Data Management with GenAI: How to Handle Structured Data

Generative AI (GenAI) is a transformative force for data leaders. It’s projected to make managing structured data easier by taking complex tasks and simplifying them, all while reducing skill gaps and driving productivity. 

In the next 5 to 10 years, natural language interfaces could become the standard for managing data. That means interacting with your data systems will be as simple as talking or typing in plain English. Companies that embrace GenAI now are positioning themselves to become the leaders in this data-driven future, gaining an edge with greater efficiency and innovation.

How GenAI is Redefining Structured Data Management

Industry analysts estimate that by 2026, data management tool providers who fail to leverage GenAI could see a 10% annual revenue drop. At the same time, GenAI is projected to slash manual data management costs by up to 20% each year while also enabling four times as many new use cases. These numbers show just how much impact GenAI will have on data management, signaling a profound shift in the field.

For over a year, data and AI leaders have been exploring GenAI use cases. They’ve discovered incredible value—but also hit a few tough roadblocks. A recent McKinsey survey revealed that 70% of top-performing companies struggled with integrating data into AI models, citing issues like data quality, governance, and insufficient training data. These challenges highlight one thing: to fully leverage GenAI at scale, you need a solid grasp of how your data capabilities are evolving.

No More Gatekeepers: Data Management in Plain English

GenAI plays a key role in making data management more accessible and efficient. It integrates with existing metadata management tools and adds an extra layer of semantic enrichment, making data management more democratic and open to a broader range of users within the organization. The result is more productivity and faster, more innovative implementations of data use cases. Natural language interfaces make data tasks accessible to a broader range of professionals, cutting down the need for deep technical expertise.

The impact on business operations is already visible. According to Gartner, 23% of organizations that have implemented GenAI report a significant reduction in the workload of data engineers, data stewards, and DBAs. When natural language becomes the primary mode of interaction with data systems, it transforms organizational models by making advanced data management techniques accessible to a broader audience. Data stewards, for example, can now express data quality rules in plain language and automate the corresponding scripts. Game. Changer.

Let GenAI Handle the Tedious Use Cases—Focus on Innovation

GenAI can also accelerate innovation through improved implementation of data management use cases. By breaking down skill barriers it opens up data management to a broader range of roles within an organization, fostering a collaborative environment. Automating routine tasks is a key advantage. Whether it’s supporting data cataloging and metadata management or enhancing governance and quality, GenAI drives productivity across the board.

But the magic doesn’t stop there. GenAI also steps up in more advanced tasks like enterprise knowledge management, taxonomy-to-ontology mapping, and the augmentation of data fabric. It streamlines self-service semantic interaction, automates code generation, description, and correction, and also optimizes queries for better performance and cost savings. GenAI even helps with data migration code generation and infrastructure-as-code management. In short, GenAI efficiently handles the mundane, freeing up data specialists to focus on the higher-value tasks.

GenAI Implementation Woes: The Devil’s in the Data

Of course, every superpower has its kryptonite. GenAI, for all its potential, does come with challenges. First up: data quality. Traditional methods often fall short when dealing with the complexity of structured datasets. For example, trying to process combinations of structured financial information and unstructured data like financial statements and market analyses often leads to errors. This is because internal teams struggle to codify their knowledge for replication in data pipeline processes.

Another hurdle is integrating GenAI with existing data systems. Many organizations have poor data lineage or cataloging, which limits their understanding of how their data is generated and processed. This lack of clarity can hold GenAI back, as training and utilizing AI models rely on accurate, comprehensive data.

Security and governance issues also pose substantial challenges. Integrating GenAI with enterprise data requires strong governance frameworks to maintain data integrity and security. Plus, the potential for GenAI to spit out incorrect responses means human oversight is still critical. Constant review and accuracy checks are essential—but they can be resource-intensive.

Best GenAI Diet: Clean Data and Good Governance

The success of Generative AI heavily depends on the quality of the data it processes. Implementing advanced semantic mapping techniques can be a game changer when dealing with structured data. These techniques automatically interpret and standardize data from different sources, ensuring it is organized in a consistent and meaningful way.

This process is crucial because it significantly reduces errors and inconsistencies, which are common issues when data is pulled from multiple systems. Clean and well-mapped data creates a solid foundation for more accurate and reliable AI outputs. In turn, this enhances the overall performance of your AI models. Better data quality leads to sharper insights and more effective decision-making.

Another essential aspect of making GenAI work effectively with structured data is embedding augmented AI governance and security directly into your data workflows. This isn’t just about setting rules but ensuring they are automatically followed. Automated governance frameworks can document, certify, and trace every interaction with your data. 

Such level of oversight maintains data integrity and helps ensure compliance with regulations. Additionally, integrating automated security measures, such as tagging Personally Identifiable Information (PII) and other sensitive data and setting up access controls, provides another layer of protection. These steps help guarantee that your GenAI models operate within a secure and compliant environment, safeguarding your data and minimizing the risk of breaches or unauthorized access.

Silo Busting and Metadata Mastery for GenAI Efficiency

Structured data often lives in silos—isolated in different systems across an organization. These silos can make it difficult to get a complete picture, as the data isn’t easily combined or compared. This is where industry-specific semantic AI can make a significant impact. 

By standardizing and aligning data across silos, your AI models can understand the unique language and context of your industry. This leads to more precise, context-aware insights. Breaking down these silos doesn’t just improve data integration—it enables more comprehensive and accurate decision-making. When data flows freely across your organization, AI models can provide insights that are relevant and deeply informed by your industry’s specific nuances.

Active metadata management is another critical component for boosting the efficiency of your GenAI data management use cases. Metadata, essentially data about your data, must be actively managed by continuously monitoring, updating, and aligning it across all your systems. This ensures that your structured data remains current, accurate, and contextually relevant. 

Active metadata management reduces the need for manual updates and keeps your GenAI models running with the most up-to-date information. This proactive approach lightens the maintenance load and ensures that AI outputs are based on the latest and most accurate data available.

The Future of Data is Here—Are You Ready?

While using GenAI for data management presents some challenges—such as data quality, integration, and governance—they are not roadblocks. Advanced semantic mapping ensures clean, standardized data, improving AI accuracy. Active metadata management keeps data current, minimizing integration hurdles. And automated governance frameworks, coupled with built-in security measures, address compliance and safeguard data.

With these solutions, the potential of GenAI for data management far outweighs the obstacles. By overcoming these challenges, organizations can unlock GenAI’s full power, leading to increased efficiency, innovation, and more precise decision-making.

The key is acting now. Integrate GenAI into your data management strategy and witness the transformative impact it can have on your business.