collaborations to come.

So, are you ready to elevate your data governance? Join the Data Leaders Network, and let’s shape the future of data governance together!