Measuring What Matters : Principles for a Balanced Data Suite That Prioritizes Problem-Solving and Learning
Responding effectively and with professional integrity to the many challenges of public administration requires recognizing that access to more and better quantitative data is necessary but insufficient. Overreliance on quantitative data comes with its own risks, of which public sector managers should be keenly aware. This paper focuses on four such risks. The first is that attaining easy-to-measure targets becomes a false standard of broader success. The second is that measurement becomes conflated with what management is and does. The third is that measurement inhibits a deeper understanding of the key policy problems and their constituent parts. The fourth is that political pressure to manipulate key indicators can lead, if undetected, to falsification and unwarranted claims or, if exposed, to jeopardizing the perceived integrity of many related (and otherwise worthy) measurement efforts. Left unattended, the cumulative concern is that these risks will inhibit rather than promote the core problem-solving and implementation capabilities of public sector organizations, an issue of high importance everywhere but especially in developing countries. The paper offers four cross-cutting principles for building an approach to the use of quantitative data—a “balanced data suite”—that strengthens problem-solving and learning in public administration: (1) identify and manage the organizational capacity and power relations that shape data management; (2) focus quantitative measures of success on those aspects which are close to the problem; (3) embrace a role for qualitative data, especially for those aspects that require in-depth, context-specific knowledge; and (4) protect space for judgment, discretion, and deliberation in those (many) decision-making domains that inherently cannot be quantified.
| Main Authors: | , |
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| Format: | Working Paper biblioteca |
| Language: | English |
| Published: |
World Bank, Washington, DC
2022-05
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| Subjects: | PUBLIC ADMINISTRATION, PUBLIC SECTOR MANAGEMENT, PUBLIC SECTOR, QUANTITIVE DATA ANALYSIS, BALANCED DATA SUITE, MIXED METHODS, DATA CURATION, ORGANIZATIONAL LEARNING, PROBLEM SOLVING, |
| Online Access: | http://documents.worldbank.org/curated/en/099726505172234979/IDU001f1e0160432e04a9308b43090ad984a77ba http://hdl.handle.net/10986/37450 |
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| Summary: | Responding effectively and with
professional integrity to the many challenges of public
administration requires recognizing that access to more and
better quantitative data is necessary but insufficient.
Overreliance on quantitative data comes with its own risks,
of which public sector managers should be keenly aware. This
paper focuses on four such risks. The first is that
attaining easy-to-measure targets becomes a false standard
of broader success. The second is that measurement becomes
conflated with what management is and does. The third is
that measurement inhibits a deeper understanding of the key
policy problems and their constituent parts. The fourth is
that political pressure to manipulate key indicators can
lead, if undetected, to falsification and unwarranted claims
or, if exposed, to jeopardizing the perceived integrity of
many related (and otherwise worthy) measurement efforts.
Left unattended, the cumulative concern is that these risks
will inhibit rather than promote the core problem-solving
and implementation capabilities of public sector
organizations, an issue of high importance everywhere but
especially in developing countries. The paper offers four
cross-cutting principles for building an approach to the use
of quantitative data—a “balanced data suite”—that
strengthens problem-solving and learning in public
administration: (1) identify and manage the organizational
capacity and power relations that shape data management; (2)
focus quantitative measures of success on those aspects
which are close to the problem; (3) embrace a role for
qualitative data, especially for those aspects that require
in-depth, context-specific knowledge; and (4) protect space
for judgment, discretion, and deliberation in those (many)
decision-making domains that inherently cannot be quantified. |
|---|