Sherry Bennett

On January 1, 2021, the House and Senate overrode President Trump’s veto of H.R.6395, the National Defense Authorization Act (NDAA) for Fiscal Year 2021, rendering the bill law. The FY2021 NDAA includes the National Artificial Intelligence Initiative Act (NAIIA), one of the most ambitious attempts by Congress to date, seeking to establish the United States leadership in artificial intelligence (AI).

As the world continues to fight the spread of the deadly coronavirus, data scientists are helping them stay one step ahead. Indeed, predictive analytics, artificial intelligence (AI), and big data have emerged as a powerful tool to predict, respond to, and combat pandemic outbreaks. Data scientists predicted the coronavirus outbreak

Since the release of the final Federal Data Strategy Action Plan (henceforth FDSAP) the end of last year, much has been written about the Federal Data Strategy. However, little has been written to connect the important role it can play in enabling the adoption of AI within the federal public sector.

The end of the federal fiscal year is a great time for public sector decision-makers to consider how their respective organizations can get a head start on supporting the Federal Data Strategy Action Plan. The current draft of the plan, which is scheduled to be finalized by September, contains several recommendations required to be completed within a 3-, 6- or 12-month timeframe.

The summer of 2019 is off to a great start for data professionals seeking to make valuable contributions working in the federal public sector.  After several solicitations for public comments over the last year, the Office of Management and Budget (OMB) has at last issued the final draft of the Federal Data Strategy. The Federal Data Strategy by design is intended to help the government accelerate the use of data to drive and deliver mission objectives.

A survey of Department of Defense employees commissioned by DLT partner, Veritas, and conducted by Federal Computer Week found that 46% of respondents agree that data drives all or most of their decisions (58%), yet only 13% would rate their data management capabilities as “Excellent” while 60% rate them as “Satisfactory” or “Poor”. Challenges Across Each Stage of Data Life Cycle

The purpose of this workshop is to provide senior-level executives with a framework and set of actionable steps to understand and assess the data maturity of their organization. By leveraging the Federal Government Data Maturity Model (FGDMM), attendees will engage in "hands-on" exercises to articulate a data strategy and roadmap for their agency. At this workshop, you will hear from a number of industry leaders as they share their experiences with Data Strategy and Management, including:

Technological innovations in the storage and computing capacity in the world of data has moved exponentially – especially in the healthcare space.  One of the most dramatic use cases is in the field of genomics.  As Andrea Norris, the CIO for NIH mentioned at a public sector Healthcare Summit on IT Modernization last week, technology has greatly increased the speed and lowered the costs associated with gene sequencing.  Originally, when the human genome project completed the task, it took around 13 years with funding around 2.1 billion dollars (the project was from 1990–2003)

2019 is off to a great start for anyone in the business of working with data in the public sector.  President Trump signed the Foundations for Evidence-Based Policymaking Act on January 14th, and with it, the OPEN Government Data Act has finally become law.[1]

The Federal Data Strategy principles (https://strategy.data.gov/principles), as currently articulated, are a set of best practices and guidelines, which could be utilized to govern the development and maturity of an organization’s management of data as an asset.  However, without guidance or a framework within which to actualize these principles, these principles may well be rendered a wish-list.

Form follows function – and so should your organization’s data!