IT and business executives frequently talk about information as one of their most important assets. But few behave as if it is. Even today, executives report on their financials, their customers, and their partnerships, but rarely the health of their data assets. And corporations typically exhibit greater discipline in managing and accounting for their office furniture than their data. The arrival of generative AI (GenAI) is sparking a discussion of how to adopt AI in measuring, monetizing, and managing data assets. Laney shares insights from his best-selling book, Infonomics, about how organizations can actually treat information as an actual enterprise asset. He discusses why data both is and isn’t an asset and property and what this means to organizations—particularly as they prepare to put AI to work broadly. He also covers well-honed approaches to and examples of organizations managing, monetizing, and measuring their data assets. 

Jain and Das discuss how organizations should secure their AI application and the critical data they are feeding into these systems to ensure compliance and prevent damaging data leaks.

Learn how National Student Clearinghouse (NSC) created an operational MDM platform, giving access to a large volume of streamlined, high-quality data. With billions of records, a legacy IT system, and an enterprise focus on moving to the cloud, NSC focused on modernization for the cloud data ecosystem, adhering to compliance regulations and enhancing matching across the enterprise. Discover how NSC is now empowered with a single platform to support and facilitate customer requests with one source of truth while benefiting from a collaborative hub for data management and governance.

Generative AI (GenAI) is all the rage these days, but finding effective and realistic uses for it is still elusive.

The vast majority of current GenAI projects will fail, not because of inherent flaws in large language models (LLMs), but because of misconceptions about how to use them and the lack of capabilities needed to successfully design, develop, and operationalize GenAI-driven applications. Carlsson debunks the most harmful myths that set up projects for failure and looks at case studies of how advanced AI teams in industries ranging from pharma to food delivery are shattering these myths and delivering transformative outcomes.

In this era where AI is reshaping industries, the integration of large language models (LLMs) like ChatGPT with private knowledge platforms is a groundbreaking development. Datavid shares experiences and lessons learned from both internal R&D and the benchmarking of several LLMs with customers and subsequent integration with existing KM platforms. Deep dive into the synergistic potential of combining the advanced natural language processing capabilities of LLMs with the rich, domain-specific data housed in private knowledge platforms. Come explore how this integration can revolutionize AI applications in your industry!

When talking about AI, it’s important to recognize that many technologies, not just GenAI, need to be considered.

Chen explores the inherent connection among logistic regression, neural networks, and computer vision using mathematical structures as a lens. Drawing parallels between the construction of logistic regression functions and mathematical representations uncovers the foundational role of abstract mathematical concepts in shaping these methodologies. In logistic regression, the linear function, dynamically shaped by a combination of various features, emerges as a visual metaphor—a plane in the mathematical fabric. In neural networks, weights and nodes form a space surrounded by multidimensional planes, aligning closely with mathematical principles. In computer vision, filters function as weighted combinations of pixel features, extending the mathematical concept to image processing. This presentation illuminates the harmony and shared essence of mathematical principles across diverse machine learning and computer vision paradigms.

In the rapidly evolving landscape of AI, the ability to efficiently handle and process vast amounts of complex data is paramount. Vector databases and vector search have emerged as critical components in this domain, offering a specialized approach to managing multidimensional datapoints, or vectors, that are essential for advanced AI applications. Agarwal gives a comprehensive exploration of vector databases, their role in AI solutions, and the emerging trends and technologies that are shaping their development.

A well-known drawback to using generative AI (GenAI) is its tendency to produce false information.

A crucial aspect of constructing and applying GenAI for enterprise-level applications is mitigating hallucinations. The generation of factually inaccurate information can occur both during the initial development of large language models (LLM s)and the subsequent refinement of existing model responses through prompt engineering. Bhattacharya explores diverse approaches to mitigate these issues, including the introduction of new decoding strategies, optimizations based on knowledge graphs, the incorporation of innovative components in loss functions, and supervised fine-tuning. She also addresses methods such as retrieval augmentation, feedback-based strategies, and prompt tuning, which can be implemented during the prompt engineering phase.

AI has the power to help your organization disrupt, innovate, generate faster insights, cut costs, and increase productivity. But responsible and successful AI use demands high-quality, trusted data and transparent, observed, and accessible data intelligence. See firsthand how taking a model-to-marketplace approach to managing and leveraging your organization's data can help you gain the footing needed to get the AI results you desire.

The vector database has fast emerged as a preferred platform for GenAI applications.

While companies have long used vector databases to recognize patterns and support machine learning recommendation engines, now they are using them to support GenAI initiatives by storing, modeling, and searching tokenized data documents. Vector databases feed relevant content to language models (LMs), helping enrich prompts, fine-tune models, and govern outputs. Petrie defines vector databases and how they help companies boost productivity and gain competitive advantage with domain-specific GenAI initiatives. He looks at market requirements, adoption trends, challenges, benefits, use cases, and architectural approaches.

Today's data-driven strategies for boosting revenue, reducing costs, and minimizing risk often begin with modernizing applications. According to Gartner, 68% of executives globally plan to up their spending on modernization technologies; 90% see AI and ML as likely additions by 2026; and key focus areas include low-code/no-code, GenAI, and distributed cloud. See how companies like Visa, BNP Paribas, Standard Chartered, and Deutsche Bank are achieving quicker feature rollouts and reducing total cost of ownership.

Models can be structured and designed in a variety of ways to enable them to provide valuable insights.

Time series analysis plays a crucial role in enhancing the capabilities of AI by providing valuable insights into temporal patterns, trends, and dependencies within datasets. Oad explores the synergies between time series analysis and AI, showcasing how the integration of temporal data can significantly improve the performance and accuracy of AI models. Key points to cover include temporal context in data, enhanced predictive modeling, improved anomaly detection, dynamic feature engineering, optimizing AI for time-varying data, forecasting and trend analysis.

Confronting the toughest data management challenge head-on, Rudden dissects the complexities of AI-driven versioning and presents a road map for navigating this intricate landscape. She delves into the strategic application of taxonomies and ontologies within the realm of graph modeling—heralding a new era of data structuring that boosts analytics, foresight, and decision making. Her approach provides attendees with the acumen to select, organize, and manage the right datasets, fortifying their data architecture against the rapid evolution of technology. Geared for a diverse array of data professionals, from strategists and scientists to engineers and BI experts, Rudden's insights are set to empower the audience with practical tools and methodologies. This keynote is your key to demystifying data management and embracing its future with confidence and expertise.

Although typical data architectures are able to process streaming data, more often than not, the analytics are performed offline in batch mode. The real-time data is available for analysis, but the benefits of real time are lost the instant the data lands in a datastore or lakehouse for analysis. Ahuja delves into a modern data and analytics architecture—the Unified Real-Time Data Platform—that solves the real-time challenge. He shares details and use cases on how to process streaming data, enrich it with contextual historical data, and execute advanced analytical workloads—all at ultra-low latencies and massive scale.

No more black box AI implementations—the technology needs to be ethical and explainable.

As AI becomes increasingly integrated into our lives, it is important to ensure that it is developed and deployed in an ethical and responsible manner. One key component of this is explainable AI, which provides transparency and accountability by enabling users to understand how AI systems make decisions and identify potential biases or errors. This can help mitigate risks and build trust in AI, leading to more ethical and responsible use. In this presentation, Moharir explores the importance of explainable AI and its role in promoting responsible and ethical AI development.

Take advantage of machine learning and NLP within the organization.

Generative AI (GenAI) is proving useful in the enterprise, but in many applications, it can't be used "off-the-shelf." For instance, deploying GenAI to answer business research questions from long text documents—primary and secondary market research reports, journal articles, thought leader white papers—requires several adaptations to make the process (and processing) efficient and effective. One of those adaptations is optimizing the document text with natural language processing (NLP) to accommodate the text capacity limitations of large language model APIs. Seuss explains and demonstrates how to use NLP to feed the GenAI only "summary worthy sentences" that are rich in meaning and help ensure the GenAI response is as accurate and meaningful as possible.

Introducing AI into the enterprise is top of mind for many these days.

Probstein introduces the concept of metasearch as a transformative tool in the business world, akin to a master key unlocking various treasure chests. This analogy aptly describes the modern enterprise landscape, where numerous cloud-based applications, each with their unique datasets, are seamlessly accessible. He emphasizes the practicality of this approach, highlighting the efficiency of using metasearch over traditional methods that often involve heavy data amalgamation. By keeping the data in its original “chests” and using metasearch as the unifying tool, businesses can enjoy a more streamlined and agile data management process. The talk further delves into the synergy between metasearch and AI technologies like ChatGPT. AI, when applied to the rich and varied internal data of a company, can act as an intelligent guide, making sense of the vast information treasures. This approach not only simplifies data interaction, it also unlocks deeper insights, enhancing decision making and strategic planning.

Knowledge graphs are now independent entities capable of continuous self-improvement.

Recent advancements in large language models (LLMs) have spearheaded the development of self-sustaining knowledge graphs. Aasman focuses on four important aspects essential for knowledge graphs to autonomously synthesize and manage information: Intuitive Query Primitives, which allow effortless extraction of data from LLMs; Natural Language to Structured Query Translation, which translates natural language queries into structured queries across various languages; Integrated Vector Store, which facilitates seamless interactions between internal, private data and external, public data; and Neuro-Symbolic Framework, which synergizes rule-driven logic, constraint-based reasoning, description logic, Graph Neural Networks (GNN), Machine Learning, and LLM inferences. The presentation showcases practical applications.

Many companies have prioritized various data management trends this year to meet their increasing demands in data and AI initiatives. To help guide people, Radiant Advisors and Database Trends and Applications magazine conducted a market survey in Q1 2024, which analyzed what companies are doing beyond the hype. This survey focused on companies' perceptions, planning, and adoption of current data management practices, such as data fabric and active metadata, multi-domain master and reference data management, data quality, data observability, and data catalogs. The study covered a range of industries and company sizes. Following your participation at Data Summit 2024, you can compare your enlightened perspectives with the survey findings.