- Constructing research datasets
Description
Creating empirical databases involves systematically gathering, organizing, and maintaining accurate, verifiable data to support analysis, decision-making, and problem-solving. This strategy enables identification of trends, assessment of interventions, and informed policy development. By centralizing and standardizing diverse data sources, empirical databases provide reliable evidence for addressing specific issues, tracking progress, and enhancing transparency and accountability in response to global challenges.
Claim
Creating empirical databases is absolutely essential in today’s data-driven world. Relying on anecdotes or theory alone is reckless; only systematic data collection empowers accurate analysis and credible decision-making. Organizations that neglect empirical databases risk misguided strategies and missed insights. Comprehensive, well-structured databases are not optional—they are the bedrock for scientific progress, innovation, and effective policy. Ignoring this strategy is a recipe for stagnation and irrelevance.
Counter-claim
Creating empirical databases is simply not an important strategy. In today’s world, we are already drowning in data, and compiling even more databases adds little actual value. Time spent building empirical datasets could be better used on analysis and actionable insights. The constant push for more databases only bogs down innovation and distracts from real problem-solving. Frankly, prioritizing database creation is a misguided effort in the age of information overload.
Broader
UIA organization
SDG
Metadata
Database
Global strategies
Type
(D) Detailed strategies
Subject
- Information » Data, codes
- Research, standards » Research
Content quality
Yet to rate
Language
English
1A4N
W8745
DOCID
13387450
D7NID
224756
Editing link
Official link
Last update
Dec 3, 2024