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Generative AI Ready Data: Essential Steps for Data Leaders

Generative Artificial Intelligence (GenAI) is no longer just a buzzword; it’s transforming businesses across the globe. But here’s the kicker – your generative AI model is only as good as the data it’s fed. So, how do you ensure your data is ready for AI? Let’s dive into the world of AI-readiness and discover how to keep your data ahead of the curve.

From Data Drudgery to AI Mastery: Why AI-Ready Data is Key

AI readiness is not just a technical requirement; it’s a strategic necessity. It involves proving that the data is suitable for AI applications by continuously assessing and aligning it with specific AI requirements. There’s no one-size-fits-all approach because the context of each use case determines what qualifies as AI-ready data. This shift in traditional data management practices to more dynamic, AI-focused approaches is essential to ensure that the data effectively supports AI technologies, leading to more reliable and impactful outcomes.

Delivering AI-ready data is crucial for unlocking AI’s business value. Enterprises investing in AI at scale must evolve their data management capabilities to meet AI’s unique demands. This evolution involves maintaining the fundamental principles of data management and extending them to accommodate AI. When data is AI-ready, it supports the iterative provision of high-quality data, ensuring that AI initiatives can meet both current and future business needs. This adaptability is essential for maintaining user trust, preserving intellectual property, and eliminating issues such as bias and errors in AI outputs.

Don’t Let Your Data Be the Weakest Link

Today, most organizations typically do not develop in-house LLMs but opt to deploy AI models from external vendors. These models were trained on internet data, lacking the domain-specific context of your business. To make them provide organization-specific answers, you must retrain these models with your internal data. Beyond the quality and build of the AI model itself, these AI solutions depend heavily on the availability of your data, as well as its quality and understanding. Yet, enterprises often dive into AI implementation projects without considering critical data management issues. This, in turn, leads to disappointment in the models’ performance and even to failed GenAI deployments. To support Generative AI endeavors, classical data and analytics management must undergo a makeover. 

We’re entering an era of augmented data management practices better suited to meet AI’s data requirements. For instance, using a knowledge graph-based intermediation as part of your data fabric can enhance LLM-led queries by providing contextual insights and boosting the accuracy of generated results. New ways of managing data also address specific hurdles related to AI, such as bias and hallucinations. These challenges, which arise from how data is interpreted, labeled, and influenced by human actions (aka conflicting definitions), require strong systems and analyses to reduce their impact. 

Mind the Gap: Overcoming AI and Data Management Disconnects

Embracing AI has its perks, but it’s not without its hurdles. One big challenge is the gap between AI and traditional data management. Often, the AI community isn’t fully aware of the capabilities and tools available in data management, leading to issues as prototypes move to production at scale. And while there has been gradual improvement, traditional data management may still overlook AI-specific needs, such as data bias, labeling, and drift.

Bridging this gap means integrating AI considerations into every step of your data management process. It’s about ongoing and new governance approaches tailored to AI and ensuring that data management activities don’t stop once the AI model is up and running. Keeping your data relevant and reliable to ensure the success of your AI projects isn’t a one-time deal; it’s a continuous commitment.

Data Makeover: Transforming Your Data into AI-Ready Assets

So, how do you get your data AI-ready? Start by making AI readiness a cornerstone of your data management strategy. This means implementing active metadata management, data observability, and data fabric as foundational elements. Next, adopt a data-centric approach to AI model development. AI models rely heavily on good data, so diversifying data sources, models, and teams is key to ensuring ethical and valuable outcomes. Leverage your data management expertise and bring AI engineering, DataOps, and MLOps to support the entire AI lifecycle.

Establishing data fitness policies is also crucial. Defining and measuring minimum data standards for AI readiness early and continuously proving data fitness to help maintain the integrity of AI initiatives. This means regularly checking data lineage, quality, governance, versioning, and running automated tests to ensure your data meets the high standards required for AI.

Lastly, keep an eye on advanced data management tools that can support generative AI deployments by addressing data-centric challenges. Look for those rich in augmented capabilities that integrate seamlessly with AI tools and promote effective data management practices.

Moving Away from Data Chaos

illumex was named as a Sample Vendor for the AI-Ready Data technology in the Gartner® Hype Cycle™ for Artificial Intelligence, 2024 report.

Want to ensure your data is AI-ready and primed for generative AI applications? With illumex, your organization can navigate the complexities of unifying business data semantics with ease. illumex’s  Generative Semantic Fabric automatically finds, maps, and adds meaning and business context to your structured enterprise data.

Never touching or moving your data, illumex looks at metadata to create a unified knowledge graph that acts as a single source of truth and serves as a translator for business user input. It understands “business” natural language, which may not necessarily match the database semantics, and aligns it with existing business logic and terminology to provide deterministic and governed answers. Furthermore, it establishes an automated orchestration framework for any data or LLM runtime. It serves as a seamless, automated substitute for the labor-intensive RAG/GraphRAG process, allowing for GenAI prompt explainability, GenAI governance (including verifiable and well-explained GenAI responses), and preventing specific lock-in of LLM.

Want to see Illumex in action? Contact us today for a live demo. Learn how to get your data AI-ready and transform your AI deployment strategy.

* Gartner, Hype Cycle for Artificial Intelligence, 2024, By Afraz Jaffri, Haritha Khandabattu, 17 June 2024. GARTNER is a registered trademark and service mark of Gartner, Inc. and/or its affiliates in the U.S. and internationally, and HYPE CYCLE is a registered trademark of Gartner, Inc. and/or its affiliates and are used herein with permission. All rights reserved. Gartner does not endorse any vendor, product or service depicted in its research publications, and does not advise technology users to select only those vendors with the highest ratings or other designation. Gartner research publications consist of the opinions of Gartner’s research organization and should not be construed as statements of fact. Gartner disclaims all warranties, expressed or implied, with respect to this research, including any warranties of merchantability or fitness for a particular purpose.

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