Data Driven Government
September 25, 2019 – Washington, D.C.

The premier analytics and AI conference for government

To support the President’s Management Agenda to use data as a strategic asset, Data Driven Government aims to help agency leaders more effectively accomplish their mission, increase efficiency, and improve evidence-based policymaking.

 

Data Driven Government is focused on helping government executives to share and discuss emerging trends and best practices of how government agencies are currently using data analytics to enhance mission outcomes. A practically-focused, vendor-neutral conference, Data Driven Government advances the deployment of analytics and data science within Federal, State, and Local government to reduce fraud, waste, and abuse, automate manual processes, and drive smarter decisions by extracting actionable insights from the vast quantities of data within government agencies.

Data Driven Government — the facts:

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Speakers 2018

David C. Williams
Board of Governors
David C. Williams

David C. Williams was sworn in as the second independent Inspector General (IG) for the U.S. Postal Service on August 20, 2003. Williams is responsible for a staff of more than 1,100 employees — located in major offices nationwide – that conducts independent audits and investigations for the largest civilian federal agency that has $67 billion in annual revenues, a workforce of 671,687 employees and contractors and nearly 32,528 facilities.

The office is under the general supervision of the nine Postal Service governors and is not subject to any other Postal Service supervision.

In his last position, Williams served as the Deputy Assistant Administrator for Aviation Operations at the Transportation Security Administration (TSA) from August 2002 until August 2003, where he managed the Aviation Inspection Program at federalized airports.

Williams has served as IG for five federal agencies. He was first appointed by President George Bush to serve as IG for the U.S. Nuclear Regulatory Commission from 1989 to 1996. President William Clinton next appointed him IG for the Social Security Administration from 1996 to 1998, and then as IG for of the Department of the Treasury in 1998. In 1999, President Clinton named him as the first IG for Tax Administration of the Department of Treasury, where he directed a staff of 1,050 to detect fraud, waste and abuse. In 2001 President George W. Bush named Williams the Acting IG for HUD, while he was also serving at the Department of the Treasury.

Williams served in the U.S. Army Military Intelligence and began his civilian federal career as a special agent with the U.S. Secret Service. Moving up the career ladder, he served as Director of Operations in the Office of Labor Racketeering at the Department of Labor; the President’s Commission on Organized Crime; and as Director of the Office of Special Investigations at the U.S. General Accounting Office. Williams is the recipient of the U.S. Bronze Star and the Vietnamese Medal of Honor for service in Vietnam.

A native of Illinois, Williams graduated from Southern Illinois University, Edwardsville, Ill., and received his Advanced Degree in Education and a Masters in Education from the University of Illinois in Champaign, Ill. He also attended the U.S. Military Intelligence Academy, the Federal Law Enforcement Training Center and the U.S. Secret Service Training Academy.

John Elder Ph.D.
Founder & Chair, Elder Research
John Elder Ph.D.

John Elder chairs America’s most experienced Data Science consultancy. Founded in 1995, Elder Research has offices in Virginia, Maryland, North Carolina and Washington DC. Dr. Elder co-authored 3 award-winning books on analytics, was a discoverer of ensemble methods, chairs international conferences, and is a popular keynote speaker. John is occasionally an Adjunct Professor of Systems Engineering at the University of Virginia, and was named by President Bush to serve 5 years on a panel to guide technology for national security.

Tom Davenport
Independent Senior Advisor, Deloitte Consulting, LLP, Professor of Information Technology and Management, Babson College and Co-Founder of the International Institute for Analytics
Tom Davenport

Tom Davenport is the President’s Distinguished Professor of Information Technology and Management at Babson College, the co-founder of the International Institute for Analytics, a Fellow of the MIT Center for Digital Business. He is an independent senior advisor to Deloitte Analytics, Deloitte Consulting LLP. He collaborates with Deloitte thought leaders on all things related to business analytics, from the potential of cognitive technologies to industry-focused explorations and outcomes. Covering topics from emerging technologies to innovative business applications, Tom’s Deloitte University Press series reveals leading-edge thinking on analytics and cognitive technology.

Connect with Tom on LinkedIn and Twitter

Jane Wiseman
Senior Fellow, Ash Center for Democratic Governance and Innovation, Harvard Kennedy School.
Jane Wiseman

Jane Wiseman leads the Institute for Excellence in Government, a non-profit consulting firm dedicated to improving government performance.  She is also an Innovations in American Government Fellow at the Ash Center for Democratic Governance and Innovation.  She has served as an appointed official in government and as a financial advisor and consultant to government.

Her current consulting, research, and writing focus on government innovation,  data-driven decision-making and operational efficiency in government.  With the Harvard Ash Center, she supports a national network of urban Chief Data Officers to accelerate the use of analytics in local government.  With Harvard Ash Center, she created an open platform for government access to the best examples of government operational efficiency approaches, with case studies showing successful implementation.  She has advised the US cities funded by Bloomberg Philanthropies in their Mayors Challenge competition.  She has written on customer-centric government, data-driven decision-making in government, pretrial justice, and 311 for a variety of audiences.

Her prior consulting work has included organizational strategy, performance management and eGovernment strategy work for Accenture and Price Waterhouse.  Selected clients include the National Governor’s Association, the United States Department of Veterans Affairs, the National Criminal Justice Association, the Commonwealth of Massachusetts, the United States Postal Service, the State of Michigan, the United States Department of Housing and Urban Development, and the United States Department of Commerce.

Ms. Wiseman has served as Assistant Secretary, Massachusetts Executive Office of Public Safety and as Assistant to the Director for Strategic Planning, National Institute of Justice, United States Department of Justice.  Ms. Wiseman represented the Justice Department on detail as a Staff Assistant for the US House of Representatives Appropriations Committee.  Ms. Wiseman holds a Bachelor of Arts in Government from Smith College and a Master of Public Policy from the Harvard Kennedy School.

Ethan Chen
Director, Division of Data Management Services and Solutions, FDA CDER OSP Office of Business Informatics
Ethan Chen

Ethan Chen provides overall leadership to CDER in streamlining electronic and traditional submissions and delivering solutions to enable rapid adoption of emerging electronic data standards. Since joining the FDA in 2012, Mr. Chen has led the several critical initiatives as the CDER Informatics Architect, including Data Management and Business Intelligence programs. Ethan has over 20-years’ experience in Data Management, Enterprise Architecture, Solution Development and System Integration.

Christina K. McGlosson
Associate Director, Enforcement Division’s Whistleblower Office, CFTC
Christina K. McGlosson

Ms. McGlosson is a newly appointed Associate Director in the Enforcement Division’s Whistleblower Office at the CFTC and is directing the Enforcement Division’s data analytics strategy.

Through December 2017, Ms. McGlosson was Senior Special Counsel to the SEC’s Deputy Chief Economist and the Division of Economic and Risk Analysis’s Deputy Director, providing guidance and counsel on a wide variety of Commission legal and policy matters. She joined the Division of Economic and Risk Analysis in 2013, to advise Senior Division Staff on Enforcement Division investigation and litigation practices, strategies, and programmatic directives, so that these considerations may be integrated into the development of data-driven, risk analytic programs designed to detect federal securities law violations involving fraud and misconduct. She led the development of the Division’s Office of Risk Assessment.  She speaks frequently about the SEC’s and the CFTC’s development and use of data analytics.

Ms. McGlosson joined the SEC in 1998, and served in a variety of positions in the Division of Enforcement, including Senior Counsel to the Director and Deputy Director of Enforcement, Senior Adviser to the Chief of the Office of Market Intelligence, and Senior Counsel in the Office of Chief Counsel. She has also successfully investigated, brought before the Commission, and litigated in U.S. District Court, a number of financial fraud, insider trading, executive compensation, and microcap fraud cases.

Ms. McGlosson received her J.D. Cum Laude, from The Catholic University of America’s Columbus School of Law, where she received a concentration in Securities Law. She holds an A.B. in Government from Georgetown University, where she graduated with First Honors.

Daniel Morgan
Chief Data Officer and Acting Chief Technology Officer, US Department of Transportation
Daniel Morgan

Daniel Morgan is the first Chief Data Officer of the United States

Department of Transportation. As the CDO, he has overall responsibility for the Departmental data program and data compliance across the Department. He is responsible for establishing a clear vision of the data managed in DOT and the application of DOT data for decision-making. He serves as data strategist and adviser, steward for improving data quality, liaison for data sharing and developer of new data products.

Prior to assuming this role, Mr. Morgan spent 15 years as a management consultant, providing services to public and private sector clients in a variety of areas, including: open government, information technology governance, capital planning and investment control, enterprise architecture, and human capital planning.

Mr. Morgan holds a Bachelor’s Degree in Mechanical Engineering from the University of Illinois at Urbana-Champaign.

Kris Rowley
Chief Data Officer, GSA-IT
Kris Rowley

Kris focus’s his energy on understanding the GSA data culture and cultivating and discussing ways to improve information management and data access across the agency.  This includes working with GSA leadership on fostering a data governance model that is driven by a Steering Committee, chaired by Kris, and includes senior executives from across all functional areas. Kris has helped GSA prioritizes resources, identifies enterprise data challenges, and to charter cross functional working groups to address data related issues. The working groups include data stewards and IT data strategy teams.  The data stewards are most closely associated with the Agencies enterprise applications and help address data quality and redundancy issues.  The IT data strategy team is technical engineers and program managers that build and maintain those systems and the data warehouses and data models that transform the data.  Together, these working groups have consolidated previously stovepipe processes and technologies and have created common procedures for sharing data across functional areas.

2019 Workshops

you can attend for intensive learning

Sep 23, 2019
** NEW! ** Fraud Analytics & Anomaly Detection
Mike Thurber
Hosts:
Mike Thurber
Date:
Sep 23, 2019
Time:
8:30 am - 4:30 pm
Price:
Government Employee: $500
Private Industry / Contractors: $1,000
** NEW! ** Fraud Analytics & Anomaly Detection

Intended Audience: Fraud analysts and managers interested in learning the latest array of data science and machine learning techniques useful for reducing and eliminating fraud, waste, and abuse.

 Knowledge Level: Attendees should have some basic experience with data-driven modeling.

Workshop Description

Dramatically reducing fraud and other abuse is a challenging task requiring a comprehensive and flexible analytics solution. Wherever your organization is along the wide spectrum of fraud analytics capabilities — from basic rules and reporting to complex network graph analysis – this workshop will show you how to build out all the components of a holistic and robust fraud analytics platform. We will review critical technical details as well as problems to watch out for based on lessons learned over years of consulting on real-world fraud cases.

This one-day session teaches best practices for fraud analytics constrained by the realities of incomplete data and unrecognized or emerging fraud patterns. Existing fraud models have proven value, but nagging concerns remain about “unknown unknowns”. A new view of the problem is required.

  • You should attend this workshop to learn:
  • What anti-fraud techniques work best
  • Graph (network) features that highlight fraud
  • How to extract information hidden in unlabeled data
  • A new way to powerfully combine supervised and unsupervised models

Attendees will learn how to:

  • Use new unsupervised learning techniques to identify previously unseen cases of fraud
  • Recognize and remove common biases
    (such as stemming from rare known fraud cases and from selection effects)
  • Prioritize anomaly types by their fraud likelihood
  • Incorporate multiple techniques and processes into an effective anti-fraud platform
Sep 24, 2019
The Best of Predictive Analytics: Core Machine Learning and Data Science Techniques
John Elder Ph.D.
Hosts:
John Elder Ph.D.
Date:
Sep 24, 2019
Time:
8:30 am - 4:30 pm
Price:
Government Employee: $500
Private Industry / Contractors: $1,000
The Best of Predictive Analytics: Core Machine Learning and Data Science Techniques

Intended Audience: Interested in the fundamentals of modern machine learning techniques.

Program Level: Intermediate
For this introductory-level workshop, it is helpful for attendees to already be familiar with the basics of probability and coding.

Recommend Field of Study:

  • Computer Science
  • Analytics
  • Statistics
  • Mathematics
  • Finance and Marketing

Instructional Method: Group – Live
CPE Credit’s received for completion:

  • 1.5 CPE Credits for Management Adv Service
  • 1.5 Statistics
  • 1.5 Specialized Knowledge and Apps
  • 1.5 Business mgmt. and org

Companion Workshop: This workshop is the perfect complement for Dr. Elder’s other one-day PAW workshop, “The Deadly Dozen: The Top 12 Analytics Mistakes and the Techniques to Defeat Them,” although both workshops stand alone and may be taken in either order.

 

Workshop Description

This one-day session surveys standard and advanced methods for predictive modeling (aka machine learning).

Predictive analytics has proven capable of generating enormous returns across industries – but, with so many machine learning modeling methods, there are some tough questions that need answering:

  • How do you pick the right one to deliver the greatest impact for your business, as applied over your data?
  • What are the best practices along the way?
  • How do you make it sure it works on new data?

In this workshop, renowned practitioner and hugely popular instructor Dr. John Elder will describe the key inner workings of leading machine learning algorithms, demonstrate their performance with business case studies, compare their merits, and show you how to select the method and tool best suited to each predictive analytics project.

Attendees will leave with an understanding of the most popular algorithms, including classical regression, decision trees, nearest neighbors, and neural networks, as well as breakthrough ensemble methods such as bagging, boosting, and random forests.

This workshop will also cover useful ways to visualize, select, reduce, and engineer features – such as principal components and projection pursuit. Most importantly, Dr. Elder reveals how the essential resampling techniques of cross-validation and bootstrapping make your models robust and reliable.

Throughout the workshop day, Dr. Elder will share his (often humorous) stories from real-world applications, highlighting mistakes to avoid.

If you’d like to become a practitioner of predictive analytics – or if you already are and would like to hone your knowledge across methods and best practices – this workshop is for you.

What you will learn:

  • The tremendous value of learning from data
  • How to create valuable predictive models with machine learning for your business
  • Best Practices, with real-world stories of what happens when things go wrong

Prerequisites:
The workshop is filled with real-world stories and explanations of methods that are visual and analogy-based, rather than mathematical. Each section is designed to make clear the gist of its concept to a complete novice, and to conclude with intriguing ideas for advanced researchers. Experience has shown that attendees who get the very most out of the course:

  • Have some experience with programming, or algorithmic approaches to problem-solving
  • Have taken an introductory course in probability or statistics … but most importantly
  • Have a problem to solve that inspires and anchors their learning as techniques are introduced
Sep 26, 2019
Data Science for Managers
Carl Hoover Ph.D.
Hosts:
Carl Hoover Ph.D.
Date:
Sep 26, 2019
Time:
9:00 am - 4:30 pm
Price:
Government Employee: $500
Private Industry / Contractors: $1,000
Data Science for Managers

Intended Audience: Business managers, executives and leaders interested in data science.

Program Level: Beginner to Intermediate

Recommend Field of Study:

  • Computer Science
  • Analytics
  • Statistics
  • Mathematics
  • Finance and Marketing

Instructional Method: Group – Live
CPE Credit’s received for completion:

  • 1.5 CPE Credits for Management Adv Service
  • 1.5 Statistics
  • 1.5 Specialized Knowledge and Apps
  • 1.5 Business mgmt. and org

 

Workshop Description

This 1-day workshop is an introduction to data science for executives and managers of data science programs. It provides a high-level overview of modern data science concepts, tools, and techniques from a management perspective. All stages of the data science lifecycle will be discussed in the context of agile management methodologies using real-world case studies. Managers will learn how to identify skilled data scientists and build data science teams that use sound scientific methods to meet their organization’s objectives. Leaders will learn to ask the right questions, solve the right problems, keep data science projects on track, ensure their solutions are deployed and avoid common pitfalls along the way. Both technical and non-technical participants will benefit from this workshop and be equipped with the knowledge necessary to lead effective data science programs in their organizations.

What you will learn:

  • Basic concepts of data science and advanced analytics
  • Agile planning within a typical data science project lifecycle
  • Project scoping by defining appropriate data science tasks and objectives
  • Best and worst data science practices

Prerequisites:
There are no prerequisites for this workshop; however, it is best suited for participates who:

  • Have executive or mid-level management experience
  • Are currently managing or planning to manage data science projects

Learning objectives:
Participants will learn how to:

  • Understand fundamental concepts of data science
  • Plan and manage the data science project lifecycle using agile techniques
  • Define the appropriate data science problem to solve
  • Build effective data science teams within an organization
  • Avoid common pitfalls in data science management

The Venue

Capital Hilton

Address
16th & K Street, NW
Washington, DC 20036

 

Website
Visit the Capital Hilton Site

 

Phone
+1-202-393-1000

Capital Hilton, 16th Street Northwest, Washington, D.C., District of Columbia, USA

FAQ

How can we help?

  • When and where does the conference take place?

    The conference and expo take place on Wednesday September 25, 2019 at the Capital Hilton, on the corner of 16th & K Street, NW
    Washington, DC. In addition, there are two days of workshops taking place on the Tuesday and Thursday either side of the conference, also at the Capital Hilton.

  • Where can I buy my ticket?

    Registration details for the conference are here.

  • What topics does the conference cover?

    All topics around emerging trends and best practices of how government agencies (at both Federal, State and local level) are currently using data analytics to enhance mission outcomes. For example:

     

    • How are public sector agencies using analytics & AI? Where are they headed?
    • How can we make analytics mainstream? How can we build a culture of innovation?
    • How can advanced analytics convert insights from data into successful mission strategies for the public sector?
    • How do you build cohesive data strategies that focus on mission value?
    • What are the next-generation analytics & AI technologies relevant to the public sector?
    • How do you build high-performing analytics teams? How do you attract and develop the right talent?
  • Where can I get other questions answered?

    Questions about registration? See here or email: regsupport@risingmedia.com

    Questions about sponsoring? See here or email: pgillis@risingmedia.com

    Questions about speaking? See here or email: rglavina@risingmedia.com

    Questions about partnering to help promote Data Driven Government? Email: marketing@risingmedia.com

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