Although generative AI (Gen AI) is seen more as a content creation tool, it can help with data governance in a multitude of ways, including with data quality, data integrity, data standardization, data compliance, and data accuracy.
The history of AI in general and Gen AI in particular can best be summed up in quote by the American broadcast journalist and war correspondent, Edward R. Murrow, who said, “There are decades where nothing happens; and there are weeks where decades happen.” After seven decades of research, two AI winters, an AI renaissance in the eighties, followed by three decades of impressive hardware and software technological breakthroughs, AI is finally having its day in the sun.
Gen AI apps can help with content creation, customer personalization, automation, content repurposing, as well as assist with coding. It enhances data governance by automating processes, improving data quality, and ensuring corporate and legal compliance.
Enterprise AI Adoption
80%+
Enterprises Expected to Use Gen AI by 2025
According to Gartner’s October 11, 2023 press release, more than 80% of enterprises will have used generative AI APIs or deployed generative AI-enabled applications within two years.
As I explain in my article, Data Governance in Business Intelligence and Analytics
What is Data Governance?
As I explains in his article, Some Best Practices in Data Governance
The key components of data governance include:
Data quality
Data integrity
Data standardization
Data security/data privacy
Metadata management
Compliance
Audit capabilities
Change management
These components collectively support an effective data governance framework, enabling organizations to manage their data assets both strategically and responsibly.
The Role of Gen AI in Data Governance
Gen AI refers to a subset of AI that focuses on creating new content and data, including text, images, audio, and videos, often in response to user prompts. Gen AI is capable of producing various types of content by learning patterns from existing data. It utilizes advanced machine learning models, particularly generative models, to produce outputs that resemble the training data. This technology can create anything from written stories and poems to realistic images and videos.
Although Gen AI is seen as a content creation tool, it can play a significant role in data governance by enhancing various aspects of data management and compliance. Gen AI can automate processes such as data labeling, profiling, and classification. It reduces manual effort while minimizing human error, which should improve data governance efficiency and accuracy.
Gen AI data governance. Source: https://www.linkedin.com/pulse/data-governance-frameworks-generative-ai-dr-rabi-prasad-bwfkc By identifying patterns and anomalies within large datasets, Gen AI enhances the reliability and consistency of the data used for decision-making, ensuring that all insights are based on high-quality information.
Gen AI can also facilitate compliance with regulations like GDPR and CCPA through automated audits. It continuously monitors data handling practices and implements policies that govern data usage, helping organizations adhere to every changing legal standard.
By automating routine tasks, Gen AI can free up resources for strategic initiatives. Organizations can then focus on innovation rather than getting bogged down by manual data management processes.
Growing Use Cases
In its October 11, 2023 press release, Gartner claimed more than 80% of enterprises will have used generative AI (Gen AI) APIs or deployed generative AI-enabled applications within two years. In the release, Arun Chandrasekaran, Distinguished VP Analyst at Gartner, added, “Generative AI has become a top priority for the C-suite and has sparked tremendous innovation in new tools beyond foundation models.” Gartner sees demand substantially increasing for generative AI in a multitude of sectors, including healthcare, life sciences, legal, financial services and the public sector.
The Evolution of Data Management
Whereas traditional data management focused on data storage in databases and data warehouses without much emphasis on quality or governance, modern data governance frameworks emphasize a comprehensive approach that integrates data quality, security, privacy, and compliance into one overall data strategy. Today’s data management applies a more holistic approach than in the past. There is a strong focus on ensuring high data quality through automated processes for data cleansing, profiling, and monitoring data integrity.
Today, regulatory compliance can’t be an afterthought. Organizations must be proactive about managing compliance. On top of that, international organizations must deal with a patchwork of disparate rules and regulations depending on the territory the business wants to operate in.
The use of advanced technologies, including Gen AI, can automate routine tasks while enhancing the efficiency of the data governance process. Modern data governance frameworks are designed to be adaptive. They allow organizations to respond quickly to changes in regulations or because of changing business needs. There is an increasing emphasis on ethical considerations in data usage, including bias detection and transparency in decision-making processes.
Benefits of Integrating Gen AI with Data Governance
There are many benefits of integrating Gen AI into data governance. Gen AI’s ability to identify patterns and anomalies in data can improve data accuracy and reliability. Gen AI-powered compliance management systems can automate the monitoring of regulatory requirements and facilitate real-time compliance checks. Natural Language Processing (NLP) can analyze corporate contracts, emails, and other textual data to ensure compliance with legal standards. Gen AI algorithms can identify unusual patterns in data that may indicate compliance issues or financial risks.
Generative AI’s can streamline data management processes and automate tasks like data labeling, profiling, and classification. Gen AI can also help streamline data management processes, including audits. It automates various data management tasks such as data labeling, profiling, and classification. This automation not only saves time but also enhances accuracy by reducing human error. Streamlining data management processes through automation allows the company to focus on strategic initiatives.
In their article, How Real-World Enterprises are Leveraging Generative AI
Gen AI plays a crucial role in detecting and preventing fraud in the financial sector, claim Saftler et al . “By analyzing transaction patterns, customer behavior, and other relevant data, these AI models can identify anomalies and potential fraudulent activities, enabling financial institutions to proactively mitigate risks and protect their customers,” they add.
Reducing Bias
One of the biggest problems inherent in an IT system is bias in the data. This can render analytical models useless. “Junk in, Junk out,” as many modelers will tell you. Gen AI can include mechanisms that detect and address potential biases in a dataset, promoting ethical practices in AI usage. This has the added benefit of fostering trust among stakeholders.
By identifying patterns and anomalies in large datasets, Gen AI helps ensure that the data used for decision-making is clean, consistent, and reliable. This is crucial for building models and generating accurate insights. This goes a long way in helping maintain the integrity of a data-driven operation.
Robust Gen AI governance can incorporate bias detection mechanisms to ensure any data used for training models is fair and unbiased. This is essential for promoting ethical AI practices and building trust among stakeholders.
Overall, Gen AI can facilitate transparency in corporate decision-making processes because this provides insights into how data is collected, processed, and utilized. This transparency is vital for fostering trust in AI systems as well as ensuring the AI system’s accountability.
By providing insights into how data is collected, processed, and utilized, Gen AI fosters transparency in a company’s decision-making processes. This can be crucial for data governance accountability. Gen AI supports agile governance models that can dynamically respond in real-time to changes in the data environments. This helps organizations manage emerging data risks.
Challenges Addressed by Gen AI in Data Governance
In a February 28, 2024 press release , Gartner predicted that “By 2027, 80% of data and analytics (D&A) governance initiatives will fail due to a lack of a real or manufactured crisis.” Chief Data and Analytics Officers (DCAO) “should stop taking a center-out, command-and-control approach to D&A governance, and instead, rescope their governance to target tangible business outcomes, make it sensitive to opportunity and risk, and agile and scalable as their organization evolves,” said Saul Judah, VP Analyst at Gartner. As the adoption of AI and Gen AI accelerates, Judah recommends CDAOs update their D&A governance practices to include AI.
At the same time, CDAOs should incorporate AI and Gen AI-enabled capabilities to their D&A governance program. Gartner predicts that by 2027, the application of Gen AI will accelerate time to value of D&A governance and master data management programs by 40%.
“Before CDAOs embark on delivering GenAI use cases, they must ensure their organization’s core, genetic information is well governed. For this, they should use and prioritize GenAI capabilities that would lead to faster time to value for their governance program,” said Anurag Raj , Sr Principal Analyst at Gartner. “GenAI capabilities can help in this through ramping productivity in governance activities such as cataloguing and classification, broader and easier adoption (e.g., better self-service capabilities), or capabilities that solve specific business challenges, such as enriching customer data for better targeting,” he added.
Data & Analytics Governance
80%
D&A Governance Initiatives Expected to Fail by 2027
In a February 28, 2024 press release, Gartner predicted that “By 2027, 80% of data and analytics (D&A) governance initiatives will fail due to a lack of a real or manufactured crisis.”
Conclusion
Although it took AI and Gen AI decades to reach its potential, it isn’t going anywhere anytime soon. Gen AI can significantly transform data governance by improving efficiency, quality, compliance, and adaptability in managing an organization’s data.
Today’s data management requires a holistic approach. The strong focus on ensuring high data quality through automated processes for cleansing, profiling, and monitoring data integrity almost requires Gen AI. This evolution signifies a shift from reactive and siloed practices to proactive, integrated, and technology-driven ones.
Organizations that haven’t planned to adopt Gen AI tools to strengthen their governance practices should be concerned. Technology is a double-edged sword. While it can either empower you, it can also leave you in the dust.
Gen AI coupled with data governance has the power to help businesses maximize the value of their data assets. With the rapid evolution of data environments, Gen AI can make organizations more agile. Their data governance frameworks can quickly adapt to the changing business landscape. This includes dynamically applying policies to relevant data in real-time, which is particularly important in fast-paced industries. By automating routine governance tasks, Gen AI frees up resources for innovation. Organizations can focus on strategic initiatives rather than getting bogged down by manual data management processes. Data governance provides the right data to the right people at the right time for the right purpose in the right format with the right usage controls. Gen AI might just be the right tool delivered at the just right time, even if it took a while to get here.
