More and more, data and analytics leaders around the world are seeking ways to transform data access and reduce the technical skills barrier using generative AI. 

Three main benefits and uses of generative AI for data

Generative AI is transforming data management activities through natural language interfaces, making data management and analytics more widely accessible. Integration with metadata management tools will serve to increase future productivity and cost optimization and lower the barrier of entry for data management positions.

1. Metadata discovery & documentation

Generative AI and language learning models (LLMs) bring a new approach to extending augmented metadata management capabilities that can help extract semantic meaning and identify context in data usage. These capabilities for metadata discovery and knowledge-building are emerging for multiple use cases, including:

• Supporting a data catalog
• Data governance
• Increasing data quality
• Enterprise knowledge management
• Participation in a data fabric structure

GenAI can also be used to generate data management code documentation for queries or data pipelines, making it easier to maintain the overall data management landscape. Data & analytics leaders considering working with generative AI products should remember to always:

• Evaluate the resources and skills supporting human intervention in the process and the ability to leverage specific industry knowledge
• Test documentation generation capabilities as needed and assess their overall impact on your data management teams

2. Data exploration & code generation

LLM code generation capabilities will transform the way we interact with data, and software vendors are increasingly fine-tuning these LLMs to support enterprise use cases.

• Human-centric interfaces & self-service data: The main benefit identified of these capabilities is to empower any user to interact with data. When combined with graphic generation and data visualization, these capabilities can transform the entire data analytics process.
• Code generation & correction: GenAI can help data professionals create and identify errors in the coding used to organize data and metadata. This code generation allows a new generation of data engineers with increased productivity and a reduced barrier to entry for data jobs. Of course, standard base code knowledge will be needed to ensure there is no logical error or issue in the code generated over time.

3. Generative AI for administration, optimization, and operational activities

GenAI can be particularly useful in activities that require a more natural language approach to finding and organizing information, including administrative and operational work with everything from data pipelines to system health monitoring.

While users will welcome all of these capabilities regardless of their skill level, the capabilities will impact only the user experience. They won’t fundamentally change the way data management is being operated.

However, over time, it can be expected that, in combination with other AI techniques and code-generation capabilities, much more of the administration and deployment will be automated, leading to self-healing, self-tuning, and cost-optimized systems.