How Does GenAI Simplify Data Management Tasks?

Generative AI is capturing the attention of many companies, with chief data officers (CDOs) being tasked with turning discussions into actionable results. The technology’s content-generating algorithms align well with the CDO’s role of converting data into value. However, it also challenges existing data governance and management frameworks.

Generative AI creates content by learning from vast amounts of unstructured data, including text, videos, audio, and code—types of information that many businesses struggle to classify or evaluate. Moreover, data governance is often viewed as inefficient and cumbersome, especially in highly regulated industries or those handling large volumes of personal data. Companies typically either over-allocate resources to the process or neglect it. In short, Generative AI complicates an already challenging process.

Addressing this issue should be a top priority for CDOs. Companies across various sectors are utilizing Generative AI to enhance customer service, automate manual tasks, and unlock new value. However, without updating their data strategies and policies, businesses are left with two difficult choices: either increase manual efforts to ensure new training data meets quality, security, and compliance standards, or proceed without proper governance, risking negative consequences that could lead to halting Generative AI initiatives.

There is, however, a silver lining. The same technology that adds complexity to data governance can also simplify it. Generative AI can automate much of the manual work, such as labeling data for privacy or intellectual property concerns, ensuring it’s used appropriately. By integrating Generative AI into their governance and management processes, companies can transform a burden into an advantage. With AI handling routine tasks, data professionals can focus more on value-driven activities, opening up even more growth opportunities.

The Unstructured Data Challenge

Data governance involves setting and enforcing rules for capturing, storing, and utilizing data while verifying its integrity and quality—building trust in the data. Data management puts these rules into action, ensuring organizations track data origins, control access, and remain aware of any issues, such as privacy or regulatory constraints, that could influence its use.

By embedding Generative AI into data governance and management, businesses can maximize opportunities without bearing the extra burden.

While companies may take varied approaches to data governance and management, one constant has always been structured data. Stored in a standardized format within databases, structured data is easily labeled, classified, and understood, allowing businesses to grasp its key attributes and how it can be used. Information like data lineage, traceability, quality assurance, and flags for personally identifiable information or other concerns are all documented.

In contrast, the unstructured data that fuels Generative AI isn’t neatly organized in databases. It encompasses everything from emails and Word documents to YouTube videos and video game dialogue. While companies may possess this data, they often lack insight into its sources, usage guidelines, and restrictions.

Generative AI relies on massive amounts of unstructured data, and the processes for labeling, classifying, and ensuring its quality are still largely manual. Businesses might already have data management practices in place for internal documents, but understanding and managing this vast data landscape to ensure its quality and compliance in customer-focused operations remains a monumental challenge.

There are also risks, especially concerning data remediation. Manually processing so much unstructured information can lead companies to fall behind in addressing data errors and inconsistencies. This is a concern for any organization but particularly for large, regulated firms.

Generative AI Can Address Its Own Challenges

Fortunately, it doesn’t have to be this way. Generative AI’s strengths—its ability to handle unstructured data and generate content—make it a natural fit for improving the efficiency and effectiveness of data management. Based on our experience, there are six key use cases for applying Generative AI in data management:

1. Generating Metadata Labels: One of the most impactful applications of Generative AI is creating metadata for unstructured data. These labels provide details like data sources, usage rights, and relationships with other content, ensuring that algorithms are trained on appropriate data within the correct context, while adhering to relevant regulations and policies.

2. Annotating Lineage Information: In enterprise IT environments, capturing and maintaining data lineage across systems is typically complex and time-consuming. Generative AI can speed up this process by parsing code and generating preliminary drafts of lineage data, allowing data governance teams to validate the output rather than creating it manually, thereby using their time more effectively.

3. Enhancing Data Quality: Data remediation usually requires significant manual effort, especially when data practices and quality vary across an organization. Generative AI can streamline or even automate tasks like removing duplicate records, standardizing formats, and filling in missing values.

4. Improving Data Cleansing: To ensure consistent and reliable algorithm outputs, Generative AI can be used to synthesize missing training data and eliminate “noise”—data that is corrupted, meaningless, or otherwise unusable. With training and effective prompt engineering, Generative AI can even generate code to address data anomalies, freeing up teams from this manual work.

5. Ensuring Policy Compliance: Generative AI can enhance awareness and enforcement of data policies by powering knowledge bases, compliance checks, and recommendations. The technology can also drive chatbots that provide employees with an interactive, conversational way to explore policies, reducing the need for ad-hoc support and training.

6. Anonymizing Data: Generative AI can transform sensitive or personally identifiable information to maintain confidentiality and privacy while still preserving the data’s utility and integrity.

These applications have a particularly strong impact on data stewards and custodians, who are responsible for maintaining data quality and trust. With Generative AI handling much of the manual, repetitive work, these teams can focus on more strategic, complex, and value-driven activities.

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