This document discusses the role of big data in performance budgeting. It outlines several key opportunities for agencies to leverage data in federal planning processes like strategic plans, annual performance plans and reports, and quarterly performance reviews. It also presents a model for maturing federal data quality practices from an ad hoc initial level to a strategic continuous improvement level. Recent examples of increased data use by federal, state and local governments are provided. The main challenges discussed are defining and measuring outcomes, creating trust and confidence in data, and integrating data sets while standardizing definitions.

1. Session 1 – Role of Big Data in
Performance Budgeting
Data Strategy
OECD 13th Annual Meeting of the OECD Senior Budget Officials Performance and
Results Network, 16-17 November 2017
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2. Key Opportunities to Leverage Data in Federal Planning
Cross-Agency
Priority Goals
(OMB)
Strategic
Plans
(Agencies)
Strategic Objective
Annual Reviews
(Agencies w/OMB)
Annual Performance
Plans & Reports/Budget
(Agencies)
CAP Goal Quarterly
Internal Reviews and
Public Updates (OMB)
APG Public Updates
(Agencies)
Quadrennial
Quarterly Performance
Reviews (Agencies)
Agency
Priority Goal
Selection
(Agencies)
Annual Financial Report/
Management’s Discussion
and Analysis (Agencies)
Annual
Bi-Annual
Quarterly
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• Agency Strategic Plan and Cross-Agency Priority Goals: Describes long-term goals the agency and U.S.
government aim to achieve and what strategies the agency will pursue to realize those objectives.
• Strategic Objective Annual Review (SOAR): A process by which an agency regularly assesses progress toward
priorities outlined in its strategic plan, leveraging data and evidence from a variety of sources.
• Quarterly Performance Reviews (QPRs): A forum for top leadership and senior management to engage on
organizational performance topics, including priority-setting, risk management, problem identification,
accountability for results, and budget formulation and execution.

3. Model for Maturing Federal Data Quality Practices1
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•Ad hoc processes
•Policies are informal and/or undocumented
•Reactive vs. proactive
•Little to no coordination across programs
•Data is corrected but not in a coordinated way
•Cross-enterprise set of policies and management
•Validity of data is auditable
•Data flaws recognized early in information flow
•Remediation governed by well-defined processes
•Strategic continuous improvement
•Established set of policies
•Proactive vs. reactive
•Policies and processes are shared across the organization
•Framework for responsibility and accountability
•Capacity for validation of data
•Data quality steps documented, repeatable
•Initial policies are defined
•Processes and policies vary across programs
•Limited anticipation of data issues
•Basic organizational management and information sharing
Level4
Level3
Level2
Level1
1Maturity Model developed by the Performance Improvement Council Data Quality Working Group, 2016

4. Recent Examples of Increased Use of Data
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Federal Government:
• USAspending.gov
• Performance.gov
• Fraud prevention
• Strategic planning
State and Local Government:
• 311 call centers
• Data-driven reviews
• Public performance dashboards
Screenshot of USASpending.gov Beta site showing federal spending by state

5. Key Challenges
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1. Defining and measuring outcomes
2. Creating trust and confidence in the data
3. Integrating data sets and standardizing definitions
Where is the wisdom we have lost in knowledge?
Where is the knowledge we have lost in information?
~ Choruses from the Rock, T.S. Eliot