CDAO Government schedule

• Leveraging the power of data to uncover patterns, anomalies, and trends that could indicate fraudulent activities
• Preventing unauthorized access to sensitive information
• Translating findings into insights to manage potential threats proactively

Justin Marsico, Chief Data Officer,

U.S. TREASURY, BUREAU OF THE FISCAL SERVICE

Renata Miskell, Deputy Assistant Secretary for Accounting Policy and Financial Transparency,

U.S. DEPARTMENT OF THE TREASURY

This discussion will focus on how government agencies can leverage hybrid cloud and AI to accelerate their missions by turning raw data into actionable insights. As agencies face unprecedented data volumes and velocity, managing this data efficiently becomes a significant challenge, especially with the growing use of AI-based analytics. 

To address these challenges, agencies are adopting hybrid cloud strategies, blending on-premises and public cloud environments. This approach provides the flexibility needed to manage and analyze data securely and effectively. However, it also introduces complexities that require sophisticated governance and security measures. 

Our discussion will include how agencies are harnessing AI and transforming data in a secure way within a hybrid environment.

COL Paul F. Evangelista, Chief Data Officer,

UNITED STATES MILITARY ACADEMY

Jason Cardone, Strategic Federal Account Executive,

CLOUDERA GOVERNMENT SOLUTIONS

Artificial Intelligence's applications continue to rapidly expand, and we are at a crucial inflection point. Hear from Scott Hallworth, HP’s Chief Data & Analytics Officer, about HP's AI transformation journey and the lessons learned.

• Fostering a culture of collaboration and open data sharing
• Reduction in barriers to make data more accessible
• Implementing data integration practices, ensuring that data from various sources is consolidated into a unified view
  

Moderated by:

Michael R. Anderson, Chief Strategist, Public Sector, INFORMATICA

Panelists:

Richard Patterson, CGFS Chief Data Officer, U.S. DEPARTMENT OF STATE

Elizabeth Puchek, Chief Data Officer, Office of the Chief Data Officer | OPQ | USCIS - U.S. CITIZENSHIP AND IMMIGRATION SERVICES

• Ensuring that your organization has reliable data storage, processing, and retrieval systems
• Enhancing data quality and accuracy
• Fostering data literacy through training
• Democratizing data and sharing insights across the organization

Moderated by:

Terry Dorsey, Sr. Data Architect & Evangelist, DENODO

Panelists

Shannon Arvizu, Ph.D., Senior Advisor to Chief Data Officer, U.S. DEPARTMENT OF COMMERCE

Spencer Cummings, Deputy Chief Data Officer, FEDERAL ENERGY REGULATORY COMMISSION

Julia Cherashore, Deputy Superintendent, Data Governance and Management, NEW YORK STATE DEPARTMENT OF FINANCIAL SERVICES

In today's data-driven landscape, organizations are constantly seeking ways to streamline their data management processes and extract maximum value from their information assets. Efficiency and effectiveness are paramount when dealing with government-size data sets. Let’s explore how a data management co-pilot can leverage AI and machine learning to act as an intelligent companion for all users, significantly enhancing productivity and proficiency in data-related tasks. Automate routine data management and stewardship activities, enabling data teams to focus on higher-value strategic initiatives.

• Enabling transformation and decision making with trusted data
• Ensuring data quality, consistency, and integration
• Improving the quality and speed of government decision-making at scale by automating administrative tasks
  

Moderated by:

Michael R. Anderson, Chief Strategist, Public Sector, INFORMATICA

Panelists:

Marcus Thornton, Deputy Chief Data Officer, COMMONWEALTH OF VIRGINIA

Andrew Patricio, Principal, Digital Services, UNIDOSUS

AI holds undeniable potential for government agencies, but you must be prepared to address the substantial challenges and risks that come with it. This session will arm you a set of set of strategies you can immediately action in your organization.

• Aligning your data & analytics strategy with cross-government & organization level priorities and goals
• Balancing strategic approaches to data analytics with the changing priorities of the wider organization
• How innovative technologies such as AI and Machine Learning can be leveraged to modernize government data architectures?

• How to overcome the challenges associated with modernizing your IT systems such as data privacy, legacy systems and security concerns
• Discover the best practices your agency can take from the planning stage down to execution
• Explore how the cloud will provide increased efficiency and cost saving practices for long term success

• Program impact measurement through personnel and data reporting requirements
• The use of evidence and data-driven decisions in policymaking & agency improvement efforts
• Utilizing data to validate a course of action before decision making

In the dynamic landscape of government services, where data is the foundation for intelligent decision-making, the need for unwavering trust in data integrity has never been more critical. Government            agencies grapple with massive volumes of complex data, and the accuracy and reliability of this information directly influences strategic initiatives, risk management, and compliance. The consequences of inaccurate or inconsistent data can cause downstream issues through an agency, eroding stakeholder confidence, exposing vulnerabilities, and jeopardizing the very foundation on which the agencies operate. This lack of data integrity negatively impacts data-driven decisions and citizen's experiences. 

This roundtable will focus on how the topical AI/ML initiatives will require aligned data sources, visualizations and the core operational predictive and generative AI models, highlighting the symbiotic relationship between data integrity and the success of AI and ML initiatives in the government sector. We will discuss: 

• How do you unlock the full potential of your AI/ML initiatives and fortify the trust in your organization's data foundation? 
• Tips for tackling the complex process of building a resilient and trustworthy data ecosystem
• Share some Real-world challenges and use cases from agencies
• Share results of how agencies have ensured confidence in their data-driven endeavors with good data. 

Moderated by:

Adam Rosenbaum, Director, TRICENTIS

Contributors:

Kristin Saling, Chief of Staff, Innovation Directorate, US ARMY RECRUITING COMMAND

Ron Keesing, Chief AI Officer, LEIDOS

• Developing a workforce with strong analytical skills
• Investing in data literacy training for employees at all levels
• Reinforcing the importance of innovation

• Defining AI and analytics
  • What is it, and what can it do well?
  • What can’t it do well?
• Getting the requirements right
  • What requirements align well with AI and analytics?
  • Customer defined requirements
  • Customer derived requirements
• Internal marketing of AI and analytics
  • Show your team how you create value
  • Understand the problems faced by your teammates
  • Help them define their AI and analytics requirements

Developing a toolkit with numerous statistical tools and in-house AI models that allow analysis of structured and unstructured data on customer's experiences using government digital services.

Ashleigh Sanders, Director, Analytics & Decision Support Division,

GSA

Isabel (Izzy) Metzger, Data Scientist,

GSA

• Exploring the ways in which innovation and transparency in data has enabled improved access, validation and enabled an entirely new generation of analytical models
• Communicating the value of predictive analytics and encouraging organization-wide participation
• Delivering demonstrable data that data is contributing to the overall mission of the department

Moderated by:

Elliot Flautt, Director, Data Solutions, TYLER TECHNOLOGIES

Contributors:

Nikeyta Mullen, Data Infrastructure Lead, C5ISC Cloud & Data Branch, U.S. COAST GUARD

Richard Patterson, CGFS Chief Data Officer, U.S. DEPARTMENT OF STATE

Andrew Patricio, Principal, Digital Services, UNIDOSUS

• Defining the state of the possible – how mature are AI technologies currently, what are the realistic use-cases, and what is hype?
• Getting beyond the buzzword to identify what opportunities does AI present to Governmental organizations seeking to improve their delivery of services?
• Avoiding bias in data sets, algorithms, and machine learning models to ensure citizens are treated fairly & equitably

Artificial intelligence and machine learning (AI/ML) are changing how we utilize and interact with data, raising new challenges to data security. By making unstructured data more accessible and giving computers the ability to make decisions – without human intervention – these innovations are fundamentally changing how data is consumed and outputs are distributed.

As a result, it is incumbent on agencies and organizations to ensure that AI/ML models are not trained on biased or unauthorized data, and that they are leveraged for ethical purposes. Yet, research from the University of Southern California, UNESCO, Forbes, and MIT, among others, has found that AI-driven biases persist in everything from education and healthcare diagnoses, to the mortgage application process. 

To ensure that models are trained on accurate, authorized, and high quality data, you need a data security solution that is able to protect data at its source, make context-based access decisions, and audit how the data is used, both in training and production. In this presentation, Immuta Public Sector CTO Chris Brown will discuss:

• How data security fits into ethical AI/ML development and model bias mitigation
• The unique data security challenges that AI/ML pose, and how attribute- and purpose-based access controls provide a scalable solution to overcome them
• New lines of defense that agencies can enforce to protect unauthorized access through GenAI

• Why ethical factors can no longer be an afterthought of AI initiatives
• Why ethics, transparency, and explainability are necessary for successful, scalable AI programs

• How are you navigating privacy, trust, and security concerns around the use of AI?
• What are you doing to develop an AI ready workforce?
• What AI technologies are you most looking forward to in the coming years?
  

Moderated by:

Tom Scurlock, RVP Public Sector, QLIK

Contributors:

Fumin Yang, Director, Product Management | Business Intelligence & Advanced Analytics Transformation, FINRA

Jing Liu, Executive Director, Michigan Institute for Data and AI in Society, UNIVERSITY OF MICHIGAN

Julia Cherashore, Deputy Superintendent, Data Governance and Management, NEW YORK STATE DEPARTMENT OF FINANCIAL SERVICES

• Unlock innovation, improve services, and create sustainable value
• Prioritize data gathering and analysis
• Challenges to data sharing

Moderated by:

Jason Woodcock, Senior Principal | Solutions, RSI

Contributors:

Jennifer Koester, Director, Enterprise Data Management Department of Technology and Information, STATE OF DELAWARE

• Identify where your tech skills gaps lay to establish a plan that will help retain your employees
• Take a contextualized approach to teaching your employees by skill building during their workday and give support from their peers
• Measure your teams progress overtime to ensure your plan is on the right track
  

Contributors:

Kristin Saling, Chief of Staff, Innovation Directorate, US ARMY RECRUITING COMMAND

Kyle Morton, Head of Industry Engagement, EDM COUNCIL

• Strengthen data governance practices to ensure the accuracy and reliability of data used in AI systems
• Integrate AI readiness into data management strategies to improve decision-making and public trust
• Foster collaboration between agencies to unify data sources, enhancing AI capabilities while maintaining data integrity
• Promote ethical AI development through robust data integrity protocols and transparent data practices across government

Moderated by:

Adita Karkera, Ph.D, Managing Director, Chief Data Officer, Government and Public Services, DELOITTE CONSULTING

Panelists:

CAPT Daniel Rogers, CDAO, U.S. COAST GUARD

Nikeyta Mullen, Data Infrastructure Lead, C5ISC Cloud & Data Branch, U.S. COAST GUARD

Babita Savitsky, Chief of Technology and Architecture, NORTH CAROLINA DEPARTMENT OF HEALTH AND HUMAN SERVICES

Courtney A. Winship, Bureau Chief Data Officer, Bureau of Diplomatic Technology, U.S. DEPARTMENT OF STATE

Robert King, Chief Data Officer, U.S. DEPARTMENT OF ENERGY

• Enhancing the effectiveness and efficiency of each stage of policymaking
• Establishing robust governance structures from the outset of the AI/CX journey 
• Building consistent CX standards across government agencies and services

• Explore strategies to align CDOs' roles and responsibilities with AI initiatives, enabling them to drive data-driven decision-making across the organization 
• Create a robust data governance framework, establish data-driven culture and leverage emerging technologies to support CDOs in their transformational roles
• Discover how to measure and communicate the value of CDOs' contributions, ensuring corporate support and investment in their roles to deliver successful data-driven transformation in the age of artificial intelligence

• With data & analytics talent in such demand, in what ways can the public sector compete with the private sector?
• Is the idea of public service our secret weapon, and how can we best deploy it? How can we ensure employees see the impact of their work, to help boost retention?
• How far is possible to mitigate the need to recruit new talent by upskilling the employees we already have?
• What measures would help reduce the skills shortage to improve the issue in the long term?

• Where do you find new potential collaborators to best enable innovation?
• What common practical, cultural, and legal issues can make collaboration difficult, and how can these be overcome?

• In what ways can we use our data to bring a higher level of accountability to the public sector?
• How can we utilize data to deliver enhanced levels of transparency to the public and all stakeholders?
• What should the balance be between the need to use potentially sensitive information to enable innovation within Government, and the need to respect the privacy of private citizens?