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The Problem with Big Data: Operating on Smaller Datasets to Bridge the Implementation Gap
Richard P Mann1, Faisal Mushtaq2, Alan D White3
1School of Mathematics, University of Leeds , Leeds , UK.
Frontiers in Public Health
|December 20, 2016
Summary
Leveraging existing small datasets can create predictive models for public health, demonstrating tangible benefits. This approach bridges the gap between big data
Area of Science:
- Public Health Informatics
- Health Services Research
- Data Science in Healthcare
Background:
- Big data initiatives in public health face a public perception gap despite scientific optimism.
- A disconnect exists between the potential of big data and public understanding of its benefits.
Purpose of the Study:
- To demonstrate the immediate value of small data approaches in public health.
- To illustrate how existing data can yield practical healthcare improvements.
- To propose a balanced strategy for data utilization in public health.
Main Methods:
- Developed a proof-of-concept predictive model using existing smaller datasets.
- Focused on predicting hospital length of stay as a tangible healthcare metric.
- Emphasized the utilization of current information resources.
Main Results:
- The small data model generated reasonable predictions for hospital length of stay.
- Demonstrated that existing small datasets are sufficient for creating valuable predictive models.
- Highlighted the feasibility of using smaller datasets for immediate healthcare applications.
Conclusions:
- Small data approaches can yield immediate, tangible benefits in public health.
- Integrating small data strategies alongside big data initiatives is crucial.
- Increased attention and funding for utilizing existing data resources are recommended.
