White House hears from public on data strategy

More than 50 experts gathered at the Commerce Department to discuss the path forward to implement the recently released White House data strategy.

big data analysis
 

More than 50 experts gathered at the Commerce Department on Monday to discuss the path forward to implement the recently released White House data strategy.

The event, convened by the Office of Management and Budget and the Data Coalition, included discussion of how the process will work and how open that process will be.

The data strategy action plan outlined 16 steps -- some agency-specific, others governmentwide -- to "establish a firm basis of tools, processes, and capacities to leverage data as a strategic asset." The forum was aimed at providing public input to the plan.

The White House set aggressive deadlines for the federal government and federal agencies, including establishing an OMB Data Council by November, piloting a governmentwide data catalog by May 2020 and identifying priority datasets at each agency by August 2020.

It also addressed goals such as improving geospatial and financial-management data standards as well as identifying opportunities to increase data skills within the federal workforce.

The creation of a data council at OMB -- included in the draft plan -- "is long overdue," said Charles Rothwell, a former director of the National Center for Health Statistics. The plan calls for the OMB council to be created within three months, with agencies launching their own offices in a month.

"To me, that timing is backwards. The OMB Data Council should serve as a model for department-level" agencies, Rothwell said.

Breaking up data that has accumulated in countless agency silos over the decades, said is an admirable goal, said Clifton Roberts, Intel's global director of cloud and data policy. However, Roberts advised that storing all that siloed data in a single place is impractical and possibly a security risk.

Roberts suggested the federal government and private industry develop a "playbook" to create a secure, federated machine learning-based model for dealing with siloed data.

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