Building Data Architecture to Support Generative AI in Processing Both Structured and Unstructured Data
The ability of generative AI to handle unstructured data presents a significant challenge in the current field of artificial intelligence. Traditional data organization methods are primarily designed for structured data, whereas unstructured data, such as chat records, videos, and code, require more flexible and intelligent processing methods. As data types diversify, enterprises must reassess their data architectures to support the smooth implementation of generative AI initiatives.
Data Governance Strategy
Data governance is crucial for ensuring data quality and consistency. Enterprises should prioritize establishing a clear data governance strategy, equipping appropriate personnel, tools, and execution authority to transform data quality challenges into competitive advantages. Forming dedicated task forces or equivalent bodies to study the applications of generative AI and large language models (LLMs) can provide significant competitive benefits.
Data Storage Strategy
Data storage strategy is central to solving data management challenges. Research indicates that over half of stored data is inactive, meaning it is rarely or never accessed. Despite this, enterprises do not want to discard it because of its potential future value. Enterprises should reassess their existing storage capabilities and build modern automated storage architectures that allow easy access and processing of data throughout its lifecycle, thus enhancing data utilization.
Data Quality Strategy
Ensuring data quality is fundamental to the success of generative AI. Enterprises should make high data quality a strategic priority, appoint a Chief Data Officer, and allocate dedicated budgets and resources. Only high-quality data can effectively support AI models and help achieve business objectives.
Measuring Progress
Enterprise leadership should establish clear data assessment standards and success metrics. By regularly evaluating data quality and governance progress, enterprises can timely adjust their strategies to ensure the smooth advancement of generative AI initiatives.
Handling Unstructured Data
Generative AI models have higher requirements for data quality, especially unstructured data. In the next five years, unstructured data is expected to grow at a compound annual growth rate of 25%, making up 90% of new data created. This type of data includes high-resolution videos, complex medical data, genome sequencing, etc. Enterprises need to deploy automated data lifecycle management solutions and utilize AI technologies to extract higher business value.
Supporting Broad Use Cases with Data Architecture
Enterprises should build relevant functions into their existing data architectures, such as vector databases and data preprocessing pipelines, particularly for handling unstructured data. Integrating these functions can significantly enhance data processing efficiency and the broad applicability of AI solutions.
Using AI to Build AI
Generative AI can be used not only for data management but also to accelerate tasks across the data value chain, from data engineering to data governance and analysis. With the help of AI technologies, enterprises can optimize data processing workflows and improve overall data value chain efficiency.
Conclusion
The challenges of generative AI in handling unstructured data require enterprises to reassess their data governance and storage strategies and build modern data architectures. Through efficient data management and quality control, enterprises can fully leverage the potential of generative AI, gaining significant competitive advantages. In this rapidly evolving era, data quality and management capabilities will determine the success and future of enterprises.