Key Announcements

1.Top Trends in Data and Analytics 2025 – Presented by Shubhangi Vashisth, Director Analyst, Gartner

AI is having a huge impact, but societal, technological, and organizational implications are also driving change in data and analytics (D&A). In this session, Shubhangi Vashisth, Director Analyst at Gartner, examined how trends, such as complexity, trust, and empowerment, are impacting leaders and teams as they make decisions in all aspects of their D&A strategy.

Key Takeaways

• Trend No. 1: Highly Consumable Data Products: “Start with business-critical use cases, Correlate and scale products to reduce data delivery bottlenecks.”
• Trend No. 2: Metadata Management Solutions: “Start with technical metadata and add business metadata to enable context.”
• Trend No. 3: Multi-Modal Data Fabric: “Build a metadata management practice where tools share metadata across the entire data pipeline. Use tools to remove duplicate metadata and share metadata bidirectionally with other tools.”
• Trend No. 4: AI Agents: “Apply actionable AI to move the needle of automation further.”
• Trend No. 5: Small Language Models: “Harness proprietary knowledge for higher reliability.”

2.Future of Data Management Using GenAI – Presented by Prasad Pore, Sr Director Analyst, Gartner

GenAI promises capabilities of improving productivity and operational efficiency of data management function, and data governance. In this session, Prasad Pore, Sr Director Analyst at Gartner, explored the future state of data management and what chief data and analytics officers (CDAOs) need to prioritize when using GenAI applications.

Key Takeaways

• “By 2028, 80% of GenAI business applications will be developed on organizations’ existing data management platforms, reducing implementation complexity and time to delivery by 50%.”
• “By 2028, the data management markets will converge into a single market around data ecosystems enabled by data fabric and GenAI reducing technology complexity.”
• “Invest in metadata management and data fabric to support multimodal inputs and outputs to deliver a GenAI experience.”
• “Leverage retrieval-augmented generation (RAG) as a service from current data management solutions that align with your organizational data when creating business GenAI applications.”
• “Utilize GenAI to improve the data management process, enhance user experience, and deliver valuable insights and predictions.”

3.Unstructured Data Quality: How o Improve Trust to Ensure AI-Ready Data – Presented by Ramke Ramakrishnan, VP Analyst, Gartner

Data architects are increasingly tasked with providing high-quality unstructured data for AI models. However, efforts have primarily focused on data security and privacy, with little attention to managing unstructured data quality. In this session, Ramke Ramakrishnan, VP Analyst at Gartner, shared insights on improving unstructured data quality and discussed emerging best practices in this area.

Key Takeaways

• “Start small while planning for scalability, as unstructured data quality practices and technologies are often manual and underdeveloped, particularly in the context of AI-related use cases.”
• “When planning projects, check both the availability of quality data for training models and the ability to effectively monitor and govern production data sources.”
•  The framework for ensuring unstructured data quality includes three steps:

1. Understand Context: Identify business objectives and user needs.
2. Establish a Knowledge Base: Create a structured format for capturing and organizing data.
3. Governance and Change Management: Regularly review and update data to align with evolving business contexts.

• “Invest in tools and methods to enhance metadata tagging, classification and indexing, making unstructured data easier to find and use securely.”
• “Establish a simple content standard and basic metadata aligned with your content chunking and embedding strategy to facilitate a search-based knowledge environment.”