AI and Big Data offer seemingly unlimited potential for organizations to better understand their customers, make more informed decisions, and address challenges with greater agility. It’s important to understand the choices available to achieve the best outcomes.

The intersection of AI and Big Data provides the ability to deliver more targeted, timely, relevant insight in a pervasive and intuitive manner. However, delivering that simplicity requires an analytics and data ecosystem that is markedly more complicated than 10 years ago. To that end, effectively deploying analytics from BI to AI is a now an exercise in portfolio management—complete with discrete customer segments, diverse data environments, development methods, and a wide spectrum of deployment options. This session puts the diverse—and growing—landscape of analytics capabilities from BI to AI into context.

In spite of the buzz around AI, organizations are struggling to build data science teams that deliver value on the ground. This talk presents the three distinctive phases of growth for data science teams, highlighting potential challenges and suggesting a standard framework of guidelines to successfully navigate this evolution. Vastly different approaches are needed in each stage of maturity to tackle aspects such as strategic direction, project framework, the mix of skills, hiring strategies, and fostering of a data culture.

Emerging technologies such as AI, IoT, and machine learning are changing what is knowable about customers. At the same time, the frequency of data misuse is leading government entities and individuals to demand higher standards of accountability.

This presentation explores the issues around modernizing security and governance, as well as what it means to deliver transparency and what users actually expect. It also covers the need to manage accountability within systems of multiple decision-makers; why it is necessary to build fairness into the system to overcome bias, discrimination, and enable diversity; and the need to address expectations of privacy and appropriate use of data.

Due to exponentially growing data stores, organizations today are facing slowdowns and bottlenecks at peak processing times, with queries taking hours or days. Some complex queries simply cannot be executed. Data often requires tedious and time-consuming preparation before queries can be run. This session will demonstrate how the power of GPUs can help conquer these challenges, enabling data professionals to rapidly analyze more data on more dimensions, for previously unobtainable business insights.

Organizations in all industries are under pressure to take advantage of Big Data and newer data sources for real-time decision making in mission-critical environments. New technologies provide opportunities to gain insight into the future.

How does an organization evolve from an application-centric to a data-driven enterprise? This presentation covers how Fannie Mae embarked on a major transformation journey to modernize its data infrastructure, transitioning from legacy data platforms to more integrated and scalable architecture to capitalize on the growing opportunities of the analytics economy and generate substantial business value, internally and externally.

As Big Data grows and evolves, your enterprise faces both challenges and market-disrupting opportunities to analyze and manage larger data volumes for business value. But with seemingly endless commerical, open source, and "as-a-service" offerings hitting the market each week. How do you choose the right mix of technologies and avoid creating an accidental architecture that will limit you from future innovaation? How are organizations actually achieving true bottom-line benefits from their Big Data initiatives? Learn how to adopt an effective and agile approach to Big Data analytics.

With the vast quantities of data flowing into organizations, the job of cleansing and validating data is only becoming more difficult. In order to gain the kind of insights and outcomes that organizations seek, new processes and technologies must be deployed.

The (IRS) Compliance Data Warehouse (CDW) is an analytical data warehouse used for research purposes. It empowers researchers to spend more time on analytics and less on data wrangling. To ensure all data is loaded properly, consistent, well-thought-out validation steps must be included in the ETL process. This presentation offers a case study of accomplishments and lessons learned (since FY 2016), including the data quality issues identified by CDW users (data stewards), and takeaways for attendees on how to improve decision making.

Best-selling author David Weinberger previews his new book on everyday chaos.

Ultimately, machine learning’s most important effect may not be in the benefits its use brings, but how it is implicitly transforming our understanding of how the world works and our most basic strategies for dealing with the future. From Newton on through the Computer Age, we have assumed that the universe is ruled by a relative handful of laws that are the same everywhere and that are simple enough for us to understand. But machine learning shows us a world of motes of data in networks so dense with connections and so delicately balanced, we sometimes can’t understand them. This sort of model of the world is changing not only our strategies, but our moral sense, our ideas about meaning, and even what makes humans special.