In the fast-paced world of data driven decision making, organizations are grappling with the challenge of realizing insights as quickly and efficiently as possible. While data products have been quite a steady architectural pillar within data ecosystems, AI has recently taken the world by storm - helping to accelerate insights at a pace previously unimaginable. In this session hear from Fahad Ahmad, data science leader at Halliburton, about their strategy to transform Halliburton’s previous data swamp into a decentralized data mesh architecture utilizing open AI and data products to deliver real-time insights. Fahad will discuss eliminating fragile data pipelines, fast data-driven decision making on curated datasets, and the innovative usage of ChatGPT to expedite the creation of data products.

Drawing on his 2023 book ‘Confident Data Science’, Adam Nelson will show you how to measure your organization's data culture. Learn how to use this key metric to understand how well your organization’s culture performs along four key dimensions: Offering access to quality information about the data it has; providing the right access to the right people at the right time; investing in data skills development; and maintaining high data quality standards.

We're moving into uncharted territories of emerging technologies, poised to revolutionize data science. In this panel discussion, we'll unravel the mysteries of the technological fringes and explore their imminent impact on mainstream adoption.

Our panel of experts will discuss a future where these technologies are ubiquitous, and how to prepare for the transformative shifts in daily life and industry. Let's peer beyond the horizon, contemplating the dawn of a new era in data science and beyond.

Being a solitary data scientist can be lonely, whether in an embedded role or pioneering data science adoption within an organization. As you collaborate with individuals with varying business contexts, subject-matter expertise, and data backgrounds, there are strategies you can apply to set yourself up for success.

 In this session, Lauren Burke will demonstrate how solitary or siloed data scientists can thrive by gaining support and buy-in from key stakeholders. She'll cover the "road trip" strategy for identifying allies and finding quick wins to demonstrate value. She'll also discuss communicating the difference between traditional analytics and data science, as well as techniques for educating stakeholders and leveling up junior practitioners.

 Attendees will learn how to identify organizational needs and effectively scope projects, ensuring alignment with business objectives and defining clear measures of success while identifying opportunities to deliver incremental value. We'll cover the importance of creating end-user-focused documentation, where it should live, and how to use "SEO" to make your data science presence more visible. 

Join us for a dynamic discussion featuring a Chief Data Officer (CDO), Vice President of Data Science, and Technical Industry Lead as they delve into the real-world challenges of implementing AI and machine learning within Fortune 500 companies and large-scale government entities. This panel will shed light on the common pitfalls, the harsh realities faced, and practical strategies for overcoming these obstacles. Don't miss this opportunity to gain valuable insights from industry experts on navigating the complex landscape of AI/ML application in high-stakes environments.

Large Language Models like the GPT, Gemini, Gemma and Llama series are rapidly transforming the world in general and the field of data science in particular. This talk introduces deep-learning transformer architectures including LLMs. Critically, it also demonstrates the breadth of capabilities state-of-the-art LLMs can deliver, including for dramatically revolutionizing the development of machine learning models and commercially successful AI products. This talk provides an overview of the full lifecycle of LLM development, from training to production deployment, with an emphasis on leveraging the open-source Python libraries like Hugging Face Transformers and PyTorch Lightning.

Companies need reliable information about the future to make decisions in the present. Strategic plans and budgets rely on good guesses about what the future holds. But typical approaches to planning are often rarefied and insulated from the real world. There's an approach developed in Switzerland and Southern California that holds real promise: Gamification.

Games are inherently competitive—which means the player with the optimal strategy prevails. That optimization can be applied to forecasting and prediction, yielding significantly better results. David Savlowitz offers a peek into gamified forecasting, a convergence of competitive gaming, investment strategy, data science, and human psychology that enables accurate forecasts.

In this session, David will explain how competitive approaches can involve a wider group that includes economists, data scientists, and non-technical staff; and how tournament models where players are rewarded for accurate predictions that maximize return on investment and minimize forecasting error.

We're moving into uncharted territories of emerging technologies, poised to revolutionize data science. In this panel discussion, we'll unravel the mysteries of the technological fringes and explore their imminent impact on mainstream adoption.

Our panel of experts will discuss a future where these technologies are ubiquitous, and how to prepare for the transformative shifts in daily life and industry. Let's peer beyond the horizon, contemplating the dawn of a new era in data science and beyond.

This session explores the technological advancements of analytics, data science, machine learning, AI, and quantum computing. Beginning with an overview of the historical development and current state of these technologies. LexisNexis' Flavio Villanustre will explore how analytics has transformed industries; how data science extracts valuable insights, how machine learning has moved from simple rule-based systems to today's deep learning models; and how quantum computing promises to solve previously intractable problems at speeds unattainable by classical computers.

Attendees will learn about the benefits and associated risks of each technology: Increases in efficiency, productivity, and innovation juxtaposed against vulnerabilities in privacy, security, bias, and wider societal concerns such as unemployment. These competing issues underscore the need for responsible, ethical research and deployment of such tools.