Why Effective Data Management is Key to Meeting Rising GenAI Demands

OpenAI’s ChatGPT release less than two years ago launched generative AI (GenAI) into the mainstream, with both enterprises and consumers discovering new ways to use it every day. For organizations, it’s unlocking opportunities to deliver more exceptional experiences to customers, enabling new types of applications that are adaptive, context-aware, and hyper-personalized.

While the possibilities are exciting, IT departments are facing new obstacles as GenAI puts strain on existing infrastructure and demands new resources to sustain data growth and meet modernization goals. Recent research revealed that 98% of enterprises have specific goals to use GenAI in 2024, and it will account for almost a third of all digital modernization spending in 2023 and 2024, or $21 million per enterprise. However, on average, survey respondents did not have faith in their current IT infrastructure being able to support in-house GenAI applications within a span of just 19 months. And an alarming 59% of IT decision-makers are worried that without significant investment, their organizations’ data management capabilities will not meet the demands of GenAI.

Critical Data Management Capabilities to Support GenAI Application Development

More than half (54%) of organizations admit they do not have all of the elements in place to ensure an all-encompassing data strategy that’s built for GenAI. IT leaders must understand the requirements for building a data management strategy that meets GenAI’s data demands. These include: 

• Having control over data storage, access and use
• The ability to access, share and use data in real-time
• Using vector search to enhance GenAI performance
• Consolidating database infrastructure to prevent applications from accessing multiple data versions

Enterprises must have full authority over data storage, utilization and access control to ensure the safe usage of GenAI. If organizations lack the capability to efficiently share and use data with minimal delay, they will struggle to satisfy the performance requirements of their applications.

As more companies integrate artificial intelligence into applications that converse with large language models (LLMs), semantic search functionalities powered by vector search and further enhanced by retrieval-augmented generation (RAG) are essential for mitigating inaccurate outputs, or “hallucinations,” and ensuring more reliable responses. Currently, only 18% of enterprises have a vector database capable of efficiently storing, managing and indexing vector data.

The study showed that two-thirds (66%) of organizations believe database modernization is necessary for in-house GenAI application support. Yet, many IT leaders believe they need multiple databases to get access to critical functionalities. The use of multiple point solution databases can introduce complexity and risk into applications, making it difficult to trace the source of AI hallucinations. By keeping data in a single multipurpose database with vector search capabilities and real-time analytics, developers can create a cleaner, safer and more efficient environment for AI applications to operate within. 

Investing in Developers Is Key to Innovation

An organization’s consumers and employees alike are demanding more seamless and tailored experiences. A majority of organizations (61%) feel under pressure to constantly update their end-user experiences. Organizations that fail to cater to end-user needs are at risk of losing business and talent, falling behind the competition. And eventually, the applications will no longer serve their purpose without the necessary innovation. The study found that the most critical characteristic for consumer-facing applications is adaptability. Adaptive applications can dynamically adjust the content and offerings presented to users based on context and their individual needs, ensuring a hyper-personalized experience.

With developers at the core of application modernization, organizations must prioritize giving them the tools they need to work more effectively and create new GenAI-powered applications faster. It’s clear AI is already making developers more productive. Looking forward, AI-driven coding tools, a multipurpose database and capabilities such as vector search, RAG and real-time analytics could accelerate and facilitate the development process for the next wave of applications to come.