Latest news with #GartnerData&AnalyticsSummit
Channel Post MEA
4 days ago
- Business
- Channel Post MEA
Organizations Leveraging Existing Data Management Platforms To Develop GenAI AppS
Gartner predicts that organizations will develop 80% of Generative AI (GenAI) business applications on their existing data management platforms by 2028. This approach will reduce the complexity and time required to deliver these applications by 50%. During the Gartner Data & Analytics Summit taking place in Mumbai this week, Prasad Pore, Sr Director Analyst at Gartner, said, 'Building GenAI business applications today involves integrating large language models (LLMs) with an organization's internal data and adopting rapidly evolving technologies like vector search, metadata management, prompt design and embedding. However, without a unified management approach, adopting these scattered technologies leads to longer delivery times and potential sunk costs for organizations.' As organizations aim to develop GenAI-centric solutions, data management platforms must evolve to integrate new capabilities or services for GenAI development, ensuring AI readiness and successful implementation. Enhancing GenAI Application Deployment With RAG Retrieval-augmented generation (RAG) is becoming a cornerstone for deploying GenAI applications, providing implementation flexibility, enhanced explainability and composability with LLMs. By integrating data from both traditional and non-traditional sources as context, RAG enriches the LLM to support downstream GenAI systems. 'Most LLMs are trained on publicly available data and are not highly effective on their own at solving specific business challenges,' said Pore. 'However, when these LLMs are combined with business-owned datasets using the RAG architectural pattern, their accuracy is significantly enhanced. Semantics, particularly metadata, play a crucial role in this process. Data catalogs can help capture this semantic information, enriching knowledge bases and ensuring the right context and traceability for data used in RAG solutions.' To effectively navigate the complexities of GenAI application deployment, enterprises should consider these key recommendations: Evolve Data Management Platforms: Evaluate whether current data management platforms can be transformed into a RAG-as-a-service platform, replacing stand-alone document/data stores as the knowledge source for business GenAI applications. Evaluate whether current data management platforms can be transformed into a RAG-as-a-service platform, replacing stand-alone document/data stores as the knowledge source for business GenAI applications. Prioritize RAG Technologies: Evaluate and integrate RAG technologies such as vector search, graph and chunking, from existing data management solutions or their ecosystem partners when building GenAI applications. These options are more resilient to technological disruptions and compatible with organizational data. Evaluate and integrate RAG technologies such as vector search, graph and chunking, from existing data management solutions or their ecosystem partners when building GenAI applications. These options are more resilient to technological disruptions and compatible with organizational data. Leverage Metadata for Protection: Enterprises should leverage not only technical metadata, but also operational metadata generated at runtime in data management platforms. This approach helps protect GenAI applications from malicious use, privacy issues and intellectual property leaks. 0 0
Channel Post MEA
04-03-2025
- Business
- Channel Post MEA
Gartner: Poor Data Quality Prohibits Advanced Analytics Deployment
Through 2025, poor data quality will persist as one of the most frequently mentioned challenges prohibiting advanced analytics (e.g., AI) deployment, according to Gartner, Inc. Because of this, data and analytics (D&A) leaders must focus on three interdependent journeys to advance enterprises' AI initiatives. These journeys include business outcomes, D&A capabilities and behavioral change. 'AI continues to drive enterprise planning, with more than half of CEOS believing the technology will most significantly impact their industry over the next three years,' said Carlie Idoine, VP Analyst at Gartner. 'With this in mind, D&A leaders are uniquely positioned to drive maximum impact on business outcomes due to their proximity to this technology.' 'With AI being a primary focus area in organizations, D&A leaders must cut through the hype and focus on investments in trust, adaptability and people,' said Gareth Herschel, VP Analyst at Gartner. Gartner analysts Gareth Herschel and Carlie Idoine on stage at Gartner Data & Analytics Summit in Orlando, Florida. During the opening keynote at the Gartner Data & Analytics Summit taking place here through Wednesday, Gartner analysts discussed three interdependent journeys in-depth to better guide D&A leaders along their AI to Business Outcomes Gartner advises D&A leaders to prioritize value in demonstrating AI's business outcomes. 'Demonstrating the value of AI continues to be a top barrier to implementation,' said Idoine. 'D&A Leaders must focus on building the right trust levels based on context as the first step to demonstrating value.' D&A leaders can take the following actions to best affect business outcomes: Establish trust models: Trusted, high-quality data is key to enabling a data-driven enterprise, yet many AI initiatives fail because of poor data quality. Trust models look at the value and risk of data and provide a trust rating based on lineage and curation. Trusted, high-quality data is key to enabling a data-driven enterprise, yet many AI initiatives fail because of poor data quality. Trust models look at the value and risk of data and provide a trust rating based on lineage and curation. Monetize productivity improvements: D&A leaders must consider the value and competitive impact as it relates to total cost, complexity and risk. D&A leaders must consider the value and competitive impact as it relates to total cost, complexity and risk. Communicate value of D&A: Consider all costs, including data management, governance, and change management. Journey to D&A Capabilities D&A leaders must ensure they are using a range of tools and technologies to build their technology stack when it comes to AI solutions. 'Stack versus best of breed is not new, but the dynamics of this decision are,' said Herschel 'D&A leaders must cultivate an adaptable ecosystem that scales in order to meet the demands of creating the best AI offerings possible.' To achieve this adaptability, D&A leaders must: Create a modular and open ecosystem: Update or replace architecture components to address new requirements and rapidly changing technologies. Update or replace architecture components to address new requirements and rapidly changing technologies. Make data AI-ready and reusable: Integrate trust into FinOps, DataOps, and PlatformOps to transition from a tech-stack to a trust-stack. Integrate trust into FinOps, DataOps, and PlatformOps to transition from a tech-stack to a trust-stack. Explore AI Agents: Utilize dynamic agents that adapt to changes using an AI-ready data ecosystem powered by active metadata. Journey to Behavioral Change Focusing on data governance, value communication, and analytics augmentation is vital, but addressing the human aspect is crucial for AI success. 'AI is transforming everything, and people are expected to transform too,' said Idoine. 'But people are not the same, and we engage with data and analytics in different ways.' To lay the foundation for the proper culture to best adopt and utilize AI, D&A leaders should take the following steps: Establish repeatable habits: Prioritize training and education with an emphasis on data and AI literacy. Prioritize training and education with an emphasis on data and AI literacy. Embrace new roles and skills: Develop roles that facilitate adaptation to GenAI's change management requirements. Develop roles that facilitate adaptation to GenAI's change management requirements. Collaborate with others: Work with diverse teams, including security and software engineering, for seamless integration. 0 0
